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Evaluating Smart Mobility and Land Use Development in India

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Title:
Evaluating Smart Mobility and Land Use Development in India A Case Study on the City of Indore, India
Creator:
Nagal, Yash
Place of Publication:
[Gainesville, Fla.]
Florida
Publisher:
University of Florida
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english
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1 online resource (113 p.)

Thesis/Dissertation Information

Degree:
Master's ( M.U.R.P)
Degree Grantor:
University of Florida
Degree Disciplines:
Urban and Regional Planning
Committee Chair:
STEINER,RUTH LORRAINE
Committee Co-Chair:
ALAKSHENDRA,ABHINAV
Committee Members:
ZWICK,PAUL D

Subjects

Subjects / Keywords:
gis -- india -- indore -- suitability -- walkability
Urban and Regional Planning -- Dissertations, Academic -- UF
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bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Urban and Regional Planning thesis, M.U.R.P

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Abstract:
It is well recognized that the urban population in the world is growing rapidly and so are the issues in urban areas due to sprawl. Sustainable strategies are required alleviate these problems and sustain the rapidly growing economy. In modern times, smart planning, where city planning is done by integrating information and communication technologies and by largely using big data has emerged as one such strategies. This study focuses on evaluating smart transportation and land use in India and how they change the quality of life in cities both financially and environmentally. The research looks at conducting a case study on Indore, India and evaluating their current smart city program which is called Smart City Indore. India is estimated to have an urban population of 40% by 2030 and 50% by 2050 which calls for evaluation of smart city programs considering the large potential requirement of them in the future. Due to demand for these programs, it is important that these programs work efficiently and make full use of the funds allocated, especially in the case of India where funds always fall short (Jawaid & Khan, 2015). By looking at best practices, recommendations would be made to the current program. With the amount of money being spent on smart city projects in India and growing need for new developments, evaluation of programs becomes more important. After analysis, the research would make recommendations for improvement in the ongoing program with respect to infrastructural changes for development and land use allocations by presenting ideas for mixed use and walk-able options. ( en )
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In the series University of Florida Digital Collections.
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Includes vita.
Bibliography:
Includes bibliographical references.
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Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (M.U.R.P)--University of Florida, 2017.
Local:
Adviser: STEINER,RUTH LORRAINE.
Local:
Co-adviser: ALAKSHENDRA,ABHINAV.
Statement of Responsibility:
by Yash Nagal.

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UFRGP
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The research paper follows citations in American Psychological Associatio n (APA) style.



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EVALUATING SMART MOBILITY AND LAND USE DEVELOPMENT IN INDIA: A CASE STU DY ON THE CITY OF INDORE, INDIA By YASH NAGAL A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE RE QUIREMENTS FOR THE DEGREE OF MASTER OF URBAN AND REGIONAL PLANNING

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2017 Yash Nagal

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To my loving family, friends, faculty members at University of Florida and everyone figh ting for the love of learning, y o ur ef forts will never be overlooked

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4 ACKNOWLEDGMENTS I thank my parents Mr. Rajesh Nagal and Mrs. Saroj Nagal for always believing in me no matter what happens and teaching me that there is no power bigger than knowledge and wisdom. I am grateful for m y sister Yashika Nagal for her unwavering support and teaching me it is never too late to learn. Apart from this I am thankful for the faculty members at University of Florida, especially, my chair Dr. Ruth Steiner, co chair Dr. Abhinav Alakshendra and Dr. Paul Zwick for guiding me throughout this research. I would also like to thank the members of the Smart City Indore project for helping me through the data collection and collaboration process. Finally, I am grateful for all friends who have supported me throughout this journey and for their support in the future, especially Alyssa Henriquez for helping me with edits for the final document. None of this would have been possible without you.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 2 LITERATURE REVIEW ................................ ................................ .......................... 18 2.1 Smart Automobiles ................................ ................................ ............................ 18 2.1.1 Vehicle Techno logies ................................ ................................ .............. 18 2.1.2 Electrical Vehicles ................................ ................................ ................... 20 2.1.3 Connected Cars/Internet of Things (IoT) ................................ ................. 21 2.2 Smart Fuels and Better Emission Standards ................................ .................... 22 2.2.1 Alternative Fuels ................................ ................................ ...................... 22 2.2.2 Emission Standards ................................ ................................ ................. 22 2.3 Smart Physical Infrastructure ................................ ................................ ............ 23 2.4 Intelligent Transport Systems ................................ ................................ ........... 24 2.5 Case Study City ................................ ................................ ................................ 27 2.5.1 Urban Transport Scenario ................................ ................................ ....... 28 2.5.2 City Vision and Goals ................................ ................................ .............. 29 2.6 Walkability and Bicycling as an Economic Alternative ................................ ...... 30 2.7 Land Use Conflict Identification Strategy ................................ .......................... 43 3 STUDY AREA ................................ ................................ ................................ ......... 45 3.1 Selection of Study Area ................................ ................................ .................... 47 3.2 City Profile ................................ ................................ ................................ ........ 48 4 METHODOLOGY ................................ ................................ ................................ ... 50 5 RESULTS and DISCUSSION ................................ ................................ ................. 58 5.1 Goal 1 Identify Suitable Land for Commercial Land Use ................................ .. 61 5.1.1 Identify Physically Suitable Lands ................................ ........................... 61 5.1.2 Identify Proximally Suitable Land for Commercial Land Use ................... 61 5.1.2.1 Proximity to major roads ................................ ................................ 61 5.1.2.2 Proximity to commercial development ................................ ............ 62 5.1.2.3 Proximity to bus stands ................................ ................................ .. 63

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6 5.1.2.4 Proximity to public and semi public development (Institutional) ..... 64 5.1.2.5 Proximity to the airport ................................ ................................ ... 65 5.1.2.5 Proximity to the rail lines ................................ ................................ 66 5.2 Identify Suitable Land for Public and Semipublic Land Use .............................. 69 5.2.1 Identify Physically Suitable Land for Public and Semipublic Land Use ... 69 5.2.2 Identify Proximally Suitable Land for PSP Land Use ............................... 70 5.2.2.1 Proximity to major roads ................................ ................................ 70 5.2.2.2 Proximity to bus stands ................................ ................................ .. 71 5.2.2.3 Proximity to PS P ................................ ................................ ............ 72 5.2.2.4 Proximity to commercial development ................................ ............ 73 5.2.2.5 Proximity to public utility and facilities (PUF) ................................ .. 74 5.2.2.6 Proximity to the airports ................................ ................................ 75 5.2.2.7 Proximity to rail lines ................................ ................................ ...... 76 5.3 Identify Suitable Lands for Residential Development ................................ ........ 79 5.3.1 Identify Land Physically Suitable for Residential Development ............... 79 5.3.2 Identif y Proximally Suitable Lands for Residential Development ............. 80 5.3.2.1 Proximity to PSP development ................................ ....................... 80 5.3.2.2 Proximity to major road s ................................ ................................ 81 5.3.2.3 Proximity to bus stands ................................ ................................ .. 82 5.3.2.4 Proximity to commercial establishments ................................ ........ 83 5.3.2.5 Proximity to PUF establishments ................................ ................... 84 5.3.2.6 Proximity to residential establishments ................................ .......... 85 5.3.2.7 Proxi mity to the airport ................................ ................................ ... 86 5.3.2.8 Proximity to rail lines ................................ ................................ ...... 87 5.4 Conflict Surface ................................ ................................ ................................ 90 5.5 Applications of Walkability in Planning ................................ .............................. 95 5.5 Budget Restructuring ................................ ................................ ...................... 102 6 LIMITATIONS and CONCLUSION ................................ ................................ ....... 107 6.1 Limitations ................................ ................................ ................................ ....... 107 6.2 Conclusion ................................ ................................ ................................ ...... 108 LIST OF REFERENCES ................................ ................................ ............................. 110 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 113

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7 LIST OF TABLES Table page 2 1 Aff ordability in Modal Share ................................ ................................ ................ 31 2 2 Walking Linked to Other Modes of Transport. ................................ .................... 31 2 3 Health Benefits of Active Transportation ................................ ............................ 39 2 4 Expenditure on Goods by Mode of Travel ................................ .......................... 41 2 5 Indicators of Transportation Equity. ................................ ................................ .... 42 4 1 LUCIS Urban Mix ed Use Opportunity Matrix ................................ ...................... 56 5 1 Goals, Objectives & Sub Objectives for Commercial Development .................... 58 5 2 Goals, Objectives & Sub Obje ctives for Institutional Development ..................... 59 5 3 Goals, Objectives & Sub Objectives for Residential Development ..................... 6 0 5 4 Area Available f or Different Land Uses ................................ ............................... 94 5 5 Unite d States Roadway Expenditures ................................ ................................ 95 5 6 AHP for Commercial Development ................................ ................................ ... 104 5 7 AHP for Residential Development ................................ ................................ .... 105 5 8 AHP for Commercial Development ................................ ................................ ... 106

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8 LIST OF FIGURES Figure page 1 1 Population Share in India ................................ ................................ ................... 14 2 1 Major Fields in Transportation ................................ ................................ ........... 18 2 2 Types of Engines in Use for Light Vehicles ................................ ........................ 20 2 3 Traditional v/s Cars with Network Solutions ................................ ........................ 21 2 4 External Costs of Au tomobile and Pedestrian Trave l ................................ ......... 35 3 1 Indore, India City Base Map. ................................ ................................ ............. 45 3 2 Transportation Network in Indore ................................ ................................ ....... 46 3 3 Area Based Develop ment Proposal for Indore City ................................ ............ 47 3 4 Pilot Area for Development in Smart City Indore Plan ................................ ........ 48 4 1 Work Flowchart Model ................................ ................................ ........................ 50 4 2 Hierarchy of Goals, Objectives and Sub Objectives in LUCIS ............................ 51 4 3 Ass igned Values in Reclassify Tool ................................ ................................ .... 52 4 5 ArcGIS Tool Rescale by Function ................................ ................................ ....... 53 4 6 Suitability Values Versus Input Values in Rescale by Function .......................... 54 4 7 Hierarchy of Steps in Suitability Analysis. Source: Overview of Suitability ......... 54 4 8 Analysis model for the urban s ub po ................................ ................................ ................................ ............. 55 4 9 Analysis Model for the Urban Sub ................................ ................................ ................................ ........... 55 5 1 Suitable Land Values for Commercial Development in the City .......................... 61 5 2 Suitable Land Proximal to Major Roads in the City ................................ ............. 62 5 3 Suitable Land Proximal to Commercial Development ................................ ......... 63 5 4 Suitable Land Proximal to Bus Stands ................................ ............................... 64 5 5 General Steps in Suitability Analysis for PSP ................................ ..................... 64

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9 5 6 Suitable Land Proximal to PSP ................................ ................................ ........... 65 5 7 General Steps in Suitability Analysis for Airport ................................ .................. 65 5 8 Suitable Lands Proximal to the Airport ................................ ............................... 66 5 9 General Steps in Suitability Analysis for Airport ................................ .................. 66 5 10 Suitable Land Proximal to Rail Lines ................................ ................................ .. 67 5 11 Proximally Suitable Land for Commercial Development ................................ ..... 68 5 12 Physically and Proximally Suitable Land for Commercial Development ............. 68 5 13 Suitable Land for Commercial Land Use ................................ ............................ 69 5 14 Suitable Land Values for PSP Development in the City ................................ ..... 70 5 15 Suitable Land proximal to Major Roads in the City ................................ ............. 71 5 16 Suitabl e Land proximal to Bus Stands ................................ ................................ 72 5 17 Suitable Land Proximal to PSP establishments ................................ .................. 73 5 18 General Method for Commercial Developme nt ................................ ................... 73 5 19 Suitable Land Proximal to Commercial Development ................................ ......... 74 5 20 General Method for PUF ................................ ................................ .................... 74 5 21 Suitable Lands Proximal to PUF ................................ ................................ ......... 75 5 22 General Method for Airports ................................ ................................ ............... 75 5 23 Suitable Land Proximal to the Airport ................................ ................................ 76 5 24 General Method for Rail Lines ................................ ................................ ............ 76 5 25 Suitable Land Proximal to Rail Lines ................................ ................................ .. 77 5 26 Proximally Suitable Land for PSP Development ................................ ................. 78 5 27 Physically and Proximally Suitable Land for PSP Development ......................... 78 5 28 Suitable Land for PSP Land Use ................................ ................................ ........ 79 5 29 Suitable Land Values for Residential Development in the City ........................... 80 5 30 General Method for PSP ................................ ................................ .................... 80

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10 5 31 Suitable Land proximal to PSP Development ................................ ..................... 81 5 32 General Method for Roads ................................ ................................ ................. 81 5 33 Suitable Lands Proximal to Major Roads ................................ ............................ 82 5 34 General Method for Bus Stands ................................ ................................ ......... 82 5 35 Suitable Land Proximal to the Bus Stands ................................ ......................... 83 5 36 General Method for Commercial Development ................................ ................... 83 5 37 Suitable Land Proxima l to Commercial Establishments ................................ ..... 84 5 38 General Method for PUF establishments ................................ ............................ 84 5 39 Suitable Land Proximal to PUF Establishments ................................ ................. 85 5 40 General Method for Residential Establishments ................................ ................. 85 5 41 Suitable Land Proximal to Residential Establishments ................................ ....... 86 5 42 General Method for Airport Suitability ................................ ................................ 86 5 43 Suitable Land Proximal to the Airport ................................ ................................ 87 5 44 General Method for Rail Line Suitability ................................ ............................. 87 5 45 Suitable Land Proximal to Rail Lines ................................ ................................ .. 88 5 46 Proximally Suitab le Land for Residential Development ................................ ...... 89 5 47 Physically and Proximally Suitable Land for Residential Development .............. 89 5 48 Suitable L and for Residential Land Use ................................ ............................. 90 5 49 Reclassified Commercial Suitability Surface ................................ ...................... 91 5 50 Reclassified Institutional Suitability Su rface ................................ ....................... 91 5 51 Reclassified Residential Suitability Surface ................................ ........................ 92 5 52 Combining the Above Three Surfaces gives the Final Conflict Sur face. ............. 92 5 53 Final Conflict Surface for High Density Mixed Land Use ................................ .... 93 5 54 Trip Mode Shares in Indian Cities ................................ ................................ ....... 97 5 55 Variation in Wa lkability Ratings in Six cities ................................ ....................... 98

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11 5 56 Time Spent on Travel Mode in Ind ore City ................................ ......................... 98 5 57 Average Time from Residence to Destination in Indore City .............................. 99 5 58 Preferred Improvement in Facilities in Indo re City ................................ .............. 99 5 59 Increase in Average Weekly Commute Time ................................ ................... 100 5 60 Change in Transit Plan ning Approach. ................................ ............................. 101 5 61 Source of Fund ing and Debt Repayment Structure for Smart City Plan I ndore. ................................ ................................ ................................ .............. 102

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12 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Urban and Regional Planning EVALUATING SMART MOBILITY AND LAND USE DEVELOPMENT IN INDIA: A CASE STU DY ON THE CITY OF INDORE, INDIA By Yash Nagal December 2017 Chair : Ruth L. Steiner Co C hair: Abhinav Alakshendra Major: Urban and Regional Planning It is wel l recognized that the urban population in the world is growing rapidly and so are the issues in urban areas due to sprawl. Sustainable strategies are required alleviate these problems and sustain the rapidly growing economy. In modern times, smart planning where city planning is done by integrating information and communication technologies and by largely using big data has emerged as one such strategies. This study focuses on evaluating smart transportation and land use in India and how they change the qu ality of life in cities both financially and environmentally The research looks at conducting a case study on Indore, India and evaluating their current smart city program whi ch is called Smart City Indore. India is estimated to have an urban population o f 40% by 2030 and 50% by 2050 which calls for evaluation of smart city programs considering the large potential requirement of them in the future. Due to demand for these programs, it is important that these programs work efficiently and make full use of t he funds allocated, especially in the case of India where funds always fall short (Jawaid & Khan, 2015). By looking at best practices, recommendations would be made to the current program. With the amount of money being spent on smart

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13 city projects in Indi a and growing need for new developments, evaluation of p rograms becomes more important After analysis, the research would make recommendations for improvement in the ongoing program with respect to infrastructural changes for development and land use allo cations by presenting ideas for mixed use and walkability centric options.

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14 CHAPTER 1 INTRODUCTION As estimated by the Indian Government, the GDP is largely contributed by the automotive sector. By the year 2026, its share is expected to be 12% which would make it one of the biggest employment sectors. In the past decade alone, 25 million jobs have been created in the automotive sector (Mehra & Verma, 2016). With increasing Gross Domestic Product per capita, there has been a constant increase in urbani zation. Per World Bank data from 2014, 32% of the population is in urban areas and this number is expected to increase to 40% by 2030. Figure 1 1. Population Share in India Due to rising incomes and low car penetrations in India, the potential for grow th in the auto and auto component production is high. These factors have boosted the automobile manufacturing industry in India. Society of Indian Automobile Manufacturing estimates that the total production of vehicles from 2013 2014 to 2014 2015 was incr eased by 8.6% (Mehra & Verma, 2016). With a road network of 2.9 million miles

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15 India has one of the largest road networks. The challenges in this area include road congestion, low usage of technology, inadequate means of public transportation, high air poll ution due to vehicles running on combustible fuels and infrastructural flaws. Solutions to these problems are essential as around 65% of freight and 80% of passengers traffic is passed by the roads (Mehra & Verma, 2016). It is well recognized that the urba n population in the world is growing rapidly and so are the issues in urban areas due to sprawl. Sustainable strategies are required alleviate these problems and sustain the rapidly growing economy. In modern times, smart planning, where city planning is d one by integrating information and communication technologies and by largely using big data has emerged as one such strategies (Durand et al., 2011). This study focuses on evaluating smart transportation in developing nations and how they change the qualit y of life in cities both financially and environmentally. Smart transportation includes an infrastructure that supports walkability and higher alternatives for public transport that are affordable for people of all economic levels (Ju et al., 2013). The re search looks at conducting a case study on Indore, India and evaluating their current smart city program which is called Smart City Indore. Analysis of smart city programs are necessary now more than ever because cities are expanding with new cities being formed by transforming villages and towns. With villages and towns lacking basic infrastructure in some places, it is essential that the infrastructural capacity of these places is evaluated. India is estimated to have an urban population of 40% by 2030 an d 50% by 2050 which calls for evaluation of smart city programs considering the large potential requirement of them in the future (Smart

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16 City Plan Indore, 2015). Due to demand for these programs, it is important that these programs work efficiently and mak e full use of the funds allocated, especially in the case of India where funds always fall short (Jawaid & Khan, 2015). Current transportation infrastructure and transportation future growth plans would be analyzed along with the current and all future lan d use allocations. For comparative analysis, the research would use other cities which are socio economically comparable to Indore. By looking at best practices, recommendations would be made to the current program. With the amount of money being spent on smart city projects in India and growing need for new developments, evaluation of programs becomes more important. With growing use of technology, it is important to incorporate technology in development strategies and techniques for problem solving. By do ing this research I hope to enhance the current program in its transportation initiatives and future transportation development projects. The goals and visions of the Smart City Program are meant be an effective policy instrument for smart growth and condu it for change. To access smart growth in communities, evaluation is essential. Calculating visions and goals include checking the clarity of statements and funding strategies that are being implemented. Apart from this, it is important to see whether the p lans are keeping pace with the rhetoric. Research on smart growth has been going on for decades in the world and there sure are debates over its merits. Critics of smart growth claim that this process is costly and in some cases, leads to lack of personal choice and excessive regulations. Due to increased street connectivity in some areas an increase in crime rates was noticed. Evidence suggests that the term smart growth is used as a form of political

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17 cover in some policies for programs that have very litt le to do with the key concerns of smart growth (Mehra & Verma, 2016) After analysis, the research would make recommendations for improvement in the ongoing program with respect to infrastructural changes for development and land use allocations by present ing ideas for mixed use and walkability centric options.

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18 CHAPTER 2 LITERATURE REVIEW For the five year period of 2016 to 2021, the Indian Government has approved 599 highway projects with an expenditure of US $ 16.2 billion and covering approximately 8,064 miles (Mehra & Verma, 2016). For the development of first 20 smart cities, the government has designated US $ 7.6 billion for the project Within the smart city scheme, resourceful urban mobility and public transit comprise a large part. The plan inc ludes Bus Rapid Transport, Mass Rapid Transport, National Highways, pedestrian skywalks, walkways, cycle tracks and expressways and like all projects these come with challenges too (Smart City Plan, 2015). The table below displays some of the major challen ges. A shift in era towards smart transportation in India can be made by making advances in four major fields: Figure 2 1 Major Fields in Transportation Source: Smart Transportation transforming Indian cities 2.1 Smart Automobiles 2.1.1 Vehicle Tec hnologies With emphasis on refined engines, safe design, sustainable/ green fuels, better emission norms, connected cars, driverless vehicles and fuel efficiency technological

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19 advancements are helping the shift towards smart transportation one step at a ti me. As driver error is known to be one of the major causes for crash, automakers created a advanced technologies like driver assist systems. Some of these advanced technologies include Anti lock braking system and electronic stability p rogram Per a study ESP could result in saving approximately 10000 lives per year in India. This study states that 70 percent of accidents linking to vehicle skidding could be avoided by using ESP in automobiles. Currently, ESP is only enabled in 4 5 percent of new cars as compared to ABS which is fitted in almost 40% of new cars in India (Mehra & Verma, 2016). The manufactures need to increase this number drastically to improve safety. Automated manual t ransmission By using this technology, disengaging and engaging of the clutch is helped by the electronic transmission unit. Crash t esting As of October 2017, the Indian Government has made crash testing mandatory for all new cars (Mehra & Verma, 2016). Use of high strength s teel. This material is light weight and meets the While these technologies are being applied in India, technological advancements like vehicle to vehicle communication, driverless cars and pre collision techno logies are being used worldwide indicating great room for improvement in developing countries in India. This also gives developing countries a great amount of data to follow and research as best practices.

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20 2.1.2 Electrical Vehicles Figure 2 2 Types of Engines in Use for Lig ht Vehicles Per to a study by Center of Automotive Research, University of Duisburg Essen, Germany, by 2030, only 56% of vehicles worldwide would use combustion engines, 35% would use hybrid technologies and 9% electric power which would reduce Green House Gas emissions substantially. Over the next 15 20% the ideal goal is to make a modal shift towards 100% battery driven electrical vehicles (Mehra & Verma, 2016). Meanwhile in India, vehicles running on duel fuel which petrol/diesel and CNG are gaining popu larity. Brands like Toyota, BMW and Mahindra have a huge presence in the Indian market. Per the National Electrical Mobility Mission Plan 2020, huge incentives would be made for the adoption of green vehicles and domestic manufacturing. It has been estimat ed that as of 2017, 5% of the total cars in the Indian car market would be electric (Mehra & Verma, 2016). This would mean 175,000 electric cars in 2017 and this number is expected to reach20 million by 2020 (Mehra & Verma, 2016). With this being said, the slow pace of infrastructural development in India has to be considered as compared to the developed countries.

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21 2.1.3 Connected Cars/Internet of T hings (IoT) Figure 2 3 Traditional v/s Cars with Network Solutions This technology uses the internet of t hings to create a communication network between the different electronic systems within the car as well as outside the car. The goal for the future is to create a communication platform between mobile or wearable devices and the electronic systems of the c ar through internet. Th e name for this concept is V2X. While all these new global technologies are on the rise, there are still concerns amongst the consumers about security implications. Per Veracode, an application security firm, half of the drivers are concerned about driver aid applications like cruise control, self parking, and collision avoidance systems. Apart from this cybersecurity is a huge concern for automobile manufactures. In 2015, United States came across a flaw in Jeeps equipped with Uconne ct software which enabled hackers to take control of the vehicle (Mehra & Verma, 2016). Initiatives to overcome cybersecurity concerns has a major impact on the paradigm shift regarding technological advancements.

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22 2.2 Smart Fuels and Better Emission S tanda rds 2.2.1 Alternative F uels In recent years, greenhouse gas emissions have been on a rise leading to the expansion of carbon footprint. A major reason for this has been rapid urbanization which presents a strong need for sustainable and clean technology. P er Environmental Protection and Conservation Authority reports, the Supreme Court banning the sale of diesel engines of 2000cc and more in Delhi NCR resulted in a 19 20% fall in population levels. Unfortunately, this also resulted in job losses for manufac turers and dealers due to unsold inventories. Some well known solutions to air pollution are use of electric and solar powered engines or use of bio fuels, compressed natural gas or ethanol. A lot of research has been done on biodiesel blending by Indian O il, a state owned company with Indian Railways, Haryana Roadways, and TATA group and their initial studies have proposed that the smoke density was reduced by use of biodiesel blends (Mehra & Verma, 2016). Biodiesel blends have the capacity to be used in a ll modes of transport including roads/BRTs, MRTs, railways, freight, waterborne transport and aviation. 2.2.2 Emission S tandards Based on European standards of emission, the Indian Government regulates the emission standards and follows Bharat Stage emissi on standards. 13 metro cities in April 2010 moved to Bharat Stage IV and the other cities went to Bharat Stage III and as of October 2014, 20 more cities are now on Bharat Stage IV (Mehra & Verma, 2016). The Government of India is adopting Bharat Stage VI directly after IV to stay updated amongst the high emission laws (Mehra & Verma, 2016). Besides new technologies for vehicles and higher emission standards, India still requires to maintain the current vehicles and better road and traffic standards.

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23 2.3 Sm art Physical I nfrastructure The basis for smart infrastructure development was set ten years ago, with the Jawaharlal Nehru National Urban Renewal Mission in India. With an impressive sea port network, second largest road network, four largest rail networ ks and ninth largest civil aviation market in the world, India is fast growing transportation economy (Mehra & Verma, 2016). In 2015, Prime Minister Narendra Modi launched Atal Mission for Rejuvenation for Urban Transformation which is an incentive based a pproach for states meeting targets for funding. The target proposed by this scheme is to make ministry of new jobs (Mehra & Verma, 2016). During these projects the total l ength of national highways is proposed to increase from 60,000 miles to 93,750 miles (Mehra & Verma, 2016). In 2012, India set up the High Speed Rail Corporation considering the huge success of high speed rail in countries like Japan, China, France and oth er European Countries. Currently, feasibility studies are going on for connecting Delhi, Mumbai, Chennai and Kolkata and research has been pushed for the development of a segment between Ahmedabad and Mumbai to run bullet trains, a common term for high spe ed trains (Mehra & Verma, 2016). With decarbonized solutions for high speed trains in the future, United Nations Environment Program claims high speed rail to be a cleaner option compared to others bringing down carbon emissions. The same study also says t hat high speed rail has lower Green House Gas emissions than road and air transp ort for per passenger per mile. With increasing global warming concerns, initiatives like these could prove as a major step forward towards sustainable development.

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24 Further, t he Ministry is also putting a lot of focus on promoting coastal shipping as it reduces the cost by 25 30% and reduces carbon emissions (Mehra & Verma, 2016). Currently, work is being done to bring up 12 big ports with 3 small ports in West Bengal, Maharash tra and Ta mil Nadu (Mehra & Verma, 2016). 2.4 Intelligent Transport S ystems Various Information and Communication Technology interventions combined in an effective manner constitute Intelligent Transportation Systems. Some of such systems are defined belo w. Passenger information systems These systems include providing expected time of arrivals on electric sign boards for bus stands, MRT, Railway stations or Airports while displaying other real time information. Some of these systems also provide informati on on mobile handsets to ease congestion at waiti ng areas. Real time parking management/ multi level parking. Real time systems provide information for available parking spots on users mobile phones to reduce congestion on roads and multilevel parking mini mize the land use while providing easy entry and exit options. Safety devices and multi sensors can be added at a low maintenance and operating cost. Smart c ards This technology allows people to access all forms of public transport with one single card. S uch technologies are already under consideration by New Del hi and other State Governments. Electronic toll collection This technology uses Radio Frequency Identification to read information from a distance and then deduct toll at each entry. By using this the fuel efficiency and traffic management can be enhanced by avoiding the stop start at toll gates ther eby also decreasing congestion.

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25 Smart parking assist By using censors and cameras in chassis and the driveline, parking can be assisted which also inc reases efficiency, safety and comfort for inex perienced drivers and learners. Automated speed e nforcement Considering the congestion in India this technology can effectively reduce the heavy traffic. This technology has already been effectively implemente d in other countries. Airport s urveillance and safety e quipment These initiatives include installation of CISF airport security, CCTV surveillance, high tech X ray baggage inspection facilities and other surveillance systems. R adio frequency identificatio n in freight t ransportation This technology has been growing slowly in India and Indian logistics sector still lags global standards while ranking 46 th among 155 countries by International Journal of Multidisciplinary Research and Development As time goe s on, the Carbon dioxide regulations would continue to get firmer which would create a lot of pressure on manufacturing industries. Promoting emission reducing vehicles in the market result in higher costs of manufacturing. This would also mean high costs for vehicles which would mean bigger profits for the manufacturers in the long run. There would be a push towards investment in e mobility and alternate powerful technologies to stay up to date with the increasing emission standards. Apart from this, many policies are in place for now and the future to ensure good growth of smart transportation in India. Below is a list of policy interventions: National Urban Transport Policy, 2014 Atal Mission for Rejuvenation and Urban Transformation (AMRUT) Automotive Mi ssion Plan 2016 26 (AMP 2026 ) Smart Cities Mission

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26 Make in Indi a Natural Manufacturing Policy Skill Development Fame India Scheme Voluntary Vehicle Modernization/End of Life Policy With a growing economy of 7.4% in comparison to shrinking western economie s, India is in a great spot for impressive growth and is a great destination for investment worldwide (Mehra & Verma, 2016). Apart from this, it has a young human capital base of 550 million with strong policy intervention (Mehra & Verma, 2016). A total of US$27 billion has been provided as budget for upcoming projects by the central government and by 2017, it is expected to provide another US$47 billion for road related projects Cons idering these huge investments, India is on the verge of a giant transfor mation in the field of transportation (Mehra & Verma, 2016). Over the next decade, India plans to bring down carbon emissions while creating a larger job market using sustainable resources. The goal is to bring more and better investments while creating a world class transportation infrastructure which would include green fuels and higher qualities of life both economically and environmentally. In line with the rising urban population and transportation needs, India still lacks investment. Per India Transpo rt Report Moving India to 2032, the number of total passengers has been estimated to reach 168,875 BPKPM in 2031 from 10,375 in 2011 which would be a large increase of 15% along with a 9% rail traffic growth and 15.4% road traffic growth per year. This re port also estimates that to support a proper infrastructure for this kind of growth there would be requirement of US$ 570 billion funding by the year 2031. Considering the current scenario, most of these investments should go towards improvements towards t he transportation infrastructure. The

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27 importance of smart transportation is no question in developed western countries, majorly in Europe. From use of electric cars in private transports to rapid metro rail, these countries have fast adopted these sustaina ble and easy transit technologies for growth. Improved vehicular movement is a huge part improving transportation infrastructure. Following the footsteps of European countries, India has been moving towards technology slowly but surely and while there is a huge difference of policy implementation between developed and developing countries, India has initiated several such programs. A prime example of this the upcoming metro rail network for capital cities with plans for electric cars on the way by 2030. In 2015 2016 Union Budget, US$119.24 million was allocated towards vehicles with hybrid technology. Hybrid vehicles have received a great response and expect more funding in the future. With smarter policies and private/public partnerships these goals can b e achi eved faster and more awareness can be created for desired change. 2.5 Case Study City In the past three years, Indore has made several efforts towards improving transportation conditions. The city has increased its daily ridership from 41,214 to 87,8 77 and bus fleet to 115 from 85 while introducing Bus Rapid Transit lanes of 7.15 miles (Smart City Plan Indore, 2016). Using ICT and GPS based AVL tracking, 50 bus stops have passenger information systems (Smart City Plan Indore, 2016). Pedestrian footpa ths were increased by 13% and 19 miles of non motor vehicle zone lanes were introduced (Smart City Plan Indore, 2016). Apart from this, the major road network of the city was increased from 43 miles to 61 miles (Smart City Plan Indore, 2016).

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28 Under Safe C ity Initiative, street lighting coverage of roads has improved to 84% from a pervious of 66% and 165 cameras have been installed for surveillance on 15 intersections (Smart City Plan Indore, 2016). Per the Smart City Mission Report for Indore, the city fue ls the economic engine of the state for Automobile Industries. Along with good connectivity by Air, Rail and Road to all major cities, it also has connectivity to three National Highways NH 3, NH 59 and NH 598A and two state highways SH 27 and SH 34. Even with these new advancements, the city still lacks spatial planning. Per the same report, industrial and residential areas have been developed without proper infrastructure and lack open public spaces, education, healthcare and adequate road networks. The i nformal sector is present in both residential and commercial fields with one out of three people being slum dwellers. 2.5.1 Urban Transport Scenario In the past three years, there has been a 60% increase in traffic congestion and air quality has deteriora ted due to more than 10% growth in privately owned vehicles (Smart City Plan Indore, 2016). Furthermore, increased road traffic and safety issues have been raised by poor pedestrian and road facilities. Excluding AICTSL bus services, public transport has b een in poor condition with only 0.045 buses per 1000 people while the benchmark is 0.4 0.6 buses per 1000 people (Smart City Plan Indore, 2016). Apart from this, organized road network is exhibited only in 95.3 miles out of possible 222.2 miles (Smart City Plan Indore, 2016). While all these situations present an enormous opportunity for growth, it is important to keep a check on the developments. As development takes place, there is projected to be a rise in private owned vehicles and with the lack of pro per multi modal

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29 transport, this would further exacerbate the traffic congestion problem in the city. Along with this, these would also lead to deteriorate the air quality and adversely impact public health with increased commute times. Per Smart City eval uation report for Indore, a SWOT analysis was performed and a strategic blueprint for development was made for the next 5 10 years to increase livability and sustainability. The core focus of the strategic road map for Indore is Transit Oriented Developmen t. This strategy is expected to rejuvenate urban form, maintain architectural integrity, cultural inheritance, economic development and digitization (Smart City Plan Indore, 2016). By using TOD, the aim is to promote compact redevelopment by providing adeq uate housing for every income group. These also include decreasing travel cost and commute time, dependency on private vehicles, traffic congestion and pollution and crash rates. Apart from this, there is a great emphasis on promoting non motorized transp ort options and walking zones to decongest the core of the city. Metro Rail project for the city has been approved along with proposed schemes such as bike sharing systems and park and ride to increase active ridership for alternate modes of public transpo rt (Smart City Plan Indore, 2016). 2.5.2 City Vision and Goals Urban Mobility is another sector of concern as the lack of public transport options and poor road infrastructure. The foot path coverage is 27.62% as compared to the required standards which a re 50 75% as per MoUD SLB (Smart City Plan Indore, 2016). The city has goals to improve these conditions by introducing new provisions. Metro rail has been proposed of 64.05 miles along with 7 miles of BRT in operation (Smart City Plan Indore, 2016). Along with this, intelligent transport systems for traffic

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30 management, parking and electronic toll payment methods are in the proposal while improving nodes and interchanges. The aim to create a modal shift from private ve hicle towards public transport. To meas ure the progress of these transportation goals and advancements, the following indicators can be used: Increase in ridership of public transport. Better health index Improved air quality Reduction in carbon footprint 2.6 Walkability and Bicycling as an Ec onomic Alternative 10 20% of total transportation trips are completed by non motorized modes with links to public transit and privately owned vehicles (Litman, 2017). Considering walkability as an economic alternative provides mobility to citizens combined with benefits of exercise. Apart from this, is there evidence towards the fact that even when motorized travel has increased, people have been still using bicycling and walking as recreation (Litman, 2017). Improving conditions and infrastructure for bicy cling and walking provides an economically feasible way to improve urban transport. Walking and bicycling have been traditionally more affordable than motorized options. A major value is placed on driving in comparison to walking in conventional planning which reflects how transport is measured (Litman, 2003). As most travel surveys ignore short trips like non motorized links, walkability is not given a high value in the modes of transportation chain. The figure below shows how walkability is an affordable and convenient mode of travel.

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31 Table 2 1.Affordability in Modal Share. Source: Economic Value of Walkability Affordable and Efficient Own and operate an automobile Walk and bike for exercise Join a health Club Walk and bike children to school Chauffe r children to school Build sidewalks Build roads and parking facilities The table below shows the difference between communities considering only walking and communities showing walking linked with other modes of transport. Table 2 2. Walking Linked to Other Modes of Transport. Source: Economic Value of Walkability. Car Only Walking All or Part Transit All or Part Winnipeg 73% 16% 15% Vancouver 72% 20% 12% Calgary 72% 21% 12% Canada 69% 22% 10% Toronto 61% 24% 20% Ottawa 60% 33% 16% Average 68% 2 3% 14% Only 7% of Canadian urban communities are entirely by walking although 23% of them involve walking, similarly in Germany, 70% of the trips involve walking but only 22% are completely done by walking (Brog. Erl and James 2003). People on the sidewa lks, skaters or skateboarders usually go ignored in the surveys leading to the ignorance of pedestrian activity in traffic surveys (Haze 2000). Per the 2009 National Household Travel Survey, there has been an increase of 25% showing 10.9% of personal trips showing walking and 1% showing bicycling which is twice as indicated by any travel survey. Apart from this, a study shows the number of nonmotorized trips are actually six times greater than indicated by conventional surveys (Rietveld 2000).

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32 When compared in terms of distance of trips, walking looks insignificant but if it is evaluated in terms of number of trips or travel time, it sums up to a huge part of an average travel journey. Per a survey in the U.K., due to slower speeds and waiting periods at bus stops, travelers only constitute 5% of the person trips but 40% of the person minutes (Litman, 2017). Per the same survey, 2.8% of the total mileage is represented by walking but it represents 17.7% of the travel time a nd 24.7% of the trips (Litman, 2017) Undervaluing walkability and other non motorized travel can be detrimental as transportation decisions usually involve adjustments between different travel modes (Litman, 2003). These trends lead to automobile dependent communities because of the funding leading towards creation of wider roads, high speed zones and large transportation planning is done in a standardized way by using computer models like Highway design and Main tenance Model which assume that a person is better off spending 5 minutes driving to run an errand than walking or cycling for 10 minutes since it gives an equal or greater cost value to non motorized than motorized trips but these calculations ignore heal th and recreation benefits of nonmotorized travel (Litman, 2017). Reasons why walking is undervalued Difficultly of measurement Measuring walking is relatively a difficult task when compared to motorized travel modes. Counting the number of vehicles and speeds for traffic information is comparatively easy to count than the walkability parameters, especially considering the ignorance in traffic surveys for non motorized mode of travels. Due to these reasons there is a lack of data on non motorized travel methods for transportation planners to analyze.

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33 Perception of walking as low s tatus As traditionally walking has been a mode of travel of the low income communities, especially in developing countries, it is often not valued high and is lesser considerat ion while selecting a mode of travel. Ignorance of b enefits Often health and recreation benefits of active transportation are ignored in economic analysis of transportation modes. Apart from this walkability tends to increase mobility for people that cann ot afford other modes of transport like privately owned vehicles and even public transport in some cases. Cost b enefits One of the major reasons for not having an organized walking industry is the inexpensiveness of it as a mode of travel. In developing countries, the funding for walkability tends to be as low as 0.5 % of the total transportation budgets ignoring the chances of consumer cost savings but the prediction of these savings is still tough to predict (Smart City Plan Indore, 2015). Assuming wal kability takes c are of Itself Amongst decision makers, walking is often taken as granted and assumed that it will take care of itself (Goodman and Tolley, 2003). A great example of this is the lack of availability of sidewalks in communities often scaring people away from walking due to safety issues. Types of economic i mpacts The lack of resources for calculating economic benefits leads to ignorance of considering their benefits. The Active Transport Quantification tool (ICLEI 2007) is one such tool to m easure benefits like savings from shifting to walking from driving, public health benefits like more exercise, decrease in pollution, accidents and congestion (Litman, 2017). Accessibility The ability to reach desired goods, activities and services has b een traditionally defined as accessibility (Litman, 2003). Providing basic mobility needs

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34 for people who transportation disadvantaged can be achieved by increasing walkability in neighborhoods. Public transportation is a huge part of increasing effectivene ss of urban mobility which is why connecting people to public transport is an important issue that needs to be addressed. Transportation disadvantaged people include people with disabilities, low incomes, elderly population and children (Litman, 2017). The re are various methods to evaluate accessibility while considering the quality of pedestrian Nonmotoriz Consumer cost s avings Improved walkab ility infrastructure provides According to a study, households in car dependent neighborhoods spend 50% more on transportation related expenses (more than $8,500 annually) tha n households in neighborhoods with better mixed use patterns and more alternatives for travelling like public transit, bicycling and walking (less than $5,500 annually) (McCann 2000). These savings can be evaluated based on potential savings from other tra nsportation methods. In some cases, improvements in walkability infrastructures lead to reduced vehicle ownership and maintenance costs (Litman, 2017). Reducing externalities of t ransport Use of motorized vehicles leads to various public costs which incl ude costs for parking facilities, gas expenses, risk of crash and environmental damages (Murphy & Delucchi, 1998). To reduce these externalities, a shift from motorized to non motorized modes of transport has been proved to be an efficient idea. For shorte r vehicle trips, which usually tend to have higher vehicle dollar per mile costs, walking can be substituted to bring down costs. When vehicle engines

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35 get cold, the energy consumption increases rapidly increasing both the cost as well as environmental dama ges caused by the vehicles (Litman, 2017). In lieu with this relationship, a long motorized vehicle can be easily substituted by a potential short walking trip reducing externalities especially under peak urban conditions where the engines are running for a long time with minimum to no movement due to traffic congestion. Figure 2 4 External Costs of Automobile and Pedestrian Travel Source: Economic Benefits of Walkability The above figure explains the run down for cost savings on transport externalit ies by switching from driving to walking. Estimated savings according to this model range from 25 cents per vehicle mile in normal traffic conditions to 50 cents per vehicle mile in peak urban traffic conditions (Litman, 2009). Enhanced mixed land use i nit iatives Various economic, environmental and social costs are imposed with the creation of low density development including large amounts of land surfaced for wide roads and parking facilities (Burchell, 1998).

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36 Improvements in walkability infrastructure c an help reduce these costs by supporting Smart Growth initiatives which involve decreasing the amount of land required for transportation facilities and creating neighborhoods more accessible for walking and promoting mixed use patterns (Ewing et al., 2002 ). Evaluating these initiatives mandate an understanding of how transportation, especially walkability impact land use patterns (Litman, 2002). Promoting walkability reduces traffic noises and land required for travel and parking while creating more cluste red land use patterns. Walkable communities result in high density neighborhoods and reduce per capita land consumption taking a step towards building non automobile dependable societies. It is important to understand the economic, environmental and social reimbursements of different forms of land use patterns and high density developments (Arnold & Gibbons, 1998). Livability standards in a c ommunity Community Livability Standards refer to the social and environmental quality of a community for residents, visitors or employees of the community (Weissman & Corbett, 1992). Social Capital refers to the value of community relationships which include intra neighborhood interactions and community participation rates, especially between people of different economi c and social backgrounds (Forkenbrock & Weisbord, 2001). These factors create indirect benefits in the society including an increased sense of security and better property values as well as economic growth (Litman, 2011). Community livability increases wit h more safe and walkable streets in a neighborhood making walkability infrastructure a major contributor for better living standards (Forkenbrock & Weisbrod, 2001). Residents living on high speed and traffic streets have a lesser possibility of interacting with their neighbors than

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37 the residents living on less congested and lower speed streets (Appleyard, 1981). In addition, residents living on streets with less traffic show more consideration for the environment (Appleyard, 1981). The increase in property values and business due to better community livability conditions can be evaluated by methods like contingent valuation and hedonic pricing (Litman, 2001). The value of walkability can still be affected by factors community design (Eppli and Tu, 2000), aut omobile dependency, vehicular street traffic as a factor of pedestrian safety and closeness to public trails (NBPC, 1995). Although it is difficult to establish a direct relationship between reduced crime and walkability, increased community interaction ca n still be used a measure to lessen crime rates and relatable social issues in neighborhoods (Litman, 2002). Health. Insufficient physical exercise has been a major cause in creating health problems worldwide. According to health experts, at 30 minutes of moderate exercise is recommended at least 5 days a week with ten minute intervals (Surgeon General, 1999). According to Killingsworth and Lamming in 2001, the following diseases are directly related to lack of physical exercises, Obesity Heart disease S ome forms of Cancer Osteoporosis Diabetes Stroke Hypertension Depression and dementia Walking is one of the easiest forms of physical activities and with an increasing population which include children being at a risk of inadequate physical activity, it is the most practical way to avoid this problem (Litman, 2017). According to health experts,

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38 better balanced transportation systems enhance public health by promoting active transport, especially for older people who are particularly at a higher risk of di seases (Sallis, et al. 2004). There is research that displays the quantified health benefits of land use planning supporting walkability and bicycling increasing daily physical exercises (Litman, 2009). Per a research by Boarnet, Greenwald and McMilan in 2 008, improvements in street design that promote walking lead to reduced fatality rates. The table below gives the quantified framework of the research. Per a research in 2004 by ECU, 43% of people acquire the recommended physical activity when they live wi thin a ten minute radius of safe walkable places while only 27% achieve adequate physical exercise when they live in places without safe walkable places. In 2009, Tomalty and Haider conducted a study on how design principles in a community like land use mi x and density, street connectivity, availability of sidewalks, walkability index, etc. affect bicycling and walking in a neighborhood and their health benefits across 16 different and diverse neighborhoods. Per this evaluation, there is a statistically sig nificant relationship between increased bicycling and lower body mass indexes as well as lower hypertension while explaining that people living in a more walkable neighborhood have a high probability to walk at least 10 minutes every day while decreasing t heir chances of being acquiring obesity. Per a model developed for public health cost savings by Stokes, MacDonald and Ridgeway in 2008, the light rail system in Charlotte, NC is estimated to save a sum of $12.6 million over a period of nine years includi ng the cost savings in health by Evaluation Manual for Land Transport gives monetary values to the benefits received in

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39 health caused by active transportation methods and infra structure (LTNZ 2010). This evaluation assumes that half of the total benefit is received by the people opting to walk and bike while the other half is received by the society which include savings like hospital cost savings. The table below displays the v alues. Table 2 3 Health Benefits of Active Transportation Source: Economic Benefits of Walkability Internal External 2007 USD/mile Low 0.05 0.05 0.1 Cycling Mid 0.1 0.1 0.19 High 0.19 0.19 0.38 Low 0.12 0.12 0.24 Walking mid 0.24 0.24 0.48 High 0.48 0.48 0.96 This survey found higher weekly expenditures by consumers who travel by walking than those who drive or rider transit to downtown shopping districts in the UK. Per international research, a modal shift from motorized vehicles to non motorized vehicles result in an overall increase in road safety (Litman & Fitzroy, 2005). A prime example of this is the Netherlands, where a great amount of transport is non motorized and per capita traffic deaths and bicyclist death rate per kilometer i s much less than other motorized vehicle dependent countries (Pucher and Dijkstra, 2000). A great evaluation method for calculating public health benefits of walking and biking are public surveys to determine the average daily walking and bicycling miles c ompleted by

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40 a person in a neighborhood after improvements in walkability infrastructure (Boarnet et al., 2008). Growth in e conomy Walkability can impact economic growth in an area in increased business activity in the neighborhood attracting more investments (Leinberger & Alfonzo, 2012). In 2011, Tolley calculated the financial impacts of improved walking and bicycling infrastructure on vendors and residents of the neighborhood. In thi s study, he discovered that there is an increase in property values and rents and native financial activities with increased businesses opportunities following improved infrastructure for walking and bicycling. Comparing parking space requirements for bicy cles and cars, he determined that bicycle parking spaces create a much better retail spending as compared to the spending attained when the same space was used as car parking because of the higher spending trend of people walking or bicycling throw the sho ps as compared to people driving through the shops (Tolley, 2011). For a person driving through the businesses there is an extra effort involved for finding a parking spot and then walking from the parking spot to the shop as compared to a person that walk s or bikes to the store. In 2009, Sztabinski analyzed the impacts on retailers by creation of bike lanes in Seattle, Washington. Though these resulted in loss of parking on street spaces, the sales went up by 400%. The results of this research were as fo llow: Out of all the customers going to the shops, 90% either walk, bike or use public transit. Only 80% of the parking spots were used, even in peak hours. Customers who walk or bike spend most compared to customers to drive or use public transit.

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41 The num ber of retailers believing wider bike lanes and sidewalks improve business than otherwise is significantly higher. Loss in on street is easily replaced by nearby off street parking spots. Table 2 4 Expenditure on G oods by Mode of Travel Source: Economi c Benefits of Walkability Mode Weekly Expenditure (£) Bus 63 Car 64 On foot 91 Train/tube 46 Taxi/cycle 56 This survey found higher weekly expenditures by consumers who travel by walking than those who drive or rider transit to downtown shopping di stricts in the UK. Source: Economic Value of Walkability Consumer spending provides better employment and business opportunities in comparison to the spending on fuel and vehicles (Litman, 2004). To evaluate the benefits of walkability on retailers, market surveys and property assessments can be used to analyze the impact on commercial activities, competition, property values and employment opportunities. Equity The dispersal of opportunities and resources is defined as equity (Litman, 2017). Equity issue s can be divided into several types, two of which are horizontal equity and vertical equity. Horizontal equity prefers to assume that all people

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42 should be treated equally whereas vertical equity emphasizes that disadvantaged people deserve extra support (L itman, 2001). Using walkability, fair distribution of public resources can be ensured for non drivers and better financial circumstances and economic opportunities can be provided for economically and physically challenged people while ensuring basic mobil ity. Table 2 5 Indicators of Transportation Equity Source: Economic Benefits of Walkability Indicator Description Treats everybody equally This reflects whether a policy treats each group or individual equally. Individuals bear the costs they imp ose This reflects the degree to which user charges reflect the full costs of a transportation activity. Progressive with respect to income This reflects whether a policy makes lower income households better or worse off. Benefits transportation disadva ntaged Whether a policy makes people who are transportation disadvantaged better off by increasing their options or providing financial savings. Improves basic mobility and access This reflects whether a policy favors more important transport (emergency response, commuting, basic shopping) over less important transport. This table describes five indicators of transportation equity that can be used when evaluating walkability equity impacts. Source: Economic Value of Walkability

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43 It is crucial to conside r various factors while calculating equity impacts of transportation due to the various types of equity. Defining equity objectives and performance indicators is the most practical method for calculating impacts of equity (VTPI, 2008). Though equity benefi ts are tough to monetize, several neighborhoods place a great value on achieving them (Forkenbrock & Weisbrod, 2001). 2.7 Land Use Conflict Identification Strategy The LUCIS model has the five following steps: Defining goals and objectives Data inventory a nd preparation Defining and mapping land use suitability Integrating community values for land use preference Identifying potential land use conflict In this land use conflict methodology, the preference layers are classified into the low, moderate and low. The resulting matrix includes 27 possible preference values. Firstly, the community weighted values are aggregated for major land use categories as high, moderate and low. This research used the reclassify tool to cover that process. Hillsborough Cou nty in Florida, is the fourth largest county in the state and with services, and technology in the international trade market with 96% of its population living in urb an areas. The County has used GIS based decision making tools since 2002 and it has proved beneficial in lieu with the rapid pace of development (Zwick et. al. ,2016). With intense increase in population numbers, especially in developing countries, there i s an increased demand to intensify resources. Tabriz County in Iran used land suitability analysis for their decision making process to optimize utilization of their

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44 agricultural production. Land evaluation techniques were used to develop models to predict using this technique, Tabriz County had a clear indication of which areas should increase, decrease or be at the same intensity for agricultural land use. Land use suitabil ity methods provided them with an opportunity for cross comparison between current land use map and the one created with GIS assisted suitability analysis. Developing countries face a major challenge when it comes to using GIS based tools not in technol ogy but in data availability and organization structure (Yeh, 2007). A comprehensive staff training is needed in developing countries to make it a more useful resource in urban and regional planning. This creates a great opportunity for International assis tance agencies and GIS software companies (Yeh, 2007). Cuitzeo Lake Basin, Mexico performed a land use suitability analysis for sanitary landfill sitting with the aim of locating areas in compliance with environment codes and inter municipality accessibili ty (Delgado et. at, 2008). Using GIS, biophysical and socio economic data was processed and the analysis resulted in four potential areas for the site. This GIS based approach turned out to be a low cost alternative while benefitting and strengthening the decision making process in developing countries (Delgado et al., 2008).

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45 CHAPTER 3 STUDY AREA The study selected for this research is Indore, India. In the following figure, demographics are further explained. With a current population of 3.8 Million an d geographic area of 130966, it is most densely populated metropolitan in Central India (Census, 2016). Indore has been selected as one of first 100 smart cities to be established in the country. Following the selection, it has also qualified as in the fir st round of cities to receive funding and resources for development. Recently, it was elected as the cleanest city in India as per Clean India Initiative 2017. Figure 3 1. Indore, India City Base Map Source: Smart City Mission Indore

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46 Figure 3 2. T ransportation Network in Indore Source: Smart City Mission Indore The development to be performed in this project is an area based development. The selected area would be developed into a smart city which would include all essential elements as presented in the Smart City Mission Guidelines. Within this area, approximately 500 acres has been allocated for retrofitting, 50 acres have been allocated for redevelopment and 250 acres for green field development. This area is called Rajwada and the project is ca lled Rajwada Rejuvenation with goals for turning the historic inner city, market areas, riverfront and public spaces to mixed use and sustainable neighborhoods by retrofitting and redevelopment. By creating smarter

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47 transit linkages and using TOD strategies the goal is to bring down traffic congestion in the area and increase walkability. 3.1 Selection of Study Area Rajwada is considered as the downtown of Indore and selecting this area for an economic transformation was the most lucrative choice. Business centers in Rajwada are on a decline and real estate values are now getting stagnant. Apart from this, the ever increasing traffic has led to deteriorating traffic conditions and parking problems in the area. Most of public spaces are in close vicinities w ith slums and lack mass transit which further degrade the quality of living for citizens. By strategically transforming the study area, a domino effect is expected for the transformation of the rest of the city. Figure 3 3 Area Based Development Propo sal for Indore City Source: Smart City Mission Indore

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48 Figure 3 4. Pilot Area for Development in Smart City Indore Plan Source: Smart City Mission Indore 3.2 City Profile The key strengths of Indore include Trade and Commerce as it is a center for busi ness and various social groups coexist in the city and these qualities are inherited from Rajwada area. Some weaknesses of Indore include insufficient environmental services, traffic congestion and parking issues. Rajwada best displays both weaknesses and strengths for the city and would be the best prototype for the city. The selected region for Area Based Development, Rajwada is 742 acres in area. The delineation of the area has been done with respect to transit linkages, physical features, coverage of tr aditional markets and a mix of social and income groups. Most of the areas would best suit retrofitting strategy for transformation to smart city areas. Along with retrofitting,

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49 some areas would undergo redevelopment. By redeveloping selected government pa rcels, land can be monetized making Smart City Proposal financially viable. The selection of the project being Area Based was constructed with variety of expertise from technical, financial, policy making personals which included planners and experts from the sectors of MoUD, UADD(MP), DFID, Development Authorities and experts from chambers of commerce. The selection of this area is expected to improve the economy and create more jobs. The study area is densely populated and displays the values of Indore v ery closely. Per financial experts, going forward with this study area would solve two major issues, first, land monetization can be performed, and two, land can be made available to introduce smart features. The proposal for the study area includes road d evelopment, intersection improvements, increased pedestrian safety, promoting TOD designs, smart parking facilities, battery operated alternate modes of public transport, real time air quality monitoring and Intelligent Traffic Management. Redeveloped land is estimated to include high density mixed use and walkable communities with affordable prices while protecting the aesthetics of current buildings.

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50 CHAPTER 4 METHODOLOGY Land Use Conflict Identification Strategy (LUCIS Model) LUCIS is defined as a aim driven GIS model responsible for providing a spatial illustration of possible patterns of future land use (Zwick & Carr, 2007). This approach is based on the work of Eugene P. Odum. Figure 4 1. Work Flowchart Model The LUCIS model includes five ste ps while requiring three stakeholder groups to represent each of the following groups: agriculture, conservation and urban (Zwick & Carr, 2007). All the three groups have the same defined area for study and further their respective suitability is compared for potential conflict. The process is accomplished through the following five steps: Goals and objectives are defined for development. Data sources are identified for achieve the goals and objectives. Data collected is further analyzed to relative suitab ility.

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51 Resulting suitability for different land uses is combined using a weighting model, preferably Analytic Hierarchy Process. Different land use preferences are compared to create future land use conflict. All land uses generally found in the canopy of urban use are included under the urban land use category which include residential, office and commercial, retail, wholesale warehouses, institutional, industrial and recreational uses (Zwick & Carr, 2007). Suitability is defined as the degree to which t he land parcel is fit for a specific land use which is measured on a scale of 9 to 1 in this model, 9 being highly suitable and 1 being least suitable (Zwick & Carr, 2007). Preference is calculated by combining land use suitability by using a weight that d ecides the importance of that particular land use and measures to what degree a land use is preferred for a land use. Figure 4 2. Hierarchy of Goals, Objectives and Sub Objectives in LUCIS Analyzing Interval/Ratio Data Assigning utility values to int erval data is comparatively easier than nominal data since the values already have known intervals. ArcGIS Reclassify tool easily assigns values ranging from 1 to 9.

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52 Figure 4 3. Assigned Values in Reclassify Tool The above figure explains the property values for commercial parcels in Indore, India with 9 representing the highest suitable value (lowest price) and 1 representing the least suitable value (highest price) for developing land commercially. Analyzing nominal/ordinal d ata There are four metho ds to assign utility values to nominal/ordinal data (1) group voting technique (2) modeler assignment (3) modified Delphi process (4) pairwise comparison of separate features (Zwick & Carr, 2007). This research uses pairwise comparison which is a generic f orm of the analytical hierarchy process (Zwick & Carr, 2007). To begin this process, a model is created and the goal is stated and then all features of the dataset are inserted, finally all the components are compared for their usefulness in supporting the first category (Zwick & Carr, 2007). Categories are compared from 1 to 9, 1 being equally important and 9 being extremely important. The weights define the preferences for different land uses considering commercial suitability. In this example, proximity to roads is given highest preference while proximity to railway stations is given the least preference for building commercial establishments. Rescale by f unction This ArcGIS tool is used to range values per their respective suitability. The tool uses l ower threshold and upper threshold uses to

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53 calculate suitability which is calculated by using Zonal Statistics as a table, another ArcGIS tool. Using Zonal Statistics as a table, mean and standard deviation of the data set is calculated. The mean is used a s the lower threshold and the sum of mean and twice the standard deviation is used as the upper threshold. Figure 4 5. ArcGIS Tool Rescale by Function In the above example, mean of all commercial property values in Indore, India is Rs. 22,500 and the standard deviation is Rs.750 making upper threshold Rs. 24,000 and lower threshold Rs. 22,250. In this example, lower threshold gets highest suitability 9 and upper threshold gets lowest suitability 1 as low value properties for commercial development are preferable.

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54 Figure 4 6 Suitability Values V ersus Input Values in Rescale by Function Source: ArcGIS.com Mapping and d efining land use suitability case studies The suitability model consists of four major steps as follows: Statement of Intent Goals O bjectives Sub Objectives The figure further explains the hierarchy of the above stated steps. Figure 4 7. Hierarchy of S teps in Suitability Analysis Source: Overview of Suitability

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55 To perform suitability, one has to perform the modelling process from bottom up meaning starting with sub objectives. The figure below is an example for residential suitability based flood potential. Figure 4 8. Analysis model for the urban sub Source: Smart Land U se Analysis Figure 4 9. Analysis Model for the Urban Sub H The above model shows the selection of suitable lands based on proximity to hospitals. A health care facilities dataset was us ed as input layer and Euclidean Distance was performed on it (Zwick & Carr, 2007). After performing Euclidean Distance, Zonal Statistics was used to determine the mean and standard deviation of existing residential areas from the selected hospitals and med ical centers and these values were used as suitability values, cells with 0 to the mean value get 9 which is highest suitability because they are closer to existing residential units and further the gfchab Reclassify Tool UG1011SO112 gc_health.shp Make Feature Layer gc_health Layer Euclidean Distance Tool disthospital s Output Direction Reclassify Tool UG1 012SO23

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56 values decrease to 1 which is the sum of the mean and twi ce the standard deviation (Zwick & Carr, 2007). Table 4 1 LUCIS Urban Mixed Use Opportunity Matrix Mixed Use Value Description 111 Mixed Use with Low Preference 112 Residential Moderate Preference 113 Residential High Preference 121 PSP Moderate Pre ference 122 Residential and PSP Moderate Preference 123 Residential High Preference 131 PSP High Preference 132 PSP High Preference 133 PSP and Residential High Preference 211 Commercial Moderate Preference 212 Commercial and Residential Moderate Preference 213 Residential High Preference 221 Commercial and PSP Moderate Preference 222 All with Moderate Preferences 223 Residential High Preference 231 PSP High Preference 232 PSP High Preference 233 PSP and Residential High Preference 311 C ommercial High Preference 312 Commercial High Preference 313 Commercial and Residential High Preference 321 Commercial High Preference 322 Commercial High Preference 323 Commercial and Residential High Preference 331 Commercial and PSP High Prefe rence 332 Commercial and PSP High Preference 333 All with High Preferences The above matrix explains multiple preferences in this research in one matrix. The greenfield conflict matrix has been used here to identify different land use opportunities i n an existing urban core.

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57 The values in the table have been formed by reclassification of the suitability layers based on the following formula. LUCIS mixed use raster = ((commercial preference* 100) + (PSP preference* 10) + (Residential preference* 1)).

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58 CHAPTER 5 RESULTS and DISCUSSION Statement of Intent. Identify suitable for urban development within the city of Indore, India. Main Goals y suitable land for public and semi public land use (Institutional l and use) Table 5 1. Goals, Objectives and Sub Objectives for Commercial Development GOAL 1 Description 1 Identify suitable land for commercial land use. 1.1 Identify physically suitabl e land for commercial land use 1.1.1 Suitable land values. 1.2 Identify land proximally suitable land for commercial land use. 1.2.1 Proximity to major roads. 1.2.2 Proximity to commercial development. 1.2.3 Proximity to bus stands 1.2.4 Proximity to public and semi public development (Institutional). 1.2.5 Proximity to airports. 1.2.6 Proximity to railway stations.

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59 Table 5 2. Goals, Objectives and Sub Objectives for Institutional Development GOAL 2 Description 2 Identify suitable land for public and semi public land use ( Institutional Land Use) 2.1 Identify physically suitable land for PSP land use. 2.1.1 Suitable land values. 2.2 Identify land proximally suitable land for PSP land use 2.2.1 Proximity to major roads. 2.2.2 Proximity to bus stands. 2.2.3 Proximity to PSP development. 2.2.4 Proximity to commercial development. 2.2.5 Proximity to PUF (Public Utility Facilities). 2.2.6 Proximity to airports. 2.2.7 Proximity to railway stations.

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60 Table 5 3. Goals, Objectives and S ub Objectives for Residential Development GOAL 3 Description 3 Identify suitable land for residential land use. 3.1 Identify physically suitable land for residential land use 3.1.1 Suitable land values. 3.2 Identify land proximally suitable land for r esidential land use 3.2.1 Proximity to major roads. 3.2.2 Proximity to commercial development. 3.2.3 Proximity to bus stands 3.2.4 Proximity to Residential 3.2.5 Proximity to Public Utility and Facility 3.2.6 Proximity to public and semi public deve lopment 3.2.7 Proximity to airports. 3.2.8 Proximity to railway stations. General m etho d. Specific parameters were modified to suit certain analyses however, the basic structure of this model was used for the majority of the project. Use Euclidean Dist ance (Density) as the value raster, then use Zonal Statistics by Table to calculate the value range of specific land uses. Use Summary Statistics to calculate the mean and stdmean respectively. Use Rescale by Function, using mean as the lower threshold and [ mean + 2 (stdmean)] as the upper threshold.

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61 5.1. Goal 1 Id entify Suitable Land for Commercial Land U se 5.1.1. Identify Physically Suitable L ands Land v alues Based on historical data, found out the mean of mean land value of commercial development s in the city ( square meter) Used mean of mean as the lower threshold and mean of mean plus twice the standard deviation as the upper threshold Lower threshold holds highest suitability (9) and upper threshold has lowest suitability as land values are preferred low cost. High = Figure 5 1. Suitable Land Values for Commercial Development in the C ity 5. 1.2 Identify Proximally Suitable Land for Commercial Land U se 5. 1.2. 1 Proximity to major roads Method Found the mean distance of current commercial establishments from major roads based on historical data and used it as minimum distance (121 m).

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6 2 Ran statistics based on historical data and calculated the maximum distance aw ay from major roads (351 m). Ran suitability analysis and proposed suitable land for commercial land proximal to major roads. Tools used: Euclidean Distance, Zonal Statistics, Rescale by Function. Rationale: Commercial establishments would benef it from bein g near major roads so the scale ranges from anything at the minimum distance as being HIGHLY SUITABLE to anything at the maximum distance being LEAST SUITABLE. Figure 5 2. Suitable Land P roximal to Major Roads in the C ity 5 1.2.2 Proximity to commerc ial development Method. Found the mean distance of current commercial establishments from commercial developments based on historical data and used it as minimum distance (0 m). Ran statistics based on historical data and calculated the maximum distance aw ay from commercial developments (700 m). Ran suitability analysis and proposed suitable land for commercial land proximal to commercial development

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63 Tools used: Euclidean Distance, Zonal Statistics, Rescale by Function. Rationale: Commercial establishments would benef it from being near current commercial developments so the scale ranges from anything at the minimum distance as being HIGHLY SUITABLE to anything at the maximum distance being LEAST SUITABLE. Figure 5 3. Suitable Land Proximal to Commercia l D evelopment 5. 1.2.3 Proximity to bus s tands Method: Found the mean distance of current commercial establishments from bus stands based on historical data and used it as minimum distance (4950 m). Ran statistics based on historical data and calculated t he maximum distance away from bus stands (12,000 m). Ran suitability analysis and proposed suitable land for commercial land proximal to bus stands Tools used: Euclidean Distance, Zonal Statistics, Rescale by Function.

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64 Rationale: Commercial establishments would benef it from being near current bus stands so the scale ranges from anything at the minimum distance as being HIGHLY SUITABLE to anything at the maximum distance being LEAST SUITABLE. Figure 5 4. Suitable Land P roximal to Bus Stands 5. 1.2.4 Pr oximity to public and semi public development (Institutional) This step follows the same method and rationale as the above steps. Figure 5 5. General Steps in Suitability Analysis for PSP Tools Used. Zonal Statistics and Rescale by F unction Minim um Distance away from PSP (9) = 458m Maximum Distance away from PSP (1) = 1198m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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65 Figure 5 6. Suitable Land P roximal to PSP 5. 1.2.5 Proximity to the airport This step follows the same method and rationale as the above steps. Figure 5 7. Gene ral Steps in Suitability Analysis for Airport Tools Used. Zonal Statistics and Rescale by Function Minimum Distance away from Airports (9) = 500m Maximum Distance away from Airports (1) = 12,000m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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66 Figure 5 8 Suitable Lands Proximal to the A irport 5. 1. 2.5 Proximity to the rail lines This step follows the same method and rationale as the above steps. Figure 5 9 General Steps in Suitability Analysis for Airport Tools used. Zona l Statistics and Rescale by Function. Minimum Distance away from r ail lines (9) = 2138m Maximum Distance away from rail lines (1) = 5738 m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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67 Figure 5 10 Suitable Land Proximal to Rail L ines Identify land proximally suitable for commercial land use. To find proximally suitable land we combine all the sub objectives d uring weighted sum tool in ArcGIS. To combine the rasters, Analytic Hierarchy Process was used to provide weights to the layers. The following table explains the process further. Using the weights from AHP, the result was this following raster which indica tes proximally suitable land for commercial development. The table displaying results for AHP weighting are at the end of the chapter.

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68 Figure 5 11 Proxi mally Suitable L and for Commercial Development Identify suitable land for commercial development R esulting Raster = Physically Suitable Land + Proximally Suitable Land + Figure 5 12 Physically and Proximally Suitable Land for Commercial Development While weighing the two rasters, proximity was given 65% and physical suitability was given 35% to com bine. The resulting raster is as follows.

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69 Figure 5 13 Suitable Land for Commercial Land Use 5. 2. Identify Suitable Land for P ublic and Semipublic Land U se 5. 2 .1 Identify Physically Suitable L and for P ublic and S emipublic Land U se Suitable land value s Method. Based on historical data, found out the mean of mean land value of public and semipublic developments in the city ( 19432/ square meter) and standard Used mean of mean as the lower threshold and mean of mean plus twice the standard deviation as the upper threshold. Lower threshold holds highest suitability (9) and upper threshold has lowest suitability as land values are preferred low cost. High =

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70 Figure 5 14 Suitable Land V al ues for PSP Development in the C ity 5. 2 .2 Identify Proximally S uitable L and for PSP Land U se 5. 2 .2.1 Proximity to major roads Method. Found the mean distance of current PSP establishments from major roads based on historical data and used it as minimum distance (620m). Ran statistics based on historical data and calculated the maximum distance away from major roads (2020m). Ran suitability analysis and proposed suitable land for PSP land proximal to major roads. Tools used: Euclidean Distance, Zonal Stat istics, Rescale by Function. Rationale: PSP establishments would benef it from being near major roads so the scale ranges from anything at the minimum distance as being HIGHLY SUITABLE to anything at the maximum distance being LEAST SUITABLE.

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71 Figure 5 1 5 Suitable Land proximal to Major Roads in the C ity 5. 2.2.2 Proximity to bus stands Method. Found the mean distance of current PSP establishments from bus stands based on historical data and used it as minimum distance (761 m). Ran statistics based on historical data and calculated the maximum distance away from PSP developments (1760 m). Ran suitability analysis and propo sed suitable land for PSP development proximal to bus stands Tools used: Euclidean Distance, Zonal Statistics, Rescale by Function.

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72 R ationale: PSP establishments would benef it from being near current bus stands so the scale ranges from anything at the minimum distance as being HIGHLY SUITABLE to anything at the maximum distance being LEAST SUITABLE. Figure 5 16 Suitable Land prox imal to Bus S tands 5. 2.2.3 Proximity to PSP Method. Found the mean distance of current PSP establishments from PSP establishments based on historical data and used it as minimum distance (100 m). Ran statistics based on historical data and calculated the m aximum distance away from bus stands (2,000 m). Ran suitability analysis and proposed suitable land for PSP land proximal to PSP establishments

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73 Tools used: Euclidean Distance, Zonal Statistics, Rescale by Function. Rationale: PSP establishments would benef it from being near current PSP establishments so the scale ranges from anything at the minimum distance as being HIGHLY SUITABLE to anything at the maximum distance being LEAST SUITABLE. Figure 5 17 Suitable Land P roximal to PSP establishments 5. 2 .2 .4 Proximity to commercial development This step follows the same method and rationale as the above steps. Figure 5 18 General Method for Commercial Development Tools used. Zona l Statistics and Rescale by Function. Minimum Distance away from PSP (9) = 2117m Maximum Distance away from PSP (1) = 5277m

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74 Figure 5 19 Suitable Land P roximal to Commercial Development 5. 2.2.5 Proximity to public u tility and f acilities (PUF) This step follows the same method and rationale as the above steps. Figure 5 20 General Method for PUF Tools Used. Zonal Statistics and Rescale by Function. Minimum Distance away from PUF (1) = 1695m Maximum Distance away from PUF (9) = 2750m Rationale: PSP establishments would benefit from being further away from PUF e stablishments

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75 Figure 5 21 Suitable Lands P roximal to PUF 5. 2.2.6 Proximity to the airports This step follows the same method and rationale as the above steps. Figur e 5 22 General Method for Airports Tools Used. Zona l Statistics and Resca le by Function. Minimum Distance away from the airport (9) = 384m Maximum Distance away from the airport (1) = 683m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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76 Figure 5 23 Suitable Land Proximal to the A irport 5. 2.2.7 Proximity to rail l ines This step follows the same method and rationale as the above steps. Figure 5 24 General Method for Rail Lines Tools Used. Zona l Statistics and Rescale by Function. Minimum Distance away from rail lines (9) = 2919m Maximum Distance away from rail lines (1) = 5738 m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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77 Figure 5 25 Suitable Lan d Proximal to Rail L ines To find proximally suitable land we combine all the sub objectives during weighted sum tool in ArcGIS. To combine the rasters, Analytic Hierarchy Process was used to provide weights to the layers. The following table explains the p rocess further. Using the weights from AHP, the result was this following raster which indicates proximally suitable land for commercial development. The results from AHP weighting are displayed at the end of the chapter.

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78 Figure 5 26 Proximally S uitab le L and for PSP Development Identify suitable land for PSP development Resulting Raster = Physically Suitable Land + Proximally Suitable Land + Figure 5 27 Physically and Proximally Suitable Land for PSP Development While weighing the two rasters, prox imity was given 65% and physical suitability was given 35% to combine. The resulting raster is as follows which combines both physically and proximally suitable surfaces.

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79 Figure 5 28 Suitable Land for PSP Land Use 5.3. Identify Suitable L ands for Res idential Development 5.3.1. Identify Land Physically Suitable for Residential D evelopment Suitable land v alues Based on historical data, found out the mean of mean land value of public and semipublic developments in the city ( Used mean of mean as the lower threshold and mean of mean plus twice the standard deviation as the upper threshold. Lower threshold holds highest suitability (9) and upper threshold has lowest suitability as land values are preferred low cost. High =

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80 Figure 5 29 Suitable Land Values for R esidential Development in the C ity 5. 3.2 Identify Proximally Suitable Lands for Residential D evelopment 5. 3.2.1 Proximity to PSP development This step follows the same method and rationale as the above steps. Figure 5 30 General Method for PSP Tools used. Zona l Statistics and Rescale by Function. Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitabilit y.

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81 Minimum Distance away from PSP (9) = 611m Maximum Distanc e away from PSP (1) = 1650m Figu re 5 31 Suitable Land proximal to PSP Development 5. 3.2.2 Proximity to major r oads This step follows the same method and rationale as the above steps. Figure 5 32 General Method for Roads Tools used. Zonal Statistics and Rescale by Function Minimum Distance away from Major Roads (9) = 200m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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82 Maximum Distance away from Major Roads (1) = 650m Figure 5 33 Suitable Lands P roximal to Major Roads 5. 3.2.3 Proximity to bus stands This step follows t he same method and rationale as the above steps. Fi gure 5 34 General Method for Bus Stands Tools used. Zonal Statistics and Rescale by Function Minimum Distance away from the airport (9) = 951m Maximum Distance away from the airport (1) = 185 1m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitab ility.

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83 Figure 5 35 Suitable Land Proximal to the Bus S tands 5. 3.2.4 Proximity to commercial e stablishments This step follows the same method and rationale as the above steps. Figure 5 36 General Method for Commercial Development Tools used. Zo nal Statistics and Rescale by Function Minimum Distance away from commercial establishments (9) = 957m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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84 Maximum Distance away from commercial establishments (1) = 2350m Figure 5 37 Suitable Land Proximal to Commercial E stablishments 5. 3.2.5 Proximity t o PUF e stablishments This step follows the same method and rationale as the above steps. Figure 5 38 General Method for PUF establishments Tools used. Zonal Statistics and Rescale by Function Minimum Distance away from PUF establishments (1) = 1249m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Su itability.

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85 Maximum Distance away from PUF establishments (9) = 2800m Figure 5 39 Suitable Land Proximal to PUF E stablishments 5. 3.2.6 Proximity to residential e stablishments This step follows the same method and rationale as the above steps. Fig ure 5 40 General Method for Residential Establishments Tools Used. Zona l Statistics and Rescale by Function Minimum Distance away from residential establishments (9) = 0m Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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86 Maximum Distance away from residential establishments (1) = 800m Figure 5 41 Suitable Land Proximal to Residential E stablishments 5. 3.2.7 Proximity to the a irport This step follows the same method and rationale as the above steps. Figure 5 42 General Method for Airport Suitability Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Leas t Suitability.

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87 Tools Used. Zonal Statistics Rescale by Function. Minimum Distance away from the airport (9) = 400m Maximum Distance away from the airport (1) = 12000m Figure 5 43 Suitable Land Proximal to the A irport 5. 3.2.8 Proximity to rail l ines This step follows the same method and rationale as th e above steps. Figure 5 44 General Method for Rail Line Suitability Find Minimum & Maximum Distance Run Suitability Analysis Final Suitability Raster indicating Highest and Least Suitability.

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88 Zonal Statistics Rescale by Function. Minimum Distance away from rail lines (9) = 2426m Maximum Distance away from rail lines (1) = 4250m F igure 5 45 Suitable Land Proxi mal to Rail L ines To find proximally suitable land we combine all the sub objectives during weighted sum tool in ArcGIS. To combine the rasters, Analytic Hierarchy Process was used to provide weights to the layers. The following table explains the process further. Using the weights from AHP, the result was this following raster which indicates proximally suitable land for commercial development.

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89 Figure 5 46 Proximally S uitable L and for Residential Development Resulting Raster = Physically Suitable Land + Proximally Suitable Land + Figure 5 47 Physically and Proximally Suitable Land for Residential Development While weighing the two rasters, proximity was given 65% and physical suitability was given 35% to combine. The resulting raster is as follows.

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90 Figure 5 48 Suitable Land for Residential Land Use 5.4. Conflict Surface This research creates a conflict surface by combining commercial, institutional and residential suitability surfaces. The suitability surfaces must be changed from a scale of 9 to 1 to a scale of 3 to 1 by using the reclassify tool in ArcGIS. The mixed use pattern represented by the conflict surface scales from 333 (highly suitable for all three land uses) to 111 (least suitable for all three land uses).

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91 Figure 5 49 Reclas sified Commercial Suitability Surface Figure 5 50 Reclassified Institutional Suitability Surface

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92 Figure 5 51 Reclassified Residential Suitability Surface + + Figure 5 52 Combining the Above Three Surfaces gives the Final Conflict S urf ace.

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93 Figure 5 53 Final Conflict Surface for High Density Mixed Land Use This proposed mixed use development promotes walkability and bicycling by increasing accesibility to decrease overall costs. By maximizing the economic benefits of walking this res earch establishes the rationale for creating a dense mixed use land use in the city. This research shows the financial limitations for the Smart City Indore initiative and provides recommendations on how better strategies can be used to increase the overal l effiency fina ncially and physically. T able 5 4 shows the area alloted to particular land uses.

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94 Table 5 4 Area Available for Different Land Uses Value Count Area in Sq.M Area in Acres 112 2708 2437200 602 113 127247 114522300 28287 121 29 26100 6 122 36688 33019200 8156 123 129788 116809200 28852 132 1120 1008000 249 133 63 56700 14 212 2477 2229300 551 213 6815 6133500 1515 221 8794 7914600 1955 222 90614 81552600 20143 223 33907 30516300 7538 231 6397 5757300 1422 232 9691 8721900 2154 23 3 11 9900 2 321 20025 18022500 4452 322 22948 20653200 5101 323 497 447300 110 332 3857 3471300 857

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95 5.5. Applications of Walkability in Planning Proportional Share This method is based on allocating transport resources according to the mode share of travel, for example, if walking represents 2% of the travel, it gets 2% of the funding and other resources. As seen before 5 to 10 percent of the travel involves walking and 15 to 30% percent of urban trips involve at least a walking link and following a proportional share a great part of transportation resources would be dedicated to walking (Litman, 2017). Currently, it is tough to see what percentage of transportation funding goes towards non motorized vehicles since budget separations are vague in most cases. Local governments in United States provide approximately 5 15% of the budget towards walking infrastructure but the federal and state governments significantly less support (Litman, 2017). Even the state of Oregon which is considered as a global le ader in non motorized travel promotion, provides 2% of its budget to transportation while other states on average spend less than 1% (Litman, 2017). The table below further shows budget allocation disparity towards walking and bicycling infrastructure. Tab le 5 5 United States Roadway Expenditures Source: Economic Value of Walking Roadway Expenditures(billio ns) Walking Fac ility Expenditures(billio ns) Estimated Portion Devoted to Walking Federal $30.80 $0.21 0.60% Local $31.30 $3.10 10% Totals $128.50 $4.60 3.50%

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96 With inclusion of public resources like parking facilities and traffic services, the discrepancy fur ther increases. Walking and bicycling in addition to the above benefits also provide recreational benefits. If proportional share is followed, the budgets for walking and bicycling would increase by 10 times increasing opportunities for recreation as well. Transportation Cost Allocation. Cost allocation refers to the amount of spending done by a user group on the mode of transport facility and its service which include road tolls, fuel spending and other vehicle maintenance costs (FHWA, 1997). It is largel y believed that people with motorized vehicles pay more taxes towards the transportation infrastructure. While vehicle related taxes and fees pay for major highways, the local roads are still paid by the general tax payers regardless of vehicle ownership ( Litman, 2017). The average American household pays hundreds of dollars in tax money for traffic services and local road while paying hundreds for parking subsides as well (Litman, 2017). After considering all factors, motorists on an average pay less than non motorists while walking still receives less share in funding and other resources (Litman, 2009). Cost Benefit Evaluation Cost Benefit analysis is considered the most efficient method analyzing transportation programs and policies (Litman, 2001). R igorous application of cost benefit analysis provides better resources for walking for the following reasons: Improved calculations of walking trips will increase the recognition for the benefits and demands for more walkable neighborhood and infrastructur e (Litman, 2017).

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97 Comprehensive analysis of walkability to explore its benefits would provide better justification for investments in non motorized transportation opportunity. (Litman, 2017). Smart growth land use management strategies have been recently gaining recognition placing high values on walking and bicycling (VTPI, 2008). To evaluate the environmental and equity impacts of walking and bicycling more comprehensive cost benefit analysis is required for improvements in budgets for walking and bicycl ing as a part of the transportation budget (Litman, 2017). The following figure indicates that walking is found to be involved in quarter of all trips and sometimes as high as half of the trips in India making Indian cities highly suitable for walking, hi gh density mixed use and non motor vehicle zones. Figure 5 54 Trip Mode Shares in Indian Cities Source: MOUD. 2008. Study on Traffic and Transportation Policies and Strategies in Urban Areas in India. Per a survey conducted on six Indian cities by Cle an Air Asia, Indian cities have relatively low walkability scores due to poor infrastructure and unsafe environment with

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98 average score being 47 where 20 is minimum and 100 is maximum. A comparison of cities in India and cities in developed countries can provide better context to the lack of walking in India. The following figure displays the results of the survey. Figure 5 55 Variation in Walkability Ratings in S ix cities Source: Walk Score and Walkability in Indian Cities The previous figure illus trates the low walking score in Indian cities as compared to the cities in United States. The same survey presented the travel chara cteristics in Indore as follows. Figure 5 56 Time Spent on Travel M ode in Indore City Source: Clean Air Asia.

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99 Figure 5 57 Average Time from Residence to D estination in Indore City Source: Clean Air Asia. Figure 5 58 Preferred Improvement in F acilities in Indore City Source: Clean Air Asia. According to Times of India in 2015 the average weekly commute time has in creased considerably in the last 7 years. The following figure shows the in crease in New Delhi and Mumbai, from 8 hours and 46 minutes in 2008 to 12 hours and 30 minutes in 2015.

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100 Figure 5 59 Incr ease in Average Weekly Commute T ime Source: The Hindu. This increase can negatively impact social relationships and personal health of an individual. While 51% of the Indian population uses non motorized transport, the space allotted to them on the roadway is non existent (The Hindu, 2015). By 2040, India is e xpected to face an increase in cars by 775% (NDTV, 2016) which would mean 7 more cars would be fighting for the same space on the road in comparison to 2016. For years, the local and central governments have responded to these congestion and safety issues by either widening the roads or building new ones. The sales pitch for freeways is always that they solve congestion but they are only good for longer distances and result in regional growth. Building more freeways leads to a temporary solution but as tim e goes on, they only exacerbate the traffic congestion problem. Apart from the negative impacts, building freeways are extremely expensive in comparison to building or improving street furniture for walking and biking. As mentioned earlier in the paper, th e cost dedicated towards walking and biking in a transportation budget is 2% in even the most bike and pedestrian friendly cities. The

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101 emphasis of the investments has been on moving cars rather than moving people for the longest of times. Public transit is efficient in moving people from many places to a few places but lacks the ability to move people from few places to many places making cities and neighborhoods car dependent and increasing private vehicle ownership. Cities were originally designed to maxi mize exchange of goods and minimize travel, so a well designed city would be one where there is less transportation and more accessibility by putting thing closer (Moore, 2014). According to National Crime Records Bureau of India, every 4 minutes an India n resident is killed on the roads and according to Stanford Law School in 2013, human error is responsible for 90% of the traffic crashes which makes it hard to ignore that eliminating vehicles from roads by promoting walking and biking would provide a soc io economic benefit to the society, especially in a India where 51% of the population uses non motorized mode of transport exposing a large population to risk. Figure 5 60 Change in Transit Planning A pproach Source: USC Price The above diagram explai ns the difference between conventional transportation planning and modern day approaches to tackle challenges with a new perspective. By

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102 focusing on a paradigm shift in planning, these strategies bring the focus of the transportation system from moving car s to moving people. 5.5. Budget Restructuring Per Smart City Plan for Indore City, the Transportation and Walkability budget is budget would be allocated towards walking and biking. The following figure displays the debt repayment and collection involved in the Smart City Plan. Figure 5 6 1 Source of F unding and Debt Repayment S tructure for Smart City Plan I ndore Source. Smart City Indore Considering a transportation budget restructuring and increasing the walking and bicycling budget even by 2% could lead to a faster debt repayment scheme while increasing the efficiency of available funds. Like most projects, Smart City Indore also suffers from deficiency of funds an d by slightly altering the transportation budget, the city can benefit from the economic perks of increased walkability and bicycling. To provide high density mixed use development plans, LUCIS has been used for years and it provides a solid analytical bas e for the plan. By combining LUCIS and budget incentives for walking as well as bicycling, t he final development plan has the potential

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103 to provide high economic and social benefits. Lack of safety is a major issue in developing countries like India right n ow growing congestion is only making it worse, this plan better supports safety and focuses on congestion decreasing initiatives. This form of development also favors a rise in retail and employment sector, by using suitability analysis. Apart from this pe ople in India have had a history of walking since ancient times and so it would not be an irrational argument to propose a plan where people leap frog from using their cars on a daily basis to walking in close proximities. Apart from this a planned transit system taking people from one urban center to the other would work very well with this system, for example, an improved BRT or Metro. This could make it easier to travel regionally for people living in high density mixed use neighborhood while improving a ccessibility and decreasing car usage at the same time.

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104 Table 5 6 AHP for Commercial Development Roads Commercial Bus PSP Residential Airport Rail Roads 1 4 3 5 4 5 6 Commercial 0.25 1 2 3 3 4 5 Bus 0.333333333 0.5 1 3 3 5 6 PSP 0.2 0.33333333 0.33 1 2 4 5 Residential 0.25 0.33 1 0.5 1 3 5 Airport 0.2 0.25 0.2 0.25 0.33 1 3 Rail 0.166666667 0.2 0.16 0.2 0.2 0.33 1 SUM 2.4 6.61 7.69 12.95 13.53 22.33 31 Standardized Matrix Roads Commercial Bus PSP Residential Airpo rt Rail Weight % Roads 0.416666667 0.60514372 0.390117 0.3861 0.295639 0.223914 0.193548 35 Commercial 0.104166667 0.15128593 0.260078 0.23166 0.221729 0.179131 0.16129 18 Bus 0.138888889 0.07564297 0.130039 0.23166 0.221729 0.223914 0.193548 17 PSP 0. 138888889 0.05042864 0.042913 0.07722 0.14782 0.179131 0.16129 11 Residential 0.104166667 0.04992436 0.130039 0.03861 0.07391 0.134348 0.16129 9 Airport 0.083333333 0.03782148 0.026008 0.019305 0.02439 0.044783 0.096774 4 Rail 0.069444444 0.03025719 0.0 20806 0.015444 0.014782 0.014778 0.032258 6

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105 Table 5 7 AHP for Residential Development PSP Roads Bus Commercial Industrial Residential Airport Rail PSP 1 2 3 2 2 3 6 5 Roads 0.5 1 2 3 3 4 5 5 Bus 0.33 0.5 1 2 3 4 6 6 Commercial 0.5 0.33 0.5 1 2 2 6 5 Industrial 0.5 0.33 0.33 0.5 1 2 5 5 Residential 0.33 0.25 0.25 0.5 0.5 1 4 6 Airport 0.166667 0.2 0.16 0.16 0.2 0.25 1 3 Rail 0.2 0.2 0.16 0.2 0.2 0.16 0.33 1 Sum 3.52 4.81 7.4 9.36 11.9 16.41 33.33 36 Weight Standardized Mat rix PSP Roads Bus Commercial Industrial Residential Airport Rail PSP 0.284091 0.4158 0.405405 0.2136752 0.168067 0.182815 0.150015 0.138889 0.24 Roads 0.142045 0.2079 0.2079 0.3205128 0.252101 0.243754 0.180018 0.138889 0.21 Bus 0.09375 0.103 95 0.135135 0.2136752 0.252101 0.243754 0.180018 0.166667 0.1725 Commercial 0.142045 0.068607 0.067568 0.1068376 0.168067 0.121877 0.150015 0.138889 0.12 Industrial 0.142045 0.068607 0.044595 0.0534188 0.084034 0.121877 0.150015 0.138889 0.1 Residential 0.09375 0.051975 0.033784 0.0534188 0.042017 0.060938 0.120012 0.166667 0.0775 Airport 0.047348 0.04158 0.021622 0.017094 0.016807 0.015235 0.030003 0.083333 0.03375 Rail 0.056818 0.04158 0.021622 0.0213675 0.016807 0.00975 0.009901 0.027778 0.02

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106 Ta ble 5 8 AHP for PSP Development Roads Bus PSP Commercial PUF Airport Rail Roads 1 4 3 4 4 6 6 Bus 0.25 1 4 3 4 5 5 PSP 0.33 0.25 1 3 4 5 5 Commercial 0.25 0.25 0.25 1 3 6 5 PUF 0.25 0.25 0.25 0.33 1 6 6 Airport 0.16 0.2 0.2 0.16 0.16 1 2 Rail 0.16 0.2 0.2 0.2 0.16 0.5 1 SUM 2.4 6.15 8.9 11.69 16.32 29.5 30 Roads Bus PSP Commercial PUF Airport Rail Weight Roads 0.416667 0.650407 0.337079 0.3421728 0.245098 0.20339 0.166667 34 Bus 0.104167 0 .162602 0.449438 0.2566296 0.245098 0.169492 0.166667 22 PSP 0.1375 0.04065 0.11236 0.2566296 0.245098 0.169492 0.166667 16 Commercial 0.104167 0.04065 0.02809 0.0855432 0.1838235 0.20339 0.166667 11 PUF 0.104167 0.04065 0.02809 0.0282293 0.0612745 0 .20339 0.2 10 Airport 0.066667 0.03252 0.022472 0.0136869 0.0098039 0.033898 0.066667 4 Rail 0.066667 0.03252 0.022472 0.0171086 0.0098039 0.016949 0.033333 3

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107 CHAPTER 6 LIMITATIONS and CONCLUSION 6.1 Limitations There were limitations with this r esearch. The first limitation was to access the GIS data for land values in the city of Indore. Secondary data only included basic land uses divided over Khasras. To calculate the values of different land uses, approximate values were used which required f illing excel tables and translating from Hindi to English as most of the government documents are still in Hindi. This lowered the accuracy of land value analysis due to which this research had to put a low weightage on Land Value Suitability while combini ng all suitability surfaces. Although, the data lacks a little accuracy, once better data is received it can be easily put in the system to improve overall accuracy. The second limitation for this research was the lack of communication with actual stakehol ders in the city, due to a travel barrier and time difference between the countries, meetings and interviews were tough to schedule. Once there is chance for this research to be presented within the city, feedbacks can further strengthen the research. The third limitation for this research would be lack of economic data specifications in the smart city plan and lack of literature regarding economic value of walkability in India. This research wanted to establish a direct relationship between the money saved from creating a high density mixed use and walkable plan to the debt repayment structure of the Smart City Plan. The specifications for money allocated to walking and biking in the transportation budget are not present in the data and the current conversi on of money saved by increased walking and bicycling initiatives in United States to money saved in rupees in India is a tough transition as it involves many

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108 externalities. With further research into making an accurate conversion between the two currencies this research can improve. The fourth limitation was the absence of soils and flooding data for the city which could have improved the analysis. The final limitation was the limited usage of ArcGIS in India. Due to this, the data collection process was s trenuous and there was an understanding barrier for the people who oversaw dispatching the required dataset. 6.2 Conclusion The purpose of this study was to evaluate the land use development and transportation plan for City of Indore, India while suggestin g alterations to its Smart City Plan. The results displayed that there is over 1000 Acres of land suitable for high density mixed use which is spread across the city with a bulk of it in the center of the city. Apart from this 11,891 Acres of land is moder ately suitable mixed use development, some of which even extends to the outskirts of the city. With massive growth and urbanization occurring in the city, the government would benefit a great deal by using their funds allocated for mixed use development in these certain locations with respect to their high suitability. The LUCIS analysis created a master plan or surface for the city pinpointing suitable land uses for land parcels. With feedback from planners in the city and stakeholders, this study would pr ovide great potential for change. The secondary purpose of this study was to introduce a paradigm shift in traditional land development and transportation planning. The study identifies current transportation challenges and how traditional planning strateg ies have only turned out to be a temporary solution while exacerbating those issues in the long run. Further, it explains how the everlasting problem of funding shortage for modern transportation interventions can be minimized. This research proposes a par adigm shift in conventional planning by

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109 increasing budgets for walking and biking for infrastructural improvements to reap economic, social and health benefits induced by it. After explaining what the paradigm shift could be, it explains how to achieve it by using LUCIS and producing a high density mixed use and walkable plan for the city. By putting things closer to each other, it increases accessibility and shifts the focus from moving cars back to moving people. Especially, in a country like India where 51% of the total population still uses non motorized form of transportation, a study putting emphasis on improved walking and biking infrastructure could be beneficial for the planning authorities (The Hindu, 2015). With cities like Indore still fighting d igitization and use of ArcGIS in planning decision making and processes, this could be a great start and introduction of LUCIS as a decision making tool could prove lucrative for departments struggling with analysis of different land uses. Although, this s tudy cannot be defined as the one solution to planning problems in India, it does a fairly good job of managing some of the current issues and with better data, communication and feedback from stakeholders, it will only improve as we move on building up on this first initial step.

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110 LIST OF REFERENCES Appleyard, D. (1981), Livable Streets. University of California Press (Berkeley). Arnold, C., & Gibbons, J. (1996). Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. American Planni ng Association Journal, 62(2), 243 258. Boarnet, M. (1995). New Highways & Economic Productivity: Interpreting Recent Evidence. Journal of Planning Literature, 11(4), 476 486. Boarnet, M.G., Greenwald, M., & McMillan, M.E. (2008). Walking, Urban Design, and Health: Toward a Cost Benefit Analysis Framework. Journal of Planning Education and Research, 27(3), 341 358. Brog, W., Erl, E., & James, B. (2003). Does Anybody Walk Anymore?. Sustainable Transport: Planning for Walking and Cycling In Urban Environme nts, 59 69. Burchell, R. (1998). The Costs of Sprawl Revisited. Transportation Research Board, Report 39, (www.trb.org). Delgado, O.B, Mendoza, M., Granados, E.L., & Geneletti, D. (2008). Analysis of land suitability for the siting of inter municipal lan dfills in the Cuitzeo Lake Basin, Mexico. Science Direct, 28(7), 1137 1146. ECU (2004a). Physical Inactivity Cost Calculator (www.ecu.edu/picostcalc). College of Health & Human Performance, East Carolina University ( www.ecu.edu ); documentation at www.ecu.e du/picostcalc/pdf_file/Methods.pdf ECU (2004b). Physical Activity Facts and Figures. College of Health & Human Performance, East Carolina University ( www.ecu.edu ); at www.ecu.edu/picostcalc/pdf_file/FactsandFigures.pdf. Eppli, M., & Tu, C.C. (2000). Valui ng the New Urbanism; The Impact of New Urbanism on Prices of Single Family Homes. Urban Land Institute, ( www.uli.org ). Ewing, R., Pendall, R., & Chen, D. (2002). Measuring Sprawl and Its Impacts. Smart Growth America, ( www.smartgrowthamerica.org ). Feizizad eh, B., & Blaschke, T. (2013). Land suitability analysis for Tabriz County, Iran: a multi criteria evaluation approach using GIS. Journal of Environmental Planning and Management, 56(1), 1 23. Forkenbrock, D., & Weisbrod, G. (2001). Assessing the Social an d Economic Effects of Transportation Projects. NCHRP, Report 456, ( www.trb.org ). Goodman, R., Tolley, R. (2003). The Decline of Everyday Walking In The UK: Explanations And Policy Implications. Sustainable Transport: Planning for Walking and Cycling In Urb an Environments, 70 83.

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111 Haze, G., (2000). Counting Pedestrians. Walk San Francisco, ( www.walksf.org/essays/pedCountEssay.html ). Indore Municipal Corporation & Mehta and Associates. (2015). Smart City Indore. Killingsworth, R. E., & Lamming, J. (2001). Dev elopment and public health. Urban Land, 12 17. Leinberger, C.B., & Alfonzo, M., (2012). Walk this Way: The Economic Promise of Walkable Places in Metropolitan Washington, D.C. Brookings Institute ( www.brookings.edu ); at www.brookings.edu/research/papers/20 12/05/~/media/Research/Files/Papers/2012/5/2 5%20walkable%20places%20leinberger/25%20walkable%20places%20leinberger.pdf. Litman, T.A. (2002). Evaluating Transportation Land Use Impacts. VTPI, ( www.vtpi.org/landuse.pdf ). Litman, T.A. (2003). Measuring Transp ortation Traffic, Mobility and Accessibility. ITE Journal ( www.ite.org ), 73(10), 28 32. Litman, T.A. (2009), Transportation Cost and Benefit Analysis. VTPI, (www.vtpi.org). Litman, T.A. (2011), Evaluating Transportation Economic Development Impacts. VTPI, ( www.vtpi.org ). Litman, T.A. (2017). Economic Value of Walkability. Journal of the Transportation Research Board, 1828. doi: 10.3141/1828 01 Litman, T.A., & Fitzroy, S. (2005). Safe Travels: Evaluating Mobility Management Traffic Safety Impacts. VTPI ( www. vtpi.org ); at www.vtpi.org/safetrav.pdf LTNZ (2010). Economic Evaluation Manual (EEM) Volumes 1 & 2. Land Transport New Zealand ( www.landtransport.govt.nz ); at www.landtransport.govt.nz/funding/manuals.html (Active transportation health benefits data, 2 (3.8), 3 22. McCann, B., (2000). Driven to Spend; The Impact of Sprawl on Household Transportation Expenses. Surface Transportation Policy Project, ( www.transact.org ) Mehra, S., & Verma, S. (2016). Smart Transportation transforming Indian cities. Grant Thornton India. Murphy, J., & Delucchi, M. (1998). A Review of the Literature on the Social Cost of Motor Vehicle Use. Journal of Transportation And Statistics, 1(1), 15 42. NBPC (1995), The Economic and Social Benefits of Off Road Bicycle and Pedestrian F acilities. National Bicycle and Pedestrian Clearinghouse, (2), ( www.bikefed.org ). Pucher, J., & Dijkstra, L., (2000). Making Walking and Cycling Safer: Lessons from Europe. Transportation Quarterly, 54(3), at www.vtpi.org/puchertq.pdf.

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112 Rietveld, P., (2000) Nonmotorized Modes in Transport Systems: A Multimodal Chain Perspective for The Netherlands. Transportation Research D, 5(1), 31 36 Sallis, J.F., Frank, L.D., Saelens, B.E., & Kraft, M.K. (2004). Active Transportation and Physical Activity. Transportatio n and Physical Activity, 38(4), 249 268. Stokes, R.J., MacDonald, J., & Ridgeway, G. (2008). Estimating The Effects Of Light Rail Transit On Health Care Costs. Health & Place, 14(1), 45 58. Sztabinski, F., (2009). Bike Lanes, On Street Parking and Business A Study of Bloor ( www.cleanairpartnership.org ); at www.cleanairpartnership.org/pdf/bike lanes parking.pdf. Tomalty, R., & Haider, M., (2009). Walkability and Health; BC Sprawl Report 2009. Smart Growth BC ( www.smartgrowth.bc.ca ); at www.smartgrowth.bc.ca/Portals/0/Downloads/sgbc sprawlreport 2009.pdf. VTPI (2008). Evaluating Nonmotorized Transportation. Online TDM Encyclopedia, ( www.vtpi.org ). Weissman, S., & Corbett, J. (1992). Land Use St rategies for More Livable Places. Local Government Commission, ( www.lgc.org ). Yeh, G.A. (2007). The development and applications of geographic information systems for urban and regional planning in the developing countries. International Journal of Geograp hical Information Systems, 5(1), 5 27. Zwick, P., & Carr, M.H. (2007). Smart Land Use Analysis: The LUCIS Model: Land Use Conflict Identification Strategy. Journal of American Planning Association, 75(1).

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113 BIOGRAPHICAL SKETCH Yas h Nagal completed his Bach elor in Civil Engineering in 2015 from Oriental Institute of Science and Technology. From there after gaining work experience for 7 months he started school at University of Florida for Master of Urban and Regional Planning He is e xpected to graduate in December 2017. Following his interdisciplinary education background he specializes in Tran sportation Planning with a goal to bridge the gap between the fie lds of Planning and Engineering