Effect of Choice Set Composition on Route Choice Models

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Effect of Choice Set Composition on Route Choice Models
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english
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Geda, Avinash
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University of Florida
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Degree:
Master's ( M.S.)
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University of Florida
Degree Disciplines:
Civil Engineering, Civil and Coastal Engineering
Committee Chair:
SRINIVASAN,SIVARAMAKRISHNAN
Committee Co-Chair:
YIN,YAFENG
Committee Members:
RAJAPAKSHE,THARANGA
LAWPHONGPANICH,SIRIPHONG

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Subjects / Keywords:
choiceset -- choicesetcomposition -- discretechoicemodels -- routechoicemodels
Civil and Coastal Engineering -- Dissertations, Academic -- UF
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Civil Engineering thesis, M.S.
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theses   ( marcgt )
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Electronic Thesis or Dissertation

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Abstract:
Route choice models are the basis for the link level demand values. Unlike the traditional User Equilibrium and System Optimum models discrete level choice models provide a clear and better understanding of the demand forecasts for a future year. GPS based surveys have picked up lot of momentum in the recent past and the usage of the same data for the research helps lapse many limitations that are being caused due to the telephone based or other ways of route choice surveys. These GPS surveys give a clear picture of the actual chosen route. Although the data is available in a more accurate manner, it also takes in equally difficult amount of post processing and analysis of the GPS data to come up with proper understanding of the route choice. This study positions itself in the same context looking at the route choice as an alternative chosen from a set of alternatives that are potentially available for the traveler or decision maker.
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In the series University of Florida Digital Collections.
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Includes vita.
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by Avinash Geda.
Thesis:
Thesis (M.S.)--University of Florida, 2014.
Local:
Adviser: SRINIVASAN,SIVARAMAKRISHNAN.
Local:
Co-adviser: YIN,YAFENG.

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1 EFFECT OF CHOICE SET COMPOSITION ON ROUTE CHOICE MODELS By AVINASH GEDA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2014

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2 2014 Avinash Geda

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3 To m yself, m y family, n ear, d ear and all!

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4 ACKNOWLEDGMENTS First and foremost I would like to thank Dr. Siva Srinivasan for both the opportunity and for guidance throughout my m Thank you for giving me such an interesting and challenging project. Thanks also go to everyone who has helped with the technical aspects of my project, in particular to Drs. Tharanga, Toi and Yin. Your advice and guidance has been invaluable. I would also like to thank Mr. William Sampson, Director of the McTrans Center, for support ing me with the funding for my m Huge thanks go to my family and friends who have supported me throughou t e verything in life, not just my m Sindhusha and Ramya for sticking with me and making it easier for me to work on the project. You are all amazing and thank you for always being there for me! Special thanks go to my Mom, who always stood by me. She has taught me to keep my feet grounded while my head is held high, she has taught me to believe in myself and that I can ach ieve anything I set my mind to, so thank you Amma! Thank you for supporting me thr oughout my life. Last but definitely not the least, I thank myself for sitting hours together on the chair of room number 511 with the hope that this project would one day help me earn my m degree. It paid off!

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ........................... 6 LIST OF FIGURES ................................ ................................ ................................ ......................... 8 ABSTRACT ................................ ................................ ................................ ................................ ..... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 10 2 LITERATURE REVIEW ................................ ................................ ................................ ....... 12 3 DATA ................................ ................................ ................................ ................................ ..... 21 4 ROUTE CHOICE MODELS ................................ ................................ ................................ .. 31 Path Size Logit Methodology ................................ ................................ ................................ 31 Short Trips ................................ ................................ ................................ .............................. 34 Long Trips ................................ ................................ ................................ .............................. 44 5 CONCLUSIONS AND DISCUSSION ................................ ................................ .................. 49 APPENDIX: PERCENT OVERLAP OF n th ROUTE WITH CHOSEN ROUTE ........................ 51 LIST OF REFERENCES ................................ ................................ ................................ ............... 53 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ......... 55

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6 LIST OF TABLES Table page 2 1 Overview of Choice Set Generation studies ................................ ................................ ...... 13 3 1 ................................ ................................ ............................. 23 3 2 Percentage distributions of the 2742 trips for various Choice Set size levels for short, ................................ ......................... 25 3 3 Percentage distributions of the 2742 trips for various Choice Set size levels for short, long and total trips for Commonly Factor value ................................ .................... 26 3 4 Percentage distributions of the 2742 trips for various Choice Set size levels for short, long and total trips for ................................ .................... 27 3 5 Feasible choice set sizes with good amount of data availability ................................ ....... 28 3 6 Number of trips available with different choice set sizes for various CF levels. .............. 28 3 7 Percent and number of trips with at least one choice having at least 95% overlap with chosen route ................................ ................................ ................................ ....................... 28 4 1 Definitions of Route Attributes ................................ ................................ .......................... 32 4 2 Comparison of the average of each of the explanatory variables with that of chosen route for short trips ................................ ................................ ................................ ............. 34 4 3 Average of std. deviation of different variables across th e alternatives across the unique OD pairs considered for short trips ................................ ................................ ........ 36 4 4 Std. deviation of std. deviation of different variable s across the alternatives across the unique OD pairs considered for short trips ................................ ................................ .. 37 4 5 Model estimation results for short trips ................................ ................................ ............. 38 4 6 Average expected overlap when cross application is performed for short trips ................ 41 4 7 Std. deviation in average expected overlap when cross application is performed for short trips ................................ ................................ ................................ ........................... 41 4 8 Average probability of outperforming the shortest path for short trips ............................. 43 4 9 Std. deviations in probabilities of outperforming the shortest path across the trips when cross application is performed for short trips ................................ ........................... 43 4 10 Comparison of the average of each of the explanatory variables with that of chosen route for long trips ................................ ................................ ................................ ............. 45

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7 4 11 Average of std. deviation of different variables across the alternatives across the unique OD pairs considered for long trips ................................ ................................ ......... 46 4 12 Std. deviation of std. deviation of different variables across the alternatives across the unique OD pairs considered for long trips. ................................ ................................ .. 46 4 13 Model estimation results for long trips ................................ ................................ .............. 47 4 14 Percentage of trips with at max one trip in choice set having the controversial variable for that trip greater than that of chosen route ................................ ....................... 48

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8 LIST OF FIGURES Figure page 3 1 Percent Time of day trip distribution ................................ ................................ ................. 22 3 2 Percent Day of week trip distribution ................................ ................................ ................ 22 3 3 Trip Purpose distribution ................................ ................................ ................................ ... 23 3 4 Average overlap of n th route with chosen route across the various CS and CF le vels. ..... 29 3 5 5 th percentile of overlap of n th route with chosen route across the various CS and CF levels. ................................ ................................ ................................ ................................ 29 3 6 95 th percentile of overlap of n th route with chosen route across the various CS and CF levels. ................................ ................................ ................................ ................................ 30

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9 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 Science EFFECT OF CHOICE SET COMPOSITION ON ROUTE CHOICE MODELS By Avinash Geda May 201 4 Chair: Sivaramakrishnan Srinivasan Major: Civil Engineering Route choice models are the basis for the link level demand values. Unlike the traditional User Equilibrium and System Optimum models discrete level choice models provide a clear and better understanding of the demand forecasts for a future year. GPS based surveys have picked up lot of momentum in the recent past and the usage of the same data for the research helps lapse many limitations that are being caused due to the telephone based or other ways of route choice surveys. These GPS surveys give a clear picture of the actual chosen route. Although the data is available in a more accurate manner, it also takes in eq ually difficult amount of post processing and analysis of the GPS data to come up with proper understanding of the route choice. This study positions itself in the same context looking at the route choice as an alternative chosen from a set of alternatives that are potentially available for the traveler or decision maker.

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10 CHAPTER 1 INTRODUCTION The state of the practice approach to modeling route choice assumes that travelers choose the path of lowest generalized c ost, with the generalized cost generally being a function of travel time and tolls. This substantive impact of travel times and costs on route choice decisions is also supported by surveys of stated traveler preferences (Wu, 2012) and the several empirical modeling exercises (Dhakar, 2012). At the same time, empirical models have also documented the strong impact of other route attributes such as the number of turns, facility type/speed, and number of intersections. While many of the earlier empirical stud ies have been based on small samples of routes and travelers, the emergence of GPS based surveys in the last decade and the availability of fine grained GIS based network data now provide us with substantial opportunity to develop multivariate route choice models to study the marginal impacts of various attributes on route choice decisions. At the same time, these developments also introduce new challenges. One such challenge (which is also the focus of this study) is the identification of the appropriate c hoice set for developing route choice models. The GPS based surveys only help us to identify the route chosen by the traveler; these surveys do not elicit information on other alternative routes considered for making the same trip. The availability of GIS based roadway networks and the growing number of choice set g eneration algorithms (Bekhor et al 2006) allow us to construct several possible alternative routes. However, such processes do result in the generation of a very large number of route alternat ives (subject to constraints on computational time and the granularity of the network). While a large number of alternatives may be advantageous in that they allow us to potentially consider the effects of a variety of attributes, they also impose computat ional constraints on model estimation and application and could reduce the overall predictive power of the model to the extent that irrelevant alternatives are included in the choice set. Therefore, empirical evidence is required to guide the selection of choice sets for route choice modeling. Our review of the literature indicates that there is much variability in the size and composition of alternatives in the choice sets used in past modeling exercises. Systematic comparisons of models estimated with va rying choice sets appear minimal. In the light of the above discussions, the intent of this study is to examine the effects of choice set size and composition on route choice models. Data from a large scale GPS survey

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11 along with a fine grained road networ k are used to build several models. A choice set generation algorithm (the breadth first search link elimination procedure) shown in the literature to be efficient for large scale problems is used and the path size logit is used as the econometric structur e of the route choice models (again, shown to be an effective approach in the literature). Multiple choice sets are constructed which vary in size (number of alternatives) and composition (the extent of similarity among the different routes in the choice set). Further, the analyses are undertaken separately for short and long routes recognizing that is theoretically possible to generate more alternative routes for a longer distance trip. The rest of this thesis is organized as follows. Chapter 2 presents a synthesis of the literature. Chapter 3 presents an extensive discussion of the data using several descriptive analysis. Chapter 4 presents the modeling methodology, estimations and the results associated with different compositions of choice sets for sh ort and long trips. Chapter 5 looks into the conclusions of the study and discussion about the future directions.

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12 CHAPTER 2 LITERATURE REVIEW The process of developing route choice models from GPS data comprises three steps (Dhakar 2012): (1) map matching, (2) choice set generation, and, (3) model estimation considering similarities among the different alternatives in the choice set. The focus of this study is on the second step of choice set generation and as such the review of th e literature pres ented in this C hapter focuses on this aspect. In particular, we opine that two characteristic features of the choice sets, namely, the size of the choice set (number of alternatives) and the variability (or similarity) across the different alternatives in the choice set are important aspects that could impact the computational requirements for model estimation/application and the predictive accuracy of the models. The reader is referred to Dhakar (2012) for an extensive discussion of map matching procedure s and to Smits et al (2014) and Dhakar (2012) for discussions about alternate econometric methods for capturing correlations among the alternatives. Table 2 1 presents a summary of various route choice modeling studies from the stand point of choice set composition. The studies are grouped in the order such that studies dealing with small scale and non GPS data in the beginning and with those GPS related trip data and large sample sizes are towards the end. A universal choice set is a choice set which includes all the possible routes between a given OD pair. This universal choice set would contain a very large number of routes and cannot be defined practically. So, a subset of this universal choice set is considered in any of the stu dies related to choice set. It should also be noted that it is also not possible to produce that set of choices what a traveler exactly perceives because of the behavioral limitations and survey to that st has to somehow come up with a set of those routes which are relevant to chosen route. An algorithm is needed to come up with all the relevant routes for a chosen route in a given network. A broad classification of choice set generation algorithms fall i nto shortest path, constrained enumeration and probabilistic methods. An exhaustive set of choice set generation algorithms available in the various broad classification are Biased Random Walk, Link Elimination, Link Penalty, Branch and Bound, Simulation, Labeling and Stochastic approaches. Many studies also use a combination of these choice set approaches to come up with unique choice set which contains route choices that are relevant to the chosen route (Bekhor et al 2006 and Prato and Bekhor 2007)

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13 Tab le 2 1. Overview of Choice Set Generation studie s Study Data Choice Set Sizes Choice Set Generation Algorithms Frejinger et al. (2009) Synthetic dataset Average choice set size 9.66 Biased random walk Quattrone and Vieteatta, 2011 Road side survey (280 chosen routes) of trucks compared with on board GPS (52 routes) trips 30 k shortest path Combination of 5 criteria Bierlaire and Frejinger (2008) No real GPS data Reported trip dataset collected in Switzerland 780 observations 45 Stochastic Choice Set Generation Bekhor et al. (2006) No GPS data Questionnaire survey of faculty and staff at MIT, Boston Home to work 159 observations with choice sets consisting more than 1 routes Max 51, median 30 and 25% have at least 40 Lab eling and Simulation (48 draws) Bovy et al. (2008) Two datasets from different regions, Turin (182 OD pairs) and Boston (91 OD pairs) Total of 188 routes for Boston and 236 for Turin. Branch and Bound Prato and Bekhor (2006) No GPS data Web based survey in Turin, Italy Home to work 236 chosen routes 339 possible alternatives 182 different ODs Max 55, median 32 and 25% have at least 40 Branch and Bound Prato and Bekhor (2007) No GPS data Web based survey in Turin, Italy Home to work 216 observations with at least 5 alternatives except the chosen route Branch & Bound: max 44, median 17,6.36% at least 40 Merged: max 55, median 32,31.36% at least 40 Link Elimination, Link Penalty, Simulation and Branch and Bound Bekhor and Prato (2009) No GPS data Turin and Boston datasets Total of 188 routes for Boston and 236 for Turin. Branch and Bound

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14 Table 2 1. Continued Study Data Choice Set Sizes Choice Set Generation Algorithms Spissu et al. 2011 GPS data from smart phones All purpose 393 observed routes Two leg survey of 1 week each 11 (one chosen route) Simulation using Min cost algorithm through existing Cagliari model with cost function of time and distance Papinski and Scott, 2011b GPS data Home based Work Trips In all 237 trips 9 (but only 52 out of 237 could only produce) k shortest path algorithm Frejinger and Bierlaire (2007) Borlange GPS data 2978 observations 2244 unique observed routes 2179 OD pairs Min 2, max 43, 93% less than15 Link Elimination Schussler and Axhausen (2010) GPS data collected in Zurich with an on person GPS logger 1500 observations 20,60,100 Breadth First Search Link Elimination(BFSLE) and SCSG (Stochastic Choice Set Generation) Dhakar (2012 ) GPS data from Chicago for 2880 trips 5,10,15 Breadth First Search Link Elimination Hood, Sall and Charlton (2011) GPS data from San Francisco for 2777 bicycle trips Doubly stochastic: avg. 51, Link Elimination: 96 Doubly stochastic: combination of stochastic and labeling, and Link Elimination

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15 It should be understood that if the choice set generation approach is labeling, the size of choice set for all the OD pairs would be definite and constant, unless until there are any duplicate routes. It is the case with the other approaches like Link Elim ination or Link Penalty etc. which introduces the irregularity in the size of choice set. Once the choice set generation is done then comes the step with the sampling of the choices in the choice set. While the studies dealing with smaller scale data can actually consider the exhaustive level of choices generated, for data which has large data it becomes computationally intense and also there is no evidence why not to make the size of choice set same across all the OD pairs used for model estimation. Freji nger 2009, Bekhor et al 2006, Prato and Bekhor 2006, Prato and Bekhor 2007 used different approaches in comparison and combination to come up with choice sets for different OD pairs, they ended up building models with variable choice set size across the OD pairs considered for model estimation. Details about the choice set sizes and variations for the studies mentione d are available in T able 2 1. While the large scale implementation of variable choice set size across the OD pairs used for estimation model is evidently available only with the study Frejinger and Bierlaire (2007) for the Borlange data set, most of the trips have choices generated are less than 15 (reported 93%), which can be a serious issue with respect to the limitations of the size of the network which might enable the traveler to have more than 15 alternatives. The large scale implementation with constant choice set size across the OD pairs for the model estimation is done by Schussler and Axhausen 2010. They evaluated the models for diffe rent choice set sizes of 20, 60 and 100. While 60 and 100 might sound too unrealistic to say

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16 that a traveler considers those many alternatives, they also failed to look at the similarities between the different choices in the choice set. Another large scal e implementation study which falls in line with Schussler and Axhausen 2010 is Dhakar 2012. While the choice set sizes remain constant across the OD pairs for model estimation and the sizes considered for different models being 5, 10 and 15, this study als o does not really explain the sampling of these choices based on the variation among them. One more study which comes into the category of the large scale implementation is the study by Hood et al 2011 on the bicycle route choice. Although this study ha s its differences in terms of road networks, this study looks at the insights for bicycle network design. They looked at 2777 bike trips from 366 users. There were two types of choice set generation approaches employed for this of which the first one is th e doubly stochastic method, which combines the stochastic and labelling approaches, and the reported average choice set size using this approach and the re ported choice set size is 96 for each OD pair. We believe is there is no evidence reported in the study which looks into account for the composition of the choice set. Sampling is another impediment that occurs after the step of choice set generation. Ben Akiva 2009 addressed this using a sampling correction factor in the Path Size Logit model where they use a factor for correction of utilities which are considered for calculation of probabilities. While this accounts for the choice set generated to the cho ice set selection step, there is a need a for an approach to come up with the selection of alternative set for modeling from the choice set generated. As mos t of the studies listed in the T able deal with the exhaustive set of choices generated into the sel ection of the choices for model estimations, Schussler and Axhausen 2012 addressed this in a very detailed classified manner suggesting four different types of alternatives

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17 selections for model estimations. They are Random reduction, Similarity distributio n based reduction, similarity based reduction and rule based reduction. While random reduction is a simplistic selection of choice set, similarity distribution based and similarity based look into the overlaps between the choices in the choice set with res pect to the Path Size factor calculated as suggested from Ben Akiva and Bierlaire 1999, and the rule based reduction, removes the routes from the choice set which violate defined thresholds such as length, travel time etc. Implementation of all four reduct ion types are available in detailed in Schussler and Axhausen 2010. Dhakar 2012 employed BFSLE algorithm for choice set generation and employed a simple s ize of the choice set. A simple network level sampling is studied by Flotterod, et al ., 2011 which essentially deals with the sampling of alternatives given a network, origin and destination. Although the algorithm presented has a clear approach for the sampling point, this study can be irrelevant in terms of concentration of the current study as it looks into the sampling after the choices being generated. Once the selection is done, the most important step would be the preparation of the choice set for model estimation. It should be made sure at this step that the chosen route is included in the choice set. While those studies with no GPS data would add a chosen route estimated from survey based, GPS based data provide much realistic chosen route inclus ion in the choice set. Once the choice set is prepared, the models are to be estimated. It starts with a simple Multinomial Logit Model (MNL) which looks into the utility maximization of the choices involved. This looks at the deterministic and error in th e utilities to be determined.

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18 Enhancements to the simple MNL model can be like Cross Nested Logit (CNL) models etc. But there advanced sophistications to the simple MNL models with the introduction of correction factors in the deterministic component of th e Utility term. A detailed discussion on different model structures can be found in Dhakar 2012. Prato et al ., algorithms in choice set generation and also the various model struct ures for the model building. Although, there is no validation with respect to the real time data, the study critiques the various difficulties involved in different steps of the model building. A discussion on the effect of different sizes of choice set i s reported by Bliemer et al ., 2008 for a single OD pair and synthetic data for choice set sizes of 6, 10 and 12. They researched on the effects of the different sizes across the different types of models and concluded that most of them do not have robustn ess of choice predictions at individual level. Recent study by Vreeswijk, et al 2014, look into the route choice perception of the travelers in the Dutch city of Enschede and found some interesting results of the perception in shortest path travel time a nd perceived travel time on the other choices. They reported that perception by the travelers is overestimated in both the cases. The study by Prato et al ., 2007 accounts for different choice set generation algorithms and different choice sets. They esti mated models across the different model structures and observed that non nested models perform better across all the various choice set compositions. The stress of this study is more on the model structure and did not really look into the aspects of homoge nous size across different OD pairs and the overlap threshold with the actual route was imposed with overlap measure. As such, this is the closest study to our research but the objective was different in the first place.

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19 Literature Review Summary : The cru x of this study concentrates on the choice set composition, which can be deduced only after a choice set is generated. Ben Akiva, Ramming, Schussler have tested all the above listed approaches for choice set generations. Schussler and Axhausen (2010) and D hakar (2012) reported that link elimination is by far the best possible approach and hence the same is adopted for this study and a Breadth First Search Link Elimination (BFSLE) is employed to generate a choice sets for this study. Moving forward, coming t o the point of choice set composition, although there are variations with respect to the choice set size in various studies and similarity based reduction w.r.t path size factor are employed in Schusller and Axhausen ( 2010), as of our knowledge the uniquen ess of the route in terms of distance overlap in the choice set is not exclusively available as a criterion of choice selection. This can be brought up with a commonly factor that relates to how one route is different from another. The commonly factor (CF) calculates the overlap between two routes in terms of distance and is calculated as Where L ij is the common distance of route and route ; L i is the distance of route i and L j is the distance of route j Based on the restriction of t his factor to various levels we can achieve more varied choices in a choice set. The size of the choice set which is depending on the number of choices in the choice set is also an important consideration before we decide on building the route choice model s. These various sizes are researched by Schussler and Axhausen (2010), Dhakar (2012) and many others but the combination of this choice set size with respect to the variability between choices in the choice set lays base for this particular study. The nex t step in the sequence after deciding on the choice set composition would be the model structure that should be adopted to build the discrete route choice model. A simplest approach for this purpose would be the traditional MNL model which depends on the u tility

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20 maximization. CNL and PSL are a bit advanced to the traditional MNL models. Due to the computational ease and flexibility for calculating the choice probabilities a Path Size Logit (PSL) model is considered for the study where we calculate the Path Size factor for every choice in the choice set which will be include in the Utility functions which determine the utility which in turn is used in the calculation of choice probabilities. On a whole this study looks the choice set composition from the both the estimation and application perspective. By application, it means that the estimation of models is done on choice sets of different compositions and thus built models are applied onto different choice set compositions so as to observe how they compare in terms of various metrics and certain conclusions drawn from these comparisons are presented.

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21 CHAPTER 3 DATA The two major components of data used in this study are the GPS streams and the GIS based roadway network characteristics. The GPS data come from the in vehicle GPS survey component of the Chicago Regional Household Travel Inventory (CRHTI) conducted, between January 2007 and February 2008. The raw data comprises of 6,089,852 GPS points from 9941 trips made by 408 HH vehicles (259 HHs).The road way network for the study area was obtained from ArcGIS Data and Maps from ESRI. Data Exploration : The analysis sample used in this study comprises 2742 trips between 2742 unique origin destination pairs. These trips are at least 2 miles and 5 minutes lon g, and had matching algorithm. The reader is referred to Dhakar (2012) for an extensive discussion of the map matching and other data generation procedures. Figure 3 1 represents the percentage distributi on of 2742 trips during different times of day. Early morning trips are those which have the mid point time of the trip before 7AM, AM peak trips are those with have the mid point time between 7AM 9AM, AM off peak trips are the remaining trips which have t he mid point time between 9AM and noon. PM off peak trips are those with mid point time between noon and 4PM, PM peak trips are those with mid point time between 4PM 6PM, and evening trips are the remaining trips with mid point time later than 6PM and befo re midnight. Figure 3 2 shows the day of the week variation. Figure 3 3 identifies the trip purpose. Specifically, 22.6% of the trips are home based originating at home, 28.12% of the trips are home ), and 40% are non home based trips (neither end of the trip is home). It was not possible to classify the trip purpose for a few trips since the home location of the traveler was not definitely known and so the purpose of

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22 have a large variety and are unlike those considered in several other studies which are often focused on specific purpose (such as commute). Figure 3 1. Percent Time of day trip distributio n Figure 3 2. Percent Day of week trip distribution 4.63 9.88 17.51 28.96 16.96 22.06 0 5 10 15 20 25 30 35 40 45 50 Early Morning AM Peak AM Off-peak PM Off-Peak PM Peak Evening 21.52 14.81 12.69 13.49 12.4 12.91 12.18 0 5 10 15 20 25 30 35 40 45 50 Mon Tue Wed Thu Fri Sat Sun

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23 Figure 3 3. Trip Purpose distribution The following T able summarizes several characteristics of the chosen route that were generated from by mapping the GPS traces to the GIS roadway network (the reader is referred to Dhakar 2012 for a discussion of the generation of these attributes). Table 3 1. Attribute Mean Std. Dev. 5 th percentile Max Total Distance (miles) 9.39 8.55 2.41 57.12 Total Time (minutes) 15.61 12.45 4.44 83.30 Intersection Count 20.73 16.29 4.00 136.00 Longest Leg Distance (miles) 3.83 6.22 0.44 48.52 Longest Leg Time (minutes) 5.64 7.66 0.81 58.60 Total Turns 3.16 2.36 0.00 22.00 Left Turns 1.48 1.33 0.00 10.00 Right Turns 1.54 1.40 0.00 12.00 Sharp Left Turns 0.09 0.30 0.00 3.00 Sharp Right Turns 0.05 0.23 0.00 2.00 Expressway Distance (miles) 1.10 4.19 0.00 40.43 Expressway Time (minutes) 1.23 4.70 0.00 45.99 22.76 28.12 40.23 8.9 0 5 10 15 20 25 30 35 40 45 50 HB-Origin HB-Destination NHB unknown

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24 Table 3 1. Continued Mean Std. Dev. 5 th percentile Max Longest Expressway Distance Leg (miles) 1.08 4.13 0.00 40.43 Longest Expressway Time Leg (minutes) 1.21 4.63 0.00 45.99 Arterial Distance (miles) 4.17 5.91 0.00 37.94 Arterial Time (minutes) 5.96 8.15 0.00 57.16 Longest Arterial Distance Leg (miles) 3.45 5.08 0.00 35.02 Longest Arterial Time Leg (minutes) 4.88 6.91 0.00 49.04 Local Road Distance (miles) 4.12 3.48 0.38 26.65 Local Road Time (minutes) 8.42 7.01 1.00 49.44 Longest Local Road Distance Leg (miles) 1.65 2.55 0.00 18.35 Longest Local Road Time Leg (minutes) 3.36 5.05 0.00 37.13 Max Speed (mph) 42.88 7.88 35.00 55.00 Mean Speed (mph) 34.84 6.14 25.96 53.67 Next, alternate routes are added to the choice set of each trip. Starting with the shortest free flow travel time path, alternatives are generated using the BFSLE algorithm (Dhakar 2012 for details). At first, all new routes generated were added into the choice set irrespective of the extent of overlap of the newly generated alternative with any of the previously generated alternatives (in other words, th e maximum permissible commonly factor of the new route with any of the previous routes is 1). The algorithm was run for a maximum of 2 hours per trip or 100 choices limit. The following T able 3 2 summarizes the number of alternatives generated. The reader will note that the results are presented separately for short (2 7 miles) and long (7+ miles) trips. The motivation for this is twofold. First, one might expect that shorter trips to have inherently fewer alternate route options than longer trips. Second, the shortest path trips than longer trips. To be sure, of the 1437 short trips in the sample, 366

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25 trips (25.47%) have the shortest free flow travel time path overlapping with the chosen route for at least 95% w hile 160 of the 1305 (12.2%) long trips have the shortest free flow travel time path overlapping with the chosen route for at least 95%. As the choice set generation algorithm starts with the shortest time path and generates options, perhaps a greater numb er of alternatives are we acknowledge that the choice of cut off distance for short and long trips is rather arbitrary, but with the 7 mile limit, we find th at 52.4% of all trips are short and 47.6% of all trips are long giving us a reasonable sample size for modeling each case separately. Table 3 2.Percentage distributions of the 2742 trips for various Choice Set size levels for short, long and total trips f Number of Alternatives generated Short distance trips (2 7 miles) Long Distance trips (7+ miles) Overall trips Number of cases Percent number of cases Number of cases Percent number of cases Number of cases Percent number of cases At least 5 1431 99.6 1302 99.8 2733 99.7 At least 10 1401 97.5 1285 98.5 2686 98 At least 15 1338 93.1 1245 95.4 2583 94.2 At least 20 1165 81.1 954 73.1 2119 77.3 At least 25 1056 73.5 880 67.4 1936 70.6 At least 30 911 63.4 795 60.9 1706 62.2 At least 35 773 53.8 694 53.2 1467 53.5 At least 40 639 44.5 611 46.8 1250 45.6 At least 45 529 36.8 545 41.8 1074 39.2 At least 50 118 8.2 485 37.2 603 22 The reader will note that practically all trips (98%) had at least 10 routes generated and about 22% of all trips had 50 or more routes generated. Further, 37% of the longer trips had 50

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26 or more routes compared to 8% of the shorter trips. Clearly, the gene ration of more alternatives for longer trips compared to shorter trips is quite reasonable. As already indicated the first approach to generating alternatives did not consider the extent of overlap of a new alternative with any of the previous alternative s. Therefore, in the next step, we re created the choice sets, but in this case, a new alternative was added to the choice set only if it did not overlap more than 95% with any of the previous alternatives already in the choice set (i.e., the maximum permi ssible commonly factor of the new route with any of the previous routes is 0.95). The following T able 3 3 summarizes the number of alternatives generated. Table 3 3. Percentage distributions of the 2742 trips for various Choice Set size levels for short, lo Number of Alternatives generated Short distance trips (2 7 miles) Long Distance trips (7+ miles) Overall trips Number of cases Percent number of cases Number of cases Percent number of cases Number of cases Percent number of cases At least 5 1415 98.5 1036 79.4 2451 89.2 At least 10 1319 91.8 442 33.9 1761 64.1 At least 15 1132 78.8 159 12.2 1291 47 At least 20 865 60.2 52 4 917 33.4 At least 25 673 46.8 17 1.3 690 25.1 At least 30 481 33.5 3 0.2 484 17.6 At least 35 343 23.9 0 0 343 12.5 At least 40 211 14.7 0 0 211 7.7 At least 45 108 7.5 0 0 108 3.9 At least 50 0 0 0 0 0 0 The reader will note the drastic reduction in the number of alternatives generated. Specifically, only 65% of the trips have 10 or more alternatives (it was 98% when no overlap

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27 constraints were applied) and practically no trip has more than 50 alternatives Further, the reduction in the number of alternatives is more dramatic for longer distance trips. Clearly, while the BFSLE quickly generates lots of alternatives for the long distance trips, many of them are fairly similar to some other routes in the choi ce set. In a third choice set generation step, the maximum permissible commonly factor of the new route with any of the previous routes was set at 0.90. The following T able 3 4 summarizes the number of alternatives generated. There is a further reduction i n the number of options (especially in the case of long distance trips). Table 3 4 Percentage distributions of the 2742 trips for various Choice Set size levels for short, Number of Alternatives genera ted Short distance trips (2 7 miles) Long Distance trips (7+ miles) Overall trips Number of cases Percent number of cases Number of cases Percent number of cases Number of cases Percent number of cases At least 5 1237 86.1 637 48.8 1874 68.3 At least 10 740 51.5 94 7.2 834 30.4 At least 15 341 23.7 8 0.6 349 12.7 At least 20 142 9.9 0 0 142 5.2 At least 25 60 4.2 0 0 60 2.2 At least 30 22 1.5 0 0 22 0.8 At least 35 9 0.6 0 0 9 0.3 At least 40 0 0 0 0 0 0 At least 45 0 0 0 0 0 0 At least 50 0 0 0 0 0 0 In consideration of the above results, this study will examine the following combinations with reasonable estimation sample size.

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28 Table 3 5. Feasible choice set sizes with good amount of data availability Max Allowed Commonly Factor among alternatives Number of alternatives in the choice set Short Distance Long Distance 1 5,10,15 5,10 0.95 5,10 5 0.90 5 NA Table 3 6 represents the number of trips available after the initial choice set for CF=1 is subjected to various samplings of choice set sizes and CF value levels. Table 3 6.Number of trips available with different choice set sizes for various CF levels. CF Short Long CS5 CS10 CS15 CS5 CS10 1 1434 1412 1355 1304 1287 0.95 1423 1347 NA 1134 NA 0.9 1334 NA NA NA NA In the following T able 3 7, the maximum overlap of the choices in the choice set which will determine the inclusion of chosen route in the choice set is discussed about. T he percentage presented in the T able indicates to what extent of that trips proportion for that particular case has a maximum overlap of at least 95% with the chosen route. Table 3 7. Percent and number of trips with at least one choice having at least 95% overlap with chosen route CF Short Long CS5 CS10 CS15 CS5 CS10 1 521(36.3%) 583(41.3%) 626(46.2%) 229(17.6%) 261(20.3%) 0.95 528(37.1%) 590(43.8%) NA 245(21.6%) NA 0.9 527(39.5%) NA NA NA NA

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29 Figure 3 4. Average overlap of n th route with chosen route across the various CS and CF levels. Figure 3 5. 5 th percentile of overlap of n th route with chosen route across the various CS and CF levels. 0 10 20 30 40 50 60 70 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 percent overlap with chosen route Average CS15_CF1_short CS10_CF1_long CS10_CF95_short CS5_CF95_long CS5_CF90_short 0 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 percent overlap with chosen route 5th %le CS15_CF1_short CS10_CF1_long CS10_CF95_short CS5_CF95_long CS5_CF90_short

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30 Figure 3 6. 95 th percentile of overlap of n th route with chosen route across the various CS and CF levels. The above plots show the average, 5 th percentile and 95 th percentile, percent overlap of n th route of the choice set with the chosen route respectively. It is evaluated for the extreme cases of each of the long and short trip classifications. For example CS5_CF90_short from the ledger indicates that the plot is for the choice set of 5 route s obtained at the CF level of 0.9 for short trips. 86 88 90 92 94 96 98 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 percent overlap with chosen route 95th %le CS15_CF1_short CS10_CF1_long CS10_CF95_short CS5_CF95_long CS5_CF90_short

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31 CHAPTER 4 ROUTE CHOICE MODELS This C hapter opens with the Path Size Logit Methodology adopted for this study and then talks about definitions of various explanatory variables involved in the model estimation and then looks into the cases of short a nd long trips. The rest of the C hapter is organized as model estimations and appl ications for short trips and model estimations for the long trips. Path Size Logit Methodology Path Size Logit (PSL) is the modeling methodology adopted for this study. Methodology first proposed by Ben Akiva and Bierlaire 1999, it has been proven to show good empirical performance. The model takes into account for the similarity between the choices in the form of a factor called Path Size (PS) factor within the deterministic component of the utility term of the is calculated based on the formula as reported by Ben Akiva and Bierlaire 1999 as follows. (4 1) W here is the link It should be noticed that if a path does not share any links with any other routes, its PS factor value will be 1. Once the PS factor is calculated for all the rou tes in the choice set, the probabilities are calculated from the probability expression as follows:

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32 (4 2) Where and is the parameter for the Path Size to be estimated. Estimation of the models looks in two different classes of the trip lengths as mentioned earlier, short trips which have t he trip length between 2 and 7 miles and long trips for all the other trips with trip length greater than 7 miles. Various explanatory variables considered for the model estimations are defined in the T able 4 1 Table 4 1 Definitions of Route Attributes Attribute Name Definition Total Distance Total length of the route Total Time Total free flow travel time for traversing the route Left turns per minute Number of left turns made traversing the route per unit free flow travel time Right turns per minute Number of right turns made traversing the route per unit free flow travel time Intersection Count per minute Number of intersections along the route per unit free flow travel time Prop on expressways Proportion of free flow travel time spent on ex pressways Prop on arterials Proportion of free flow travel time spent on arterials Prop on local roads Proportion of free flow travel time spent on local roads Maximum speed Maximum speed attained during the trip (speed limit) Mean Speed Average speed during the trip Circuity Deviation in terms of total length from the straight line distance between Origin and Destination.

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33 A node is considered as an intersection if there are three or more segments meet on that node. Hence, the number of intersections is calculated by determining the number of nodes with three or more segments. A leg is defined as the stretch of the route be tween two intersections. Therefore, the longest leg by distance and time is calculated as the maximum leg distance and leg time respectively for a route. Number of turns in a route are determined by reading the directions output provided by the route solv er in ArcGIS. The directions window explicitly specifies the types of turn, if required, along a route. The output also distinguishes the turns in terms of sharp and normal turns. The text in the output is read to determine the number of turns. The roads in the network are classified into three categories: freeways, arterials, and local roads. The total distance and time on each road types is calculated and then the corresponding proportions are determined. The longest continuous travel (distance and time) made on each road type is also estimated. Two measures of speed are calculated for a route: average speed, and maximum speed. The average speed is calculated by taking the time weighted average of the posted speeds on the segments of a route. Circuity i s used as a measure of the route distance deviation from the network free straight line distance between the origin and destination. The straight line distance (SLD) is calculated using the Haversine formula of calculating distance between two points: SLD (miles) = ArcCos[Sin(lat1)*Sin(lat2) + Cos(lat1)*Cos(lat2)*Cos(long2 long1)]*R radius (3949.99 miles).

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34 The circuity is then calculated by taking the ratio of the route length with the straight line distance. The circuity is always greater than or equal to 1. Circuity = Route Length/SLD Short Trips This is the first classification of the two types of trip lengths considered. The choice sets considered for this case with reasonable estimation sample sizes are 5, 10 and 15 for max commonly factor value of 1, 5 and 10 for max commonly factor value of 0.95 and 5 for max commonly factor value of 0.90. The comparison holds good only when the models are estimated using common se t of OD pairs. So, the common set of OD pairs for all the choice set compositions considered came out to be 1249 unique OD pairs. A brief look at the descriptive of each of the explanatory variable associated with each of the route in the choice set is tab ula ted in T able 4 2 It should be noted that travel distance, free flow travel time, intersection count per minute of travel time, proportion time spent on arterials, proportion time spent on local roads, maximum speed, mean speed and circuity of the chose n route fall very close to that of the average of average of each of the attributes across the alternatives for the common OD pairs. But, the left turns and right turns per minute of the free flow travel time for the chosen route are on an average lesser t han the average across the alternatives of the OD pairs considered. Table 4 2 Comparison of the average of each of the explanatory variables with that of chosen route for short trips Chosen Route CS5 CF1 CS10 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 Travel Distance 3.59 3.61 3.66 3.7 3.63 3.69 3.68 Travel time 6.88 6.62 6.69 6.78 6.66 6.79 6.8 Left turns per minute 0.22 0.4 0.46 0.48 0.41 0.46 0.4 Right turns per minute 0.24 0.42 0.48 0.5 0.42 0.47 0.41

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35 Table 4 2 Continued Chosen Route CS5 CF1 CS10 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 Intersection count per min 2.06 2.23 2.25 2.25 2.23 2.24 2.21 Prop time on expressways 0.003 0.01 0.01 0.01 0.01 0.01 0.01 Prop time on arterials 0.26 0.3 0.3 0.29 0.29 .2891 .2814 Prop on local roads 0.74 0.69 0.69 0.69 0.7 .7007 .7088 Max Speed 39.05 39.85 39.98 40.06 39.87 39.99 39.90 Mean Speed 32.43 33.95 33.96 33.94 33.86 33.82 33.65 Circuity 1.43 1.41 1.43 1.45 1.42 1.44 1.44 Ln(Path Size) 0.82 0.97 1.42 1.69 0.93 1.35 0.81 The point of variance across different choice set sizes and commonly factor values is explained through the standard deviation of each of the explanatory variables across the different alternatives in the choice set for each of the compositions. The average a nd standard deviation statistics of the standard deviation of each of the explanatory variables across the alternatives in the c hoice set are tabulated in the T ables 4 3 and 4 4 respectively. Although the behavior of variance is not standard across all th e explanatory variable considered. The average std. deviation for the travel distance increases when more alternatives are added, keeping the commonly factor constant, indicating that different routes in terms of distance are being added. It also increases when size is kept constant and commonly factor is reduced, which reflects that the similarity of the routes in terms of distance is being varied. But for the case of travel time, when more alternatives are added for the same commonly factor the variance i ndicator of average standard deviation across the alternatives decrease, which essential tells that more similar routes in terms of travel time are being added, whereas the same increases when the choice set size is kept constant and the commonly factor is varied down, which is an indicator that routes which replace the existing routes are different from the existing similarities.

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36 While there is not much of variability w.r.t the left turns and right turns per minute of free flow travel time across the diffe rent compositions, intersection count per minute of free flow travel time do not really tend to change with the number of alternatives being added for a constant commonly factor value, but there is an increase in the average standard deviation across the c ommonly factor value change. Also while the proportion time spent on different road types follow the trend of left turns and right turns per minute, max speed and mean speed follow the trends of travel distance and travel time respectively. Also, Circuity follows the similar trend of travel distance, while the natural logarithm of path size factor has a different trend, which has the variance indicator decreases with increase in choice set for a constant commonly factor value, but it increases in the other case of commonly factor value for constant choice set size. Table 4 3 Average of std. deviation of different variables across the alternatives across the unique OD pairs considered for short trips CS5 CF1 CS10 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 Travel Distance 0.32 0.34 0.35 0.33 0.37 0.4 Travel time 0.75 0.73 0.73 0.77 0.77 0.86 Left turns per minute 0.19 0.18 0.18 0.19 0.18 0.19 Right turns per minute 0.19 0.18 0.18 0.19 0.18 0.19 Intersection count per min 0.35 0.35 0.35 0.37 0.37 0.42 Prop time on expressways 0.01 0.01 0.01 0.01 0.01 0.01 Prop time on arterials 0.11 0.1 0.1 0.11 0.11 0.13 Prop on local roads 0.11 0.1 0.1 0.11 0.11 0.13 Max speed 1.45 1.5 1.52 1.53 1.62 1.78 Mean speed 2.02 1.83 1.76 2.07 1.9 2.28 Circuity 0.14 0.15 0.15 0.15 0.16 0.17 Ln (Path Size) 0.32 0.45 0.5 0.31 0.43 0.3

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37 Table 4 4 Std. deviation of std. deviation of different variables across the alternatives across the unique OD pairs considered for short trips CS5 CF1 CS10 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 Travel Distance 0.35 0.34 0.33 0.36 0.35 0.38 Travel time 0.81 0.74 0.69 0.82 0.77 0.8 Left turns per minute 0.09 0.07 0.07 0.09 0.07 0.09 Right turns per minute 0.09 0.07 0.07 0.09 0.07 0.09 Intersection count per min 0.29 0.23 0.21 0.29 0.23 0.29 Prop time on expressways 0.02 0.02 0.02 0.02 0.02 0.02 Prop time on arterials 0.1 0.08 0.08 0.1 0.08 0.1 Prop on local roads 0.1 0.08 0.08 0.1 0.08 0.1 Max Speed 2.54 2.28 2.23 2.61 2.36 2.8 Mean Speed 1.61 1.21 1.08 1.6 1.2 1.55 Circuity 0.2 0.18 0.18 0.21 0.19 0.21 Ln(Path Size) 0.12 0.12 0.11 0.12 0.11 0.1 The model estimations for the choice set sizes of 5,10 and 15 for commonly factor value of 1, and sizes 5, 10 for maximum commonly factor value of 0.95 and size 5 for max commonly factor val ue of 0.90 are reported in the T able 4 5

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38 Table 4 5 Model estimation results for short trips Variable CS Size 5 For CF = 1 CS Size10 For CF = 1 CS Size 15 For CF = 1 CS Size 5 For CF = 0.95 CS Size10 For CF = 0.95 CS Size 5 For CF = 0.90 Est T stat Est T stat Est T stat Est T stat Est T stat Est T stat Total Time 0.14 2.51 0.14 3.12 0.16 3.52 0.09* 1.69 0.14 3.09 0.02* 0.23 Left Turns/min 6.15 19.75 7 24.85 7.11 27.09 6.3 20.38 7.15 25.45 6.2 20.77 Right Turns/min 6.39 20.06 7.01 25.2 7.14 27.69 6.29 20.01 6.97 25.19 6.14 20.36 Intersection count/min 0.65 6.27 0.54 5.94 0.47 5.43 0.61 6.21 0.55 6.24 0.56 6.02 Prop time on Local road 3.13 9.02 2.62 8.84 2.53 9.05 2.73 8.19 2.57 8.76 2.2 7.23 Circuity 0.42* 1.86 0.76 3.76 1.15 5.36 0.43* 1.91 0.72 3.5 0.56 2.28 Ln (Path Size) 0.62 4.17 0.28 3.3 0.3 4.19 0.31 2.09 0.07* 0.79 0.47 2.93 Log Likelihood (at convergence) 894.95 1276.82 1563.54 911.174 1293.29 1004.49 Log Likelihood (equal shares) 2010.19 2875.93 3382.35 2010.19 2875.93 2010.19 R squared 0.55 0.56 0.54 0.55 0.55 0.5 Notes: *indicates that the estimate is insignificant at 95% confidence level R squared = 1 {Log Likelihood (at Convergence)/Log Likelihood (equal shares)}

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39 As expected, free flow travel time is found to be negatively associated with the probability of choosing a route, which indicates that the routes with higher travel times are not favored, keeping everything else considered for the model estimation constant. But, it is also the case that the travel time is insignificant at 95% confidence level for choice set sizes of 5 where the max commonly factor value allowed is 0.95 or 0.9. The left turns and ri ght turns per minute of free flow travel time have a negative effect with the probability that a route from the set of alternatives can be chosen. While the trend shows that this effect gets stronger with increase in the choice set size for a constant max commonly factor value, it is not really much different across the different max commonly factor values without change in the choice set size. But, the number of intersections per minute of travel time has an intuitive effect in terms of probability of a ce rtain would be chosen, the behavior across the compositions is peculiar, suggesting that the effect goes milder with increase in choice set size for constant max commonly factor threshold and the similar trend when max commonly factor is controlled down an d choice set size is kept constant. Another intuitive result, which can be well supported by empirical evidence, is the network of Chicago, and there is very small v alue for the average proportion time spent on expressways, when compared to arterials in general also, route with more time spent on local roads is favored more between two or more similar routes which have everything intact except the proportion time spen t on local roads. And the effect of this variable is very much similar to that of intersection count per minute of free flow travel time in terms of variance in the magnitude across the different compositions of the choice set. Circuity is expected to have a negative effect on the probability of choosing a route, which is shown empirically, that more deviated a route is, from the straight line distance between the origin and destination, less chosen

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40 that route is. Finally, the variable natural logarithm of path size is associated with the probability of choosing a route in both negative and positive ways at different composition levels. The positive sign indicates that a route which is significantly different from the other routes which share more similarity in common. But, while controlling for the commonly factor for a maximum of 0.90, the effect is reversed. While there are studies which reported positive effects for ln of path size in choosing a route (for ex. Prato and Bekhor, 2006; Prato and Bekhor, 200 7; Bierlaire, and Frejinger, 2008), there can be chance for negative effects after controlling the similarities to an extent. On a whole, it can be stated that all the models estimated are reasonably intuitive for all the effects of each of the attributes of the routes. Cross Applications of Models and Comparisons : In order to better compare the models, or to determine which model performs better on which choice set compositions, brings the point of cross application of the models. While all the models estimated are deployed on to all the choice set compositions assembled, the results observed and discussed below paint a better picture to contrast between the models from the application standpoint also. Tables 4 6 4 7 paints a picture w.r.t to average expected overlap and standard deviation of the expected overlap respectively of the predicted route with the chosen route when the cross application of models estimated on the other choice set compositions is performed. Expected overlap is calculated by mu ltiplying the predicted probabilities for each of the routes in the choice set with the overlap of each of the routes with the chosen routes and then adding them together. The expected overlap is given as: (4 3)

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41 Where = performed, Table 4 6 Average expected overlap when cross application is performed for short trips Shortest Path CS5 CF1 CS10 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 CS5 CF1 61.88 56.87 57.94 58.26 57.32 58.26 58.35* CS10 CF1 61.88 54.41 56.21 56.69 55.31 56.82 57.27* CS15 CF1 61.88 53.15 55.37 55.94 54.28 56.11 56.68* CS5 CF95 61.88 55.94 57.14 57.53 56.42 57.5 57.56* CS10 CF95 61.88 53.68 55.57 56.08 54.59 56.21 56.66* CS5 CF90 61.88 54.59 55.95 56.48* 55.02 56.31 56.08 *indicates the highest value in the row excluding the shortest path. Table 4 7 Std. deviation in average expected overlap when cross application is performed for short trips CS5 CF1 CS10 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 CS5 CF1 30.14 30.63 30.75 30.23 30.74 30.53 CS10 CF1 27.89 28.78 28.97 28.17 29.08 29.13 CS15 CF1 26.6 27.83 28.09 27.06 28.26 28.51 CS5 CF95 29.6 30.15 30.31 29.69 30.27 29.97 CS10 CF95 27.1 28.07 28.29 27.38 28.36 28.34 CS5 CF90 27.54 28.27 28.49 27.63 28.42 27.94 Rows in Table 4 6 indicate when the particular model estimated from the one in the column is cross applied on to the one in the row. Reader should notice that the cross application is performed on choice sets that are generated as is, and no chosen route is included in thi s step.

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42 At an average level shortest path has a better overlap with chosen route than the expected overlap. But, as the purpose of this study determines the comparison across different models for choice set compositions, it can be observed that model estim ated with 5 alternatives and maximum commonly factor threshold of 0.90 yields better results in terms of average expected overlap when applied almost all the cases except when the on the same choice set composition it is estimated from. And, the best perfo rmance on the choice set size of 5 for maximum commonly factor value of 0.90 is when the model estimated using 15 alternatives for max commonly factor of 1 is applied. On a whole, the best of the best is when the model estimated using the choice set size 5 for maximum commonly factor value of 0.90 is applied onto the choice set with 5 alternatives for maximum commonly factor value of 1, which can result to a plausible meaning of putting in efforts for estimating the model in terms of controlling the commonl y factor and applying it on to simple choice sets generated with no commonly factor limitations. That is essentially same as the application part gets easier and time saving with respect to generation of choice set for a model with efforts in estimation. P robability of outperforming the shortest path in the choice set can be a good disaggregate level metric to look at the how better the model performs when compared to the shortest path. This metric is essentially the probability of path with an equal or bet ter overlap than the shortest path. It is calculates as follows: (4 4)

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43 Where, overlap between the shortest time path and the chosen route, and 0 oth erwise. Tables 4 8 and 4 9 list the average probability of outperforming the shortest path and standard deviation of the same when cross application is performed. Table 4 8 Average probability of outperforming the shortest path for short trips CS5 CF1 CS1 0 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 CS5 CF1 0.63 0.67 0.68* 0.65 0.68 0.68 CS10 CF1 0.52 0.57 0.58 0.54 0.59 0.6* CS15 CF1 0.47 0.52 0.54 0.49 0.54 0.55* CS5 CF95 0.62 0.67 0.68* 0.64 0.68 0.67 CS10 CF95 0.51 0.56 0.58 0.53 0.58 0.59* CS5 CF90 0.58 0.62 0.64* 0.59 0.63 0.63 *indicates the highest value in the row in comparison Table 4 9 Std. deviations in probabilities of outperforming the shortest path across the trips when cross application is performed for short trips CS5 CF1 CS10 CF1 CS15 CF1 CS5 CF95 CS10 CF95 CS5 CF90 CS5 CF1 0.33 0.32 0.31 0.32 0.31 0.3 CS10 CF1 0.34 0.33 0.33 0.33 0.33 0.31 CS15 CF1 0.34 0.33 0.33 0.33 0.33 0.31 CS5 CF95 0.33 0.32 0.31 0.32 0.31 0.3 CS10 CF95 0.34 0.33 0.33 0.33 0.33 0.31 CS5 CF90 0.34 0.33 0.33 0.33 0.33 0.32 The findings from the cross application of models in terms of outperforming the shortest path metric show that the model estimated using the highest number of alternatives for no control on commonly factor has the best performance when applied on the least choice set sizes for any

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44 commonly factor threshold level. Where as in the rest of the three cases of choice set sizes 10 for max commonly factor values of 1,0.95 and 15 for max commonly factor value of 1, the model estimated using 5 alternatives and the b est max commonly factor threshold case of 0.90 has the best performance. Best of best performance is for the case when model estimated using choice set size of 15 for max commonly factor threshold value of 1 is applied on to the choice set size 5 for max c ommonly factor value of 1. It should be noted that values listed in the T ables are rounded to the second decimal place and the explanations are based on absolute values. Long Trips This is the second classification of the two types of trip lengths conside red. The choice sets considered for this case with reasonable estimation sample sizes are 5 and 10 for max commonly factor value of 1 and 5 for max commonly factor value of 0.95. Just like in the case of short trips, the comparison holds good only when the models are estimated using common set of OD pairs. So, the common set of OD pairs for all the choice set compositions considered came out to be 1127 unique OD pairs. A brief look at the descriptive of each of the explanatory variable associated with each of the route in the choice set is tabulated in T able 4 10 It should be noted that travel distance, proportion time spent on expressways and maximum speed only, of the chosen route fall very close to that of the average of average of each of the attributes across the alternatives for the common OD pairs. But, the average free flow travel time and average circuity of the chosen route are significantly different from the average of the average of the alternatives across choice sets across the 1127 OD pairs co nsidered. The left turns and right turns per minute of the free flow travel time for the chosen route are on an average lesser than the average across the alternatives of the OD pairs. While the proportion time spent on arterials for

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45 chosen route is lesser than that of the average of average for each of the compositions, it is the other way round for the proportion time spent on local roads so as to balance the needle. The mean speed of the chosen route is also significantly lesser than that of the average of the average for each of the choice set compositions considered. Table 4 1 0 Comparison of the average of each of the explanatory variables with that of chosen route for long trips Chosen Route CS5 CF1 CS10 CF1 CS5 CF95 Travel Distance 15.59 14.94 15.03 14.88 Travel time 25.07 22.89 23.19 22.71 Left turns per minute 0.08 0.16 0.15 0.18 Right turns per minute 0.08 0.17 0.16 0.19 Intersection count per min 1.23 1.53 1.53 1.57 Prop time on expressways 0.07 0.07 0.06 0.06 Prop time on arterials 0.43 0.51 0.5 0.51 Prop on local roads 0.5 0.43 0.44 0.42 Max Speed 46.87 47.26 47.2 47.31 Mean Speed 37.5 40.23 39.94 40.47 Circuity 1.35 1.3 1.3 1.29 Ln (Path Size) 0.63 1.09 0.9 1.68 Table 4 11 4 12 depict the average and standard deviation of the standard deviation of the various explanatory variables defined in the previous section across the alternatives in the choice set compositions considered for all the common uni que 1127 OD pairs respectively The common trend with respect to variance across the alternatives of the choice set is depicted by the metric of standard deviation across the alternatives for each of the explanatory variables considered. The average of the same is tabulated in Table 4 11 Although, the variance indicator is not changing much for some explanatory variables like left, right turns and

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46 intersection count per unit free flow travel time, proportion time spent on each of the road types, for everything else of the explanatory v ariables the trend in the variance indicator is pretty much decreasing with increase in number of alternatives or controlling for the max commonly factor threshold. This is very peculiar and contrasts to the short trips considered. Table 4 11 Average of st d. deviation of different variables across the alternatives across the unique OD pairs considered for long trips CS5 CF1 CS10 CF1 CS5 CF95 Travel Distance 0.71 0.83 0.61 Travel time 1.68 1.77 1.32 Left turns per minute 0.07 0.07 0.06 Right turns per minute 0.07 0.07 0.07 Intersection count per min 0.32 0.39 0.28 Prop time on expressways 0.03 0.04 0.03 Prop time on arterials 0.12 0.15 0.11 Prop on local roads 0.12 0.14 0.1 Max Speed 1.94 2.48 1.82 Mean Speed 2.26 2.57 1.88 Circuity 0.07 0.08 0.06 Ln (Path Size) 0.37 0.33 0.51 Table 4 12 Std. deviation of std. deviation of different variables across the alternatives across the unique OD pairs considered for long trips. CS5 CF1 CS10 CF1 CS5 CF95 Travel Distance 0.79 0.77 0.58 Travel time 1.84 1.78 1.27 Left turns per minute 0.04 0.04 0.03 Right turns per minute 0.04 0.04 0.03 Intersection count per min 0.34 0.35 0.25 Prop time on expressways 0.06 0.08 0.06 Prop time on arterials 0.09 0.09 0.07

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47 Table 4 12.C ontinued CS5 CF1 CS10 CF1 CS5 CF95 Prop on local roads 0.09 0.09 0.07 Max Speed 2.72 2.94 2.27 Mean Speed 1.76 1.71 1.3 Circuity 0.07 0.07 0.06 Ln(Path Size) 0.15 0.12 0.15 Table 4 13 depicts the estimated coefficients for each of the reasonable explanatory variables considered for the model estimation, for each of the different choice set compositions mentioned. Table 4 13. Model estimation results for long trips Variable CS Size 5 Fo r CF = 1 CS Size10 For CF = 1 CS Size 5 For CF = 0.95 Est T stat Est T stat Est T stat Total Time 0.02* 0.54 0.12 2.9 0.17 4.13 Left Turns/min 19.44 13.37 20.23 16.62 19 15.16 Right Turns/min 21.1 14.95 23.29 18.42 21.3 16.57 Intersection count/min 1.38 6.68 1.56 8.83 1.47 9.15 Prop time on Local road 4.47 8.04 3.62 7.75 3.48 7.61 Circuity 1.51 2.06 0.19* 0.26 0.62* 0.84 Ln (Path Size) 1.91 8.17 0.98 7.55 0.7 2.98 Log Likelihood (at convergence) 405.41 598.64 523.90 Log Likelihood (equal shares) 1813.84 2595.01 1813.84 R squared 0.78 0.77 0.71 Notes: *indicates that the estimate is insignificant at 95% confidence level R squared = 1 {Log Likelihood (at Convergence)/Log Likelihood (equal shares)} The estimation resul ts for this case of long trips are counter intuitive from some of the

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48 turns/min, intersection count/min, proportion time spent on local roads are plausible, the signs on travel time and circuity make the case of counter intuition. The possible reasons for this can be explained from the T able 4 10 which says that the travel time of the chosen path is greater than the average on an average, which means that the ch oices generated starting from shortest path do not really converge to the point of chosen route for the alternatives in the choice set. Table 4 14.Percentage of trips with at max one trip in choice set having the controversial variable for that trip greater than that of chosen route Controversial variables CS5 CF1 CS10 CF1 CS5 CF0.95 Travel Time 80.2% 71.2% 71.9% Circuity 64.4% 51.8% 60.3% Table 4 14 looks into detail for the counter intuition of the signs on the coefficients of explanatory variables, travel time and circuity. This shows better understanding from the perspective of the choice set consideration from the choice set generation perspective. The numbers listed in the Table is the percentage number of trips which have a t max only one trip in the choice set except the chosen route having either travel time or circuity to be greater than that of the chosen route. As almost all the numbers listed except the lowest for composition of choice set size 10 and max commonly facto r of 1, every other number is above 60%, which means 60% of the data does not have more than route which has travel time or circuity greater than that of chosen route and hence the chosen route is prioritized as per maximizing log likelihood function and t he model predicted a positive sign. The statistical insignificance of these variables is due to the strong correlations and the effects of these are picked up in other variables.

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49 CHAPTER 5 CONCLUSIONS AND DISCUSSION As the objective of the study determi nes, we looked into the point of effect of choice set composition on route choice models from the estimation and application perspective. Various choice set compositions with different choice set sizes and variability factor between the alternatives in the choice sets are considered for two trip length trips. It is observed that the convergence of choice set generation to the chosen route will significantly affect the model structure. This is confirmed with respect to the classification we came up in terms of short and long trips. It is reported in T able 3 7 that the inclusion of chosen route in the choice set is very high in short distance trips than the long distance trips from the choice set generation perspective. Although, chosen route is forced into th e estimation choice set for the purposes of estimation, and the choice set generation pattern starts from shortest path and moves forward with the link elimination algorithm, the longer trips had hard time converging to the chosen route with the limits for time and maximum number of alternatives. The choice set sizes considered in this study are not adequate to develop behaviorally reasonable models for long trips. Inclusion of more choice set sizes for long trips do not yield a good estimation sample for this particular study. So, the probable remedy can be increasing the run times or max number of alternatives threshold during the choice set generation step. On the other hand, as the short trips have the highest percentage for the inclusion of chosen path in the choice set, the model structures came out up to the intuition expected with good empirical evidence and quantitative measures. Limitations : The sampling certainly has some limitations. Although there can be many more methods for sampling in reduct ion to a fixed size, as suggested by Schussler (2010), we did not employ those methods due to computational limitations. The selection of alternatives for

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50 gene ration patterns, despite there can be various combinations of the all the alternatives in the original choice set.

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51 APPENDIX PERCENT OVERLAP OF n th ROUTE WITH CHOSEN ROUTE 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 percent overlap with chosen route CS15_CF1_short Average 5th-%le 95th-%le 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 percent overlap with chosen route CS10_CF1_long Average 5th-%le 95th-%le

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52 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 percent overlap with chosen route CS10_CF95_short Average 5th-%le 95th-%le 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 percent overlap with chosen route CS5_CF95_long Average 5th-%le 95th-%le 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 percent overlap with chosen route CS5_CF90_short Average 5th-%le 95th-%le

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53 LIST OF REFERENCES 1. Bekhor, S., M.E. Ben Akiva, and S. Ramming. Evaluation of Choice Set Generation Algorithms for Route Choice Models. Annals of Operation Research, Vol. 144(1), 2006, pp. 235 247. 2. Bekhor, S., and C.G. Prato. Methodological Transferability in Route Choice modeling. Transportation Research Part B, Vol. 43(4), 2009, pp. 422 437. 3. Ben Akiva, M.E., M.J. Bergman, A.J. Daly, and R. Ramaswamy. Modeling Inter Urban Route Choice Behavior In: Volmuller, J. Hamerslag, R. (Eds.), Proceedings of the 9th International Symposium on Transportation and Traffic Theory. VNU Science Press, Utrecht, The Netherlands, 1984, pp. 299 330. 4. Ben Akiva, M.E., and M. Bierlaire. Discrete choice methods and their applications to short term travel decisions. In Handbook of Transportation Science, pages 5 33. Springer US, 1999. 5. Bierl aire, M., and E. Frejinger. Route Choice Modeling with Network Free Data. Transportation Research Part C, Vol. 16, 2008, pp. 187 198. 6. Bliemer, M.C.J., and P.H.L.Bovy. Impact of Route Choice Set on Route Choice Probabilities. Transportation Research Board: Journal of Transportation Research Board, no.2076, 2008, pp.10 19. 7. Bovy, P.H.L., S. Bekhor, and C.G. Prato. The Factor of Revisited Path Size: Alternative Derivation. Transportation Research Record: Journal of the Transportation Research Board, no. 2076, 2008, pp. 132 140. 8. Dhakar, N. Route Choice Modeling using GPS data. PhD Dissertation submitted to the University of Florida, 2012. 9. Flotterod, G. Bierlaire, M. Metropolis Hastings sampling of paths, Transportation Research Part B, Vol. 48, pp: 53 66. 10. Fr ejinger, E., M. Bierlaire, and M. Ben Akiva. Sampling of Alternatives for Route Choice Modeling. Transportation Research Part B, Vol. 43, 2009, pp. 984 994. 11. Frejinger, E., and M. Bierlaire. Capturing Correlation with Subnetworks in Route Choice Models. Tr ansportation Research Part B, Vol. 41, 2007, pp. 363 378. 12. Hood, J. Sall, E. Charlton, B. A GPS based route choice model for San Fransisco, California, The International Journal of Transportation Research, 2011, Vol. 3, pp: 63 75. 13. Papinski, D., and D.M. Scott. Modeling Home to Work Route Choice Decisions Using GPS Data: A Comparison of Two Approaches for Generating Choice Sets. Presented at the Transport Research Board Annual Meeting 2011b, Washington D.C.

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54 14. Prato, C.G., and S. Bekhor. Applying Branch and Bound Technique to Route Choice Set Generation. Transportation Research Record: Journal of the Transportation Research Board, no. 1985, 2006, pp. 19 28. 15. Prato, C.G., and S. Bekhor. Modeling Route Choice Behavior: How Relevant Is the Composition of Choice Set? Transportation Research Record: Journal of the Transportation Research Board, no. 2003, 2007, pp. 64 73. 16. Prato, C.G. Route Choice Modeling: Past, Present and Future Research Directions. Journal of Choice Modeling, Vol 2 (1), 2009, pp.65 100. 17. Quattro ne, A., and A. Vietetta. Random and Fuzzy Utility models for Road Route Choice. Transportation Research Part E, Vol. 47, 2011, pp. 1126 1139. 18. Schussler, N. Accounting for Similarities in Destination Choice Modeling. PhD Dissertation submitted to ETH ZURIC H, 2009. 19. Schussler, N., and K.W. Axhausen. Accounting for Route Overlap in Urban and Sub urban Route Choice Decisions Derived from GPS Observations. Paper Presented at Proceedings of 12th International Conference on Travel Behavior Research, Jaipur, India December 2009. 20. Smits, E.S., Bliemer M.C.J., Pel, A., Arem van, B. On Route Choice Models with Closed Form Probability Expressions, Transportation Research Board Annual Meeting, 2014. 21. Spissu, E., I. Meloni, and B. Snjust. Behavioral Analysis of Choice of Daily Route with Data from Global Positioning System. Transportation Research Record: Journal of the Transportation Research Board, No. 2230, 2011, pp. 96 103. 22. Vreeswijk, J., Thomas, T ., Berkum van, E., Arem van, B. Perception bias in route choice, Tra nsportation Research Board Annual Meeting, 2014.

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55 BIOGRAPHICAL SKETCH Avinash Geda, originally from Andhra Pradesh, India, enrolled at the University of Florida in August of 2012. He joined the Transportation Graduate program at UF following completion of his B.Tech degree in Civil Engineering from the National Institute of Technology Warangal, India. As a graduate assistant at the McTrans Center at the University of Florida, he worked on the analyzing and testing of the software level implementation of Highway Capacity Manual (HCM), Highway Capacity Software (HCS) under the supervision of Mr. William Sampson. He also served as teaching assistant for Mr. Sampson during his graduate studies at UF. He was working closely with Dr. Sivaramakrishnan Srinivasan in the area of route choice modeling using discrete choice Methodology.