Cruising for Parking in Downtown Miami

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Title:
Cruising for Parking in Downtown Miami
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1 online resource (52 p.)
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english
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Shmaltsuyev, Maksim
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University of Florida
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Gainesville, Fla.
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Thesis/Dissertation Information

Degree:
Master's ( M.A.U.R.P.)
Degree Grantor:
University of Florida
Degree Disciplines:
Urban and Regional Planning
Committee Chair:
Blanco, Andre
Committee Co-Chair:
Steiner, Ruth L

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Subjects / Keywords:
congestion -- cruising -- parking -- traffic -- transportation
Urban and Regional Planning -- Dissertations, Academic -- UF
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Urban and Regional Planning thesis, M.A.U.R.P.
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theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
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Abstract:
This study is about parking and the role that parking plays within the larger context of traffic congestion. The thesis evaluated the practicality of a performance-based parking program for the downtown business district of Miami by answering the following two research questions: 1) is there evidence of cruising for parking in downtown Miami? And 2) does Shoup's theory, higher parking price differentials (off-street - on-street parking prices) generate higher vehicle miles traveled (VMT), apply to downtown Miami. This was accomplished through the development of four sets of experiments. The first experiment tested to see if average vehicle miles traveled (VMT) on metered streets were greater than unmetered streets. The second experiment was a regression analysis to see if parking price differentials, employment density, and transit stops have a significant impact on VMT. The third experiment was a production of three maps that were used to show if VMT was more associated with density or transit stops than with parking price differentials. The fourth, and final experiment, was a field trip to downtown Miami to collect primary data on on-street occupancy rates and the time it took to cruise for a parking spot. The results showed that cruising for parking is happening in downtown Miami, but it is not severe enough to justify implementing a performance-based parking program for the downtown business district of Miami.
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In the series University of Florida Digital Collections.
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Includes vita.
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Includes bibliographical references.
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Description based on online resource; title from PDF title page.
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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.
Statement of Responsibility:
by Maksim Shmaltsuyev.
Thesis:
Thesis (M.A.U.R.P.)--University of Florida, 2012.
Local:
Adviser: Blanco, Andre.
Local:
Co-adviser: Steiner, Ruth L.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2014-05-31

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lcc - LD1780 2012
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UFE0044255:00001


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CRUISING FOR PARKING IN DOWNTOWN MIAMI B y MAX SHMALTSUYEV A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UN IVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN URBAN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 2012

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2 2012 Max Shmaltsuyev

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3 AC KNOWLEDGMENTS To my parents Bella and Michael Shmaltsuyev. This thesis would not have be en possible without your love and support. I am also gr ateful to my thesis committee, Andres Blanco and Ruth Steiner, for their support and assistance in helping me develop, refine, and produce this thesis.

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4 TABLE OF CONTENTS p age ACKNOWLEDGMENTS ................................ ................................ ................................ .............. 3 LIST OF TABLES ................................ ................................ ................................ .......................... 6 LIST OF FIGURE S ................................ ................................ ................................ ........................ 7 LIST OF ABBREVIATIONS ................................ ................................ ................................ ......... 8 ABSTRACT ................................ ................................ ................................ ................................ .... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 11 2 LITERATURE REVIEW ................................ ................................ ................................ ....... 13 Traffic Congestion ................................ ................................ ................................ .................. 13 Highway Construction ................................ ................................ ................................ ............ 14 Public Transit ................................ ................................ ................................ .......................... 15 Land Use ................................ ................................ ................................ ................................ 17 Automobile ................................ ................................ ................................ ............................. 17 Road Use ................................ ................................ ................................ ................................ 20 Parking ............................................................................................................................ ......... 20 Cruising for Parking ................................ ................................ ................................ ................ 23 3 METH ODOLOGY ................................ ................................ ................................ ................. 27 Experiments ................................ ................................ ................................ ............................ 29 4 RESULTS ................................ ................................ ................................ ............................... 35 Metered vs. Unmetered Streets ................................ ................................ ............................... 35 Regression Analysis ................................ ................................ ................................ ................ 35

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5 Maps ................................ ................................ ................................ ................................ ........ 36 Field Work ................................ ................................ ................................ .............................. 37 5 DISCUSSION ................................ ................................ ................................ ......................... 44 6 CONCLUSION ................................ ................................ ................................ ....................... 48 R EFERENCE LIST ................................ ................................ ................................ ...................... 50 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ........ 52

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6 LIST OF TABLES Table p age 2 1 Delay i ncrease if public transportation s er vice were eliminated........................ ...............26 3 1 Record from the Roadway Characteristics Inventory (RCI) dataset ... ..............................32 3 2 Blocks used to test on street occupancy rates. ..................................... ..............................32 3 3 Popular d estinations............................................................................ ...............................32 4 1 Metered vs. unm etered s treets............................................................ ................................38 4 2 Correlation matrix .................................................... ..........................................................38 4 3 Regression summary output table.. ...................................................... ..............................38 4 4 On street vehicle occupancy rate s .................................... ............................. ....................39 4 5 Cruis ing for parking...........................................................................................................40 5 1 Twentieth century cruising................................................................................................47

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7 LIST OF FIGURES Figure p age 2 1 Highway construction equilibrium c urv e............................... ........... .................................26 3 1 Miami central business district (CBD) study area....... ........................ ..............................33 3 2 Metered vs. u n metered street s ........................................................... .... .............................34 3 3 Calculating de pe ndent and independent variables.................... ........... ..............................34 4 1 VMT vs. parking price differentials .................................................... ..............................41 4 2 VMT vs. employment density............... ............................................... ....... .......................42 4 3 VMT vs. transit stops........................................................................................................ .43

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8 LIST OF ABBREVIATION S CBD Central Business District DDA Downtown Development Authority DOT Department of Transportation GIS Geographical Information System HOV High Occupancy Vehicle IC Individual Cost IDW Inverse Distance Weighted LRTP Long Range Transportation Plan MSC Marginal System Wide Cost RCI Roadway Characteris tics Inventory TTI Texas Transportation Institute VMT Vehicle Miles Traveled

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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 Arts in Urban and Regional Planning CRUISING FOR PARKING IN DOWNTOWN MIAMI By Max Shmaltsuyev May 2012 Chair: Dr. Andres Blanco Co Chair : Dr. Ruth Steiner Major: Urban and Regional Planning This study is about parking and the role that parking plays within the larger contex t of traffic congestion. The thesis evaluated the practicality of a performance based parking program for the downtown business district of Miami by answering the following two research questions: ( 1) is there evidence of cruising f or parking in downtown Miami? a nd ( higher parking price differentials (off street on street parking prices) generate higher vehicle miles traveled (VMT), apply to downtown Miami? This was accomplished through the development of fo ur sets of experiments. The first experiment tested to see if average vehicle miles traveled (VMT) on metered streets were greater than unmetered streets. The second experiment was a regression analysis to see if parking price differentials, employment density, and transit stops have a significant impact on VMT. The third experiment was a production of three maps that were used to show if VMT was more associated with density or transit stops than with parking price differentials. The four th and final ex periment, was a field trip to downtown Miami to collect primary data on on street occupancy rates and the time it took to cruise for a parking spot. The results showed that cruising for parking is happening in downtown Miami, but

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10 it is not severe enough t o justify implementing a performance based parking program for the downtown business district of Miami.

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11 CHAPTER 1 INTRODUCTION Everyone hates traffic congestion. It makes travel unreliable, reduces regional economic their eyes one lane at a time. Traffic congestion is slowing America down. In cities large and small, traffic congestion is steadily getting worse each y ear. In 2007 the Texas Transportation Institute (TTI) calculated that congestion caused urban Americans to travel 4.2 billion hours more and to purchase an extra 2.9 billion gallons of fuel for a congestion cost of $78 billion. This was an increase of 220 million hours, 140 million g allons and $5 billion from 2004. What is even more alarming is the disparity between driving and public transportation. The 2001 National Household Travel Survey estimated that 87 percent of all trips in the U.S. are made by per sonal motor vehicles and only 1.5 percent by public transit All in all, the numbers are indicative of the fact that traffic congestion is an ever growing dilemma that needs to be attac k ed from many different angles. In the transportation world, most of the focus goes towards highway projects and road building. Congestion mitigation plans hardly, if ever, factor in parking, but economists like true cost of on stre et parking. Parking is a niche topic that usually goes unnoticed in academic transportation research. Its neglect is an interesting contradiction due to the fact that cars are parked 95% of the time and only moving 5% of the time (Shoup 2005). Parking is an untapped research topic to base my thesis on parking and the role that parking plays within the larger c ontext of traffic congestion. The thesis sought to evaluate the practicality of a performance based parking prog ram for the downtown busin ess district of Miami by answering the following two research

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12 questions: ( 1) is there evidence of cruising for parkin g in downtown Miami? and ( 2) does differential s (off street on street parking prices ) generate higher vehicle miles traveled (VMT), apply to downtown Miami?

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13 CHAPTER 2 LITERATURE REVIEW The literature review is organized in a top down fashion starting with the nature and origin of traffic congestion. Next, transportation policies are examined to gain a better understanding of what has been done, what is being don e, and what will be done to mitigate traffic congestion Then, the analysis shifts from tr ansportation policies to park ing and why parking is important from a planning perspective. Finally, I narrow the focus down to one specific parking problem, cruising for parking, based parking program. T raffic Congestion The best way to approach an arduous dilemma, such as traffic congestion, is to first understand the origin of the problem. According to Downs (2006), traffic congestion is a worldwide problem with four primary causes: 1. Congestion exists because societies organize economies so most people will work during the same hours each day. 2. Rising incomes intensify congestion by permitting more households to purchase vehicles and buy homes mainly in suburban areas. 3. Population gro wth. When metropolitan growth is accompanied by rising prosperity, more households buy more cars, and roads become more congested. 4. Incidents and accidents. They result from high volumes of traffic generated by the first three causes. bel ieves that traffic congestion results from economic growth. We all

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14 If we ever hope to reach a sustainable equilibrium between our desire for prosperity and our basic need to move we need to better understand how to manage traffic congestion. This leads to the next section of the literature review, transportation policies. Highway Co nstruction Highway construction is the most common solution to traffic congestion. The economic rationale behind highway construction is to eliminate the inefficiency that is caused when individual cost (IC) exceeds marginal system wide cost (MSC) (see Fi gure 2 1 ) Note that as trip a result, total system wide benefits will begin to fall as additional users enter the system beyond s the MSC curve to the right creating equilibrium between MSC and IC (Heikkila 1994). Traffic volumes can now increase substantially as a result of lowered costs resulting from the expanded capacity of the transportation system. On the other hand, Downs (2004) views this as a self defeating exercise since increased system capacity only seems to stimulate more demand. who formerly used alternative routes during peak hours switch to the improv ed expressway (spatial convergence); (2) many drivers who formerly traveled just before or after the peak hours start traveling during those hours (time convergence); and (3) some commuters who used to take public transportation during peak hours now switc h to driving, since it has become faster (modal convergence). This does not mean that there are no benefits in highway construction. According to the expande d network and, secondly, total net benefits from use of the system have been increased. The fact that more people are using the system is a testament to its enhanced value.

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15 The key question is whether the benefits from additional use warrant the very large construction costs required to expand the network. P ublic Transit While not all academic practitioners may agree, according to Small (1997), contributions from professional economists have led to the understanding of transit subsidies. First and foremost, public transit (like any service performed in batches) benefits from economies of scale. If demand on a particular corridor rises, the transit provider serving that corridor can respond in one of several ways. The provider can maintain the same l evel of service allowing average cost to decrease, or the provider can increase route density and/or frequency, which produces cost savings to passengers in the form of less walking or waiting time. In either case, total average cost (to provider and users ) declines. A different way of putting the same point is that given a particular level of service, as measured by the density of routes and the frequency of vehicles on a given route, handling an additional passenger costs little extra because it does not necessitate extra vehicles or drivers. From the point of view of economic theory, this is the primary justification for transit subsidies (Small 1997). The problem with mass transit in America is that it is difficult to find corridors with passenger dens ities suitable for successful mass transit service. For example, the regional LRTP (long range transportation plan) for Los Angeles includes extensive investment in a completely new rail network for both light and heavy transit service. The purpose is to i ncrease the modal share of transit and, in the long run, to redirect land use patterns to a more compact, higher density urban form. Giuliano and Small (1995) say that there is little evidence to suggest that these goals can be achieved. According to Giuli ano and Small, investment in rail transit has

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16 proven to be a terribly inefficient way to divert trips from automobiles. To illustrate this conclusion Giuliano and Small examined the Los Angeles Blue Line. The Blue Line is a single light rail line extending 23 miles from downtown Los Angeles to the city of Long Beach. Completed in 1991 at a capital cost of $877 million, its 1992 annual operating cost was $42 million, of which just 11 percent was covered by fares. Surveys show that only about 30 percent of it s passengers are former automobile users (drivers or passengers); the remainder are former bus patrons or new travelers. The public subsidy for each regular Blue Line passenger attracted from automobile comes to between $20,000 and $36,000 per year, or app roximately $40 to $72 per one way trip ( pg. 203 204). On the other hand, TTI (2007) claim that if public transportation service was discontinued and the riders traveled in private vehicles, urban areas would have suffered an additional 541 million hours of delay and consumed 340 million more gallons of fuel in 2007 ( see Table 2 1 ). The value of the delay and fuel that would be consumed if there were no public transportation service would be an additional $10.2 billion in congestion cost, a 13 percent incr ease over current levels in urban areas Bus : Research shows that demand for transit is generally more elastic with respect to service quality than to price (Small 1997). This suggests that the best strategy for increasing the market share of transit may be through improvements in service quality, which includes travel time, comfort, reliability, ease of access, and convenience of transfers. Three specific strategies to improve bus transit and paratransit include permitting bu ses, vans, and carpools to us e h igh o ccupancy v ehicle (HOV) facilities, tailoring commuting services to very specific markets, and using shuttle vans or jitney services. Although the market for these services is too small to reverse the overall dominance of automobiles, it is large en ough to offer some limited relief to growing traffic congestion.

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17 L and Use By redirecting or limiting land use development, some hope the automobile will become less attractive than public transit or other modes of travel. Policy driven changes in land use patterns often cluster in two alternatives polycentric metropolitan areas and increased residential density. Many urbanists and environmentalists believe that existing patterns of land use are the ersion fosters further dependence on the automobile, leading to congestion, environmental degradation, and a deteriorating quality of urban life. They conclude that transit oriented development could ultimately reshape cities and reduce congestion and auto mobile use. Giuliano and Small (1995) are skeptical of this conclusion. They claim that although the highway system is one explanatory factor, many other decentralizing forces are also at work. These include the growing demand for single family housing ass ociated with rising household incomes, the increasing scale of residential and employment development, and, in the U.S. at least, historical preference for low density living. There is no indication that the forces of decentralization are residing. Rising incomes, information based production, more flexible work arrangements, and increasing weight on environmental quality in individual location choices all foster continued decentralization and reliance on the automobile. A utomobile Market Incentives : Con gestion pricing can be used as a disincentive to commuting by car. Theoretically, the optimal toll amount would equal the cost a driver imposes on others by entering the freeway and slowing down traffic. In the past, congestion tolling may have been expens ive to administer. Today, recent technological advances make the prospect more feasible.

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1 8 According to Giuliano and Small (1995), congestion pricing does not suffer the drawback of latent demand because it uses money, instead of congestion delay, to ration scarce highway capacity. The price applying to peak times creates an incentive not to overuse that capacity, and the incentive remains even when congestion itself is drastically reduced. Rationing by money instead of time is also more efficient because ti me spent in congestion delay is simply wasted, whereas the tolls paid by travelers are revenue to public or private organizations. Like any price change, congestion pricing would have significant effects on the distribution of real incomes. The complexity of the shifts in labor, housing, and land markets makes the ultimate distributional impacts far more difficult to predict than the direct impacts, which have been the focus of most analyses (Giuliano and Small 1993). Even the true direct impacts cannot b e judged without looking at how the revenues are spent. Ridesharing (a.k.a. Carpooling and Vanpooling) : Ridesharing has the potential to reduce traffic congestion by a considerable amount. One model suggests that if 30 percent of the single car commuters switched to two person car pools, traffic congestion would decrease by 10 percent (Downs 2004). However, carpooling is less popular than in the 1970s. The reasons are related to the availability of the automobile, increased fuel efficiency of cars, and d emographic changes. According to Ferguson (1997), data shows that older individuals and individuals with more years of education tend to be less likely to carpool. Also, the increase in the percent of single persons and persons without children is related to a decrease in car pools. Flexible Work Places : Off peak work hours and telecommuting are alternative strategies to decrease congestion. Employers could stagger work schedules allowing some workers to come in later in the day and leave after peak hours, or work extra hours on w eekends. Fax machines, modems, i nternet access, and other recent innovations could allow workers to work at home.

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19 There may even be some combination of telecommuting for part of the day and going to work during off peak hours. commute options programs. Built around a Clean Air Campaign, the program involved payment of cash incentives to driver only commuters who switched to another mode. Particip ants earned up to $60 per month (for three months) by choosing and using an eligible alternative mode of transportation. During the program, participants used alternative modes an average of more than four days each week compared to less than one day per w eek before. A year and one half after the program, participants still used a commute alternative an average of 2.4 days per week. Overall, program participants decreased their single occupant commute modes from 84 percent to 53 percent. This type of chang e has benefits in less vehicle travel and fewer parking spaces needed and participants have reported lower frustration levels and better on time arrival. period personal vehicle trips by one per week could have substantial c ongestion benefits, if employer and employees choose these options (TTI 2007). Respond More Quickly to Traffic Blocking : Removing accidents and incidents from major roads faster by using roving service vehicles run by government run Traffic Management Ce nters equipped with television and electronic surveillance of road conditions is an excellent tactic for reducing congestion delays (Downs 2004). An incident management program can also collisions within the stop and go traffic caused by the initial notification. Quick removal of stalled vehicles and crashes, combined with the Motorist Assistance Program, has reduced collisions by more than 10 percent in the first two years of operation, saving $70 million in collision costs (TTI 2007).

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20 R oad Use HOV and Diamond Lanes : The theory is that if HOV and diamond lane travelers can travel faster, then other drivers will be encouraged to rideshare. According to TTI (2007), the 70 congested corridors with data on the person volume and travel time for high occupancy vehicle lanes or high occupancy toll lanes in 15 metropolitan regions showed an annual delay reduction of 33 million hours, with a value o f $620 million per year. However, some suggest that these lanes will not necessarily reduce congestion. Unless new lanes are added, designated HOV lanes may reduce the overall road carry capacity and intensify congestion in single occupancy vehicle lanes. Downs (2004) recommends policies that combine these lanes with financial incentives, such as widespread employer sponsored programs for carpooling. Even with potential gain, HOV lanes may be more effective in reducing congestion in comparison to building highways, because the new HOV lanes encourage ridesharing. Metering : Entrance ramp meters are designed to create more space between entering vehicles so those vehicles do not collide or disrupt the flow of traffic. The Minnesota DOT conducted an experime nt that consisted of turning off the 430 ramp meters in the Minneapolis St. Paul region for seven weeks in 2000. The results showed that there is travel time savings from operating the ramp meters, but the most dramatic change was the 26 percent increase i n crashes when the meters were de activated. There was also a 14 percent increase in the volume handled by the freeway with the meters on (TTI 2007) P arking According to Shoup (2005), twenty first century parking problems stem from poor parking policies that aim t o keep curb parking free or cheap and require lots of off street parking This practice has done a lot of harm for the following reasons:

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21 Skew Travel Choices : The pa rking problem developed when car ownership grew rapidly during the 1910s and 1920s. In the 1930s, cities began to require off street parking in their zoning ordinances to deal with the parking shortage. Requiring new buildings to provide lots of off street parking solved one problem, the shortage of free curb parking, but unintentionally exacerbated many other problems. Urban planners began to assume that most people would travel everywhere by car, park at their initial destination, and then drive on to the ir next destination. Automobile centered planning led the automobile to be the 1 st and in most cases the only, mode of transportation. Distort Urban Form : Efficient land use regulations are a critical factor in maximizing development potential. Local regulations requiring developers to provide ample off street parking are inefficient and have historically led to the dispersal of new development opportunities away from central cities to suburban areas. For example, in 1961, Oakland, California, began to require parking spaces for their tenants, and within three years, the number of apartments constructed per acre in Oakland fell by thirty percent (Lewyn, 2008). Developers who construct new projects in urban areas often produce developments that are lower in density as a result of parking requirements. This practice translates into less dense development patterns with fewer residences and businesses per acre a practice that also reduces walkability and decreases opportunities to create efficient transit systems (Lewyn, 2008). Degrade Urban Design : No great city is known for its abundant parking supply. Most of the streets we admire for great urban design cannot be replicated with today parking requirement s. Many older areas that were built before citi es required off street parking compare more favorably to neighborhoods built since then. For example, multiple car garages are a great change in domestic architecture in the past several centuries. Houses that used to be built with

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22 porches that jut out to wide sidewalks are now getting replaced with garages and driveways that jut out to a street or a narrow sidewalk that has 21 floors of offices above 15 levels of parking. The building has remained empty since it was completed in 1986 partly because of its odd parking arrangement (Shoup, 2005). The practice of using parking generation rates as the basis for parking requirements has been particularly inappropriate at land uses with short, sharp peaks in parking demand. For example, shopping malls are slammed on the weekends and during the holidays but during the many parking spaces empty almost all of the time. The full cost of the parking lot is incurred to serve a few hours each week, so the cost per hou r the parking space s area occupied can be enormous. Raise Housing Costs : Parking requirements raise the cost of housing, but most people Increased parking requirement s increase housing development costs, which reduce the supply of lower priced housing and raise the cost to the consumer. A study by Jia and Wachs (1998) found that in San Francisco single family houses wi thout off street parking sold for an average of $34 8,000 while otherwise similar houses with an off street space sold for $395,000 A parking space increased the price of a house by $47,000. The study also estimated how the required parking increased the income necessary to buy a house. The annual family i ncome necessary to get a mortgage was $67,000 for a house without parking, and $76,000 for one with parking. As a result, 24 percent more San Francisco households could afford to buy houses if they did not include the required on site parking space.

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23 Damag e to the Urban Economy : The central business district (CBD) of any city is a prime example of how off street parking requirements harm the urban economy. High density is a prime advantage of the CBD because it offers proximity to many activities, but high density also implies a high cost of constructing new parking spaces. According to Shoup (2005), off street parking requirements increase the cost of all d evelopment, reduce density by preventing land from other uses, and increase traffic both within the CBD and on the routes to it. All in all, off street parking requirements reduce the essential features that make a CBD attractive high density and accessi bility. C ruising for Parking One of the problems of our existing system of having low on street parking prices is that it creates cruising for curb parking. Cruising for parking happens as drivers circle an area or parking lot looking for a parking space dilemma of needing to park for one hour and curb parking is $.50 an hour and off street parking is $2 an hour. We usually ask ourselves, how long would I be willing to cruise for curb parking rather tha n pay the higher price for off street parking? The few researchers that have attempted to estimate the volume o f cruising and the time it took to find a curb space found that between 8 and 74 percent of traffic was cruising for parking and the average time to find a curb space ranged between 3.5 and 14 min (Shoup, 2006). T he wide range in the estimates can be attributed to the reality of cruising for parking On most streets most of the time, none of the traffic is cruising, but on some streets some of the time, most of the traffic may be cruising (2006) The following excerpt from Transport Policy shows the snowball effect that cruising for parking has on the transportation network. Even a small search time per car can create a surprising amount of traffic. Consider, for example, a congested downtown where it

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24 takes three minutes to find a curb space and the parking turnover is 10 cars per space per day. Each curb space generates 30 mi n of cruising time per day. If the average cruising speed is 10 miles an hour, each curb space generates five vehicle miles traveled (VMT) per day. Over a year, this cruising amounts to 1825 VMT, greater than halfway across the United States, for each curb space. Because this cruising adds to traffic that is already congested, it makes a bad situation even worse (Shoup, 2006 pg 480 ). Cruising is individually rational when on street parking is cheaper than off street parking. Collectively, however, it congests traffic, causes accidents, wastes fuel, pollutes the air, and degrades the pedestrian environment. Some cities are beginning t o realize the problems that are cause d when they unde rprice curb parking, and they are trying to solve the parking problem by get ting the price of parking right and implementing the following three reforms: 1. Charge the right price for curb parking. The lowe st price that will leave one or two vacant spaces on each block (85% occupancy rate) performance based pricing. 2. Return the meter revenue to the neighborhoods that generate it. Revenue return will make performance based prices for curb parking politically popular. 3. Reduce or remove off street parking requirements. Do not require additional parking density and new uses for old buildings (Shoup, 2005) San Francisco is a mode l city when it comes to parking reform The San Francisco Municipal Transportation Agency (SFMTA) is one of the few transportation entities in the country that controls both transit and parking. Under the city code, developments in the downtown area do not require parking, and only seven percent of the gross floor area of any building may be dedicated to parking facilities. Furthermore, the proposed parking scheme must provide a proper mix of parking types (long term, short term, and carpool) (TRB, 2004c). As a demand management strategy and disincentive to automobile commuting, there is a 25% parking tax imposed on all garages and lots in the City. Finally, in 2011, San Francisco introduced

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25 SFpark. The SFpark demonstration, backed with nearly $20 million in federal funding, sets parking rates at curbside spaces and in public garages based on demand. SFpark is a federally funded two year pilot project that will adjust parking prices each month block by block and hour by hour. I f spots are always full at a certain place, prices will go up. If many spots are on each block. The idea is that most spots should be utilized, yet anyone looking for a spot should be abl e to find one quickly reducing the cars search for parking (SFpark.org, 2011). As San Francisco launches and tests SFpark, I want ed to see if other cities would benefit from performance based parking prices. The next chapter outlines the me thodology tha t I used to answer the research questions introduced in the previous chapter.

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26 Table 2 1. Delay increase if public transportatio n service were eliminated Population Group and Number of Areas Average Annual Passenger Miles of Travel (Million) Delay Reduction Due to Public Transportation Hours of Delay (Million) Percent of Base Delay Dollars Saved ($ Million) Very Large (14) 37,691 430 17 8091 Large (25) 5459 64 7 1193 Medium (30) 1665 15 4 270 Small (16) 287 1 3 26 Other (352) 6324 31 5 574 National Urban Total 51,426 541 13 10,154 Source: 2007 Texas Transportation Institute Figure 2 1 Highway c onstruction e quilibrium c urve ( Heikkila 1994 ).

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27 CHAPTER 3 METHODOLOGY The study used street and off street parking rates to test if cruising for parking was occurring in downtown Miami. Road data was provided by Florida Department of Transportation (FDOT) Statistics Office from the Roadway Characteristics In ventory (RCI) extracts for the year 2008. The Roadway Characteristics Inventory is the largest database ab out traffic in Florida and contains information related to roads that are maintained by or are of special interest to FDOT (FDOT, 2009). In particular the stu dy used the following fields from the RCI dataset : RDWYID RCI identifies the different roadway segments with a unique Roadway ID, with segments containing information on roadway features, characteristics and other data elements (FDOT, 2009, pg.2 4) BEGPT This is a seven byte field that represents the beginning mile point location of the Roadway ID (FDOT, 2009, pg.3 15). ENDPT. This is a seven byte field that the ending mile point of the Roadway ID (FDOT, 2009, pg.3 15) FUNCLASS The functional classification of a road refers to the FHWA approved designations that are divided into a hierarchy of road types that range from arterials to Locals (FDOT, 2009, pg.2 1) NOLANES. Number of through roadway l anes SECTADT. The total volume of traffic on a highway segment for one year, divided by the number of days in the year (FDOT, 2009, pg.12 9). V ehicle Miles Traveled (VMT), the number of miles driven by residential vehicles, is a calculation that is commonly used in traffic studies. VMT was calculated for every record in the RCI dataset by multiplying average daily traffic by lane miles and dividi ng that by number of through roadway lanes. Table 3 1 is an example of a record from the RCI dataset. In this example VMT was calculated by multiplying 11,263 (SECTADT) by 0.302 (LANEMILES) and

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28 dividing that by 2 (NOLANES_R + NOLANES_L). Lane mile was calc ulated by multiplying road section (ENDPT BEGPT) by number of through roadway lanes. Although the dataset is e 93 records in the study area. The central business di strict (CBD) of Miami was chosen because it has the following two conditions that encourage cruising for parking : low on street parking prices ( compared to other large metropolitan areas ) and an abundant supply of off street parking. Figure 3 1 is a map of the study area. The CBD boundary is not a political boundary. The boundary was originally developed by the Miami Downtown Development Authority (Miami DDA), and then further expanded by a research team at the University of Florida (UF). The research team from UF analyze d the extent to which parking management strategies can positively or negatively influence the economic and social vitality of a CBD. The original CBD developed by the Miami DDA included the following boundaries : NE 5 th Street to the north, the Miami River to the south, Biscayne Boulevard and Bayfront Park to the east and SE First Avenue to the west (Miami DDA, 2009) The UF research team expanded the northern boundary from NE 5 th Street to NE 9 th Street based off of rec Downtown Master Plan. In F igure 3 1 t he violet color polygons represent off street parking and the blue bold lines represent on street p arking. The rates for on street parking range from $1.25 to $1.50 per hour and the rates for off street parking range from $1 to $15 per hour. All private off street parking garages and surface lots that are not open to the public were removed from the off street parking dataset. The rationale behind that was that motorists who rent or own a parking spot/lot are not inclined to cruise for parking since they already have a reserv ed spot. In total, there were 49 off street parking records ( including both garages and surface lots) in the study area

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29 Experiments The first experiment tested to see if average vehicle miles traveled (VMT) on metered streets were greater than unmetered streets for 2008 In downtown Miami the majority of off street parking is located adjacent to metered parking. If cruising is occurring you would expect av erage VMT to be higher on metered streets than on unmetered streets. The second experiment was a regression analysis to see if higher parking price differentials (off street parking on street parking) generated higher VMT According to Shoup (2005), mo torists are most likely to cruise if curb parking is cheap and off street parking is expensive. In addition to parking price differentials, other variables, employment density and transit stops, were also included in the regression analysis to control for other factors that can influence VMT The dependent variable was VMT and the independent variables were parking price differentials, employment density, and transit stops. Null hypothesis, there is no difference in the prediction of VMT given parking price differentials, employment density, and transit stops Employment density was determined by u sing census block extracts from Longitudinal Employer Household Dynamics (LEHD). Longitudinal Employer Household Dynamics (LEHD) is a program within the U.S. Census Bureau. The program is tasked with the mission of administering and maintaining data on emp loyers and employees. The data was accessed by data at the census block level, so to obtain the data that was relevant to downtown Miami I used the following census b locks: 34, 36.01, 37.01, 37.02, 67.01, and 67.02. OnTheMap also allows the user to export shapefiles that contain raster files called therma ls. A thermal is a digital image that displays a variety of different colors and each color represents a different range of jobs per square mile. Tra nsit stops were determined by using the following shapefiles from

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30 miamidade.gov: Metro Rail stations, Metro Mover stations, Met ro Mover routes, and bus stops. Each parking garage was used as a focal point for determining V MT parking price differentials, employment density, and the number of transit stops that pertain to that particular garage. Every garage in the off street parking data set is marked with a unique ID. Figure 3 3 is an example of how I determined the variab les for the garages with the ID 1, 2, and 3. VMT = 6545 + 4819.5 = 11,364.5, parking price differential = 2 1.5 = $0.5, transit stops = 9, and employment density of 25,044 jobs per square mile. The third experiment was a production of three maps that were used to show if VMT was more associated with density or transit stops than with parking differentials. In total, the following three maps were developed: VMT vs. Parking Differentials, VMT vs. Employment Density and VMT vs. Transit Stops The employment density and transit stops maps were developed using ArcGIS 10 and data from Experiment 2, and the map for parking differentials was developed by using a spatia l analyst tool in ArcGIS called Inverse Distance Weighted (IDW) IDW is a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. I used IDW to generate two surfaces; one surface is a surface based off of the hourly off street parking rate and the other surface is a surface based off of the hourly on street par king rate. The differential was achieved by subtracting the two surfaces, and the areas with the hig hest parking p rice differential were The fourth experiment was a field trip to downtown Miami to test on street occupancy rates and the time it took to cruise for a parking spot The tests were conducted around the areas that were id spots in E xperiment 3 The tests were conducted during a

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31 normal work week between the hours of 12pm and 5pm. To te st on street occupancy I selected the blocks that are listed in Table 3 2 and counted the number of parking spots t hat are on the block vs. the number of cars parked in those parking spots. I also collected data on the adjacent off street parking garage, the hourly and daily rate, date and time. To t est for cruising I selected spot area (see Table 3 3) and timed (using a stop watch) how long it took me to find an on street parking spot next to the popular destination. The stop watch started the moment I spotted the popular destination and stopped when I found a parking spot. I a lso timed how long it took me to walk from the parking spot to the destination. Additional data such as the off street parking garage, the hourly and daily rate, the intersection where the on street parking spot was found, the total trip (in minutes) and t he distance walked (in miles) was collected as well.

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32 Table 3 1. Record from the Roadway Characteristics Inventory (RCI) dataset RDWYID BEGPT ENDPT FUNCLASS LANEMILES SECTADT DVMT 87067510 0.543 0.694 16 0.302 11263 1700.713 Source: 2009 Florida Department of Transportation Stati stics RCI Office Handbook Table 3 2. Blocks used to test on street occupancy rates Hot Spot ID On Street Parking 1 NE 3 St between NE 2 AVE & NE 1 Ave 2 NW 1st Ave between NW 3 ST & NW 2 ST 3 Flagler St between SW 1 Ave & Miami Ave 4 NE 1st between NE 3 Av & NE 1 Ave 5, 6 Flagler between SW 2 Av & SW 1 Ave 7 NE 2nd St between NE 1st Ave & NE 2nd Ave 8 SE 1st ST between Biscayne Blvd & SE 2 nd Ave Table 3 3. Popular destinations Hot Spot ID Popular Destination 1 Wolfson Campus 2 United States Courthouse 3 Miami Dade County Courthouse 4 Dade Common Wealth Building 5, 6 Cultural Center 7 GESU Catholic Church 8 SunTrust Bank

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33 F igure 3 1. Miami central business district (CBD) study area

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34 F igure 3 2. Metered vs. unmetered street s Figure 3 3. Calculating dependent and independent v ariables

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35 CHAPTER 4 RESULTS The results from the four experiments are provided in this chapter. The results from Experiment 1, Metered vs. Unmetered Streets are displayed in a table format. Ave rage VMT for metered streets are on the left hand column, and average VMT for unmetered streets are on the right hand column. The results from Experiment 2, Regress ion Analysis, are displayed using a correlation matrix and a regression summary output table The tables were obtained by using the data analysis feature of Microsoft Excel. Experiment 3 is a bit differ ent. The results from Experiment 3, Maps, are displayed on maps, an d the maps are meant to show if VMT is more associated with density or transit stops than with parking differentials. The final experiment, Field Work, displays on street occupancy rates and cruising for parking times for downtown Miami in a table format. Metered vs. Unmetered Streets The study area has substantially more VMT on unmetered streets than on metered streets The sum of VMT on unmetered streets was three times greater than the sum of VMT on metered streets (see Table 4 1 ) The average, which carries more significance due to the disproportionate nature of the study area found that VMT on metered streets was lower than unmetered streets by about 200 VMT The results indicate that cruising for parking did not occur in the study area. If cruising for parking did occur than one would expect average VMT to be higher on metered streets than on unmetered streets. Regression Analysis The results from the correlation matrix found that the three independent variables, parking price differentials, jobs per square mile, an d transit stops, have an inverse relationship

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36 with VMT (see Table 4 2) Parking price differentials and transit stops have a weak relationship with VMT, and jobs per square mile have absolutely no relation ship with VMT. The results from the regression significance (0.499 > 0.05) between the independent variables and the dependent variable (see Table 4 3 ) Also, the independent variables only expla in 1% of the variation in VMT. Prior to running the experiments I assumed that parking price differentials and VMT would have a positive relationship, but the results show otherwise. The negative correlation from the correlation matrix and the negative coefficient from the regression summary output mean that as parking price differentials decrease VMT increases. This contradicts the assumption that higher parking price differentials generate higher VMT Transit stops was the only variable where the r esults match the expected behavior. One would expect that as transit sto ps increase VMT would decrease. Overall no association can be made between the independent variables and VMT. Maps The maps were produced to complement the results from the regression analysis. Figure 4 1 shows VMT v s. Parking Price Differentials. The differentials are displayed using a red to blue color scheme that ranges from $13.5 an hour to zero respectively. The white numbers the H ot Spot ID is associated with the ID field in Table 3 2, 3 3, 4 4, and 4 5 The map shows that the s the center of the study are a. It appears that the higher parking price differentials are associated with l ower VMT. For example, the max differential (H ot S pot ID 3) has an estimated VMT of 10,445. On the other hand, the min differential (bottom left garage) has an estimated VMT of 51,620. VMT vs. Employment Density (see Figure 4 2) shows that employment is concentrated i n the center of the study area The results do not support the expected behavior that as employment density increases VMT increases. Lower

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37 VMT was observed in areas where employment density ranged from 61,638 and 96,306. H igher VMT was observ ed in areas where employment density ranged from 3858 to 15,413 jobs per square mile. The final map (see Figure 4 3) VMT vs. Transit Stops, shows that lower VMT can be found around areas with a higher volume of transit stops. In summary, the maps reveal th at VM T was more associated with transit stops than with parking price differentials or employment density. Field Work Unlike Experiment 1, t he data from the field work sh ows that cruising for parking is actually happen ing in the study are a. The test for on street occupancy rates found that four out of street occupa ncy rate of one hundred percent (see Table 4 4 ) spots. There was only one instance, at H ot Spot I D 5, 6, where half of th e metered spots were open. The cruising for parking test found that the average cruising time in the study area was 5: 1 7 minutes, average walking time from the parking spot to t he destination was 5:08 minutes, and the sum of the two makes the total trip 10:25 minutes (see Table 4 5 ) The highest cruising time was observed at the GESU Catholic Church, Dade Common Wealth Building, and SunTrust Bank. The lowest cruising time was fou nd at the Cultural Center, which makes sense since that was also the area where the lowest on street occupancy rates were observed.

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38 Table 4 1. Metered vs. unmetered s treets VMT (ADT lane miles) Metered Unmetered SUM 59,717.48 201,313.44 AVG 3,732.43 3,947.32 Table 4 2. Correlation m atrix VMT Parking Price Differential Jobs Per Square Mile Parking Price Differential 0.154 Jobs Per Square Mile 0.025 0.390 Transit Stops 0.159 0.014 0.037 Table 4 3. Regression summary output t able Regression Statistics Multiple R 0.23 R Square 0.05 Adjusted R Square 0.01 Standard Error 5735.35 Observations 49.00 ANOVA df SS MS F Significance F Regression 3.00 79280491.10 26426830.37 0.80 0.50 Residual 45.00 1480239053.37 32894201.19 Total 48.00 1559519544.47 Coefficients Standard Error t Stat P value Lower 95% Upper 95% Intercept 17435.80 3497.73 4.98 0.00 10391.02 24480.59 Parking Price Differential 386.40 354.81 1.09 0.28 1101.01 328.22 Jobs Per Square Mile 0.00 0.04 0.11 0.91 0.08 0.09 Transit Stops 315.59 307.62 1.03 0.31 935.17 303.99

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39 Table 4 4. On street vehicle occupancy rate s Hot Spot ID On Street Parking Off Street Parking Rate Date Time On Street Occupancy Occupancy Rate 1 NE 3 St between NE 2 AVE & NE 1 Ave 190 NE 3rd Street College Station Garage 1/2 hr Free 1/2 hr 1 $4 1 hr 2 hr $8 Each add hr $4 Max $16 8 Mar 1pm 15 out of 16 93.75% 2 NW 1st Ave between NW 3 ST & NW 2 ST LOT A 3301 LOT B 3302 $4 & $6 Each hr 8 Mar 1:10pm 22 out of 22 100.00% 3 Flagler St between SW 1 Ave & Miami Ave $4 per hr 7 Mar 3pm 17 out of 17 100.00% 3:30pm 17 out of 17 100.00% 8 Mar 12:20pm 16 out of 17 94.11% 4 NE 1st between NE 3 Av & NE 1 Ave Alfred Du Pont Garage 1/2 hr $3 each add 1/2 hr $3 All Day $15 7 Mar 4pm 20 out of 21 95.25% 8 Mar 12:15pm 21 out of 21 100.00% 5,6 Flagler between SW 2 Av & SW 1 Av Metro Dade Center Garage Cultural Center Parking $2 for 1/2 hr $17 daily 7 Mar 5pm 6 out of 11 54.54% 8 Mar 12:35pm 9 out of 11 81.81% 7 NE 2nd St between NE 1st Ave & NE 2nd Ave 190 NE 3rd Street College Station Garage Up to 1 hr $3 Each add hr $2 Max per day $10 Flat rate $5 7 Mar 1pm 15 out of 15 100.00% 1:30pm 15 out of 15 100.00% 8 Mar 12pm 15 out of 15 100.00% 8 SE 1st ST between Biscayne Blvd & SE 2nd Ave Suntrust Garage 1/2 hr $4 Each add 1/2 hr $4 All Day $28 7 Mar 2pm 18 out of 19 94.74% 2:30pm 17 out of 19 89.47% 8 Mar 12pm 19 out of 19 100.00% Note: Monday average is 91.75% and Tuesday average is 95.67%

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40 Table 4 5. Cruising for parking Hot Spot ID Destination Off Street Parking Rate Found On Street Parking At Average search time (minutes) 11 a.m. 3:30 p.m. Total Trip (minutes) Distance Walked (miles) Cruising Walking 1 Wolfson Campus 190 NE 3rd Street College Station Garage 1/2 hr Free 1/2 hr 1 $4 1 hr 2 hr $8 Each add hr $4 Max $16 NW 5 St & NW 2 Av 5.20 5.80 11.00 0.5 2 United States Courthouse LOT A 3301 LOT B 3302 $4 & $6 Each hr 5.20 3.00 8.20 0.3 3 Miami Dade County Courthouse $4 per hr Flagler & Miami Ave 2.50 0.50 3.00 0.1 4 Dade Common Wealth Building Alfred Du Pont Garage 1/2 hr $3 Each add 1/2 hr $3 All Day $15 Flagler & SW 2 Av 6.00 11.50 17.50 0.5 5,6 Cultural Center Metro Dade Center Garage $2 for 1/2 hr $17 Daily Flagler & SW 2 Av 0.00 0.50 0.50 0.05 7 GESU Catholic Church 190 NE 3rd Street College Station Garage Up to 1 hr $3 Each add hr $2 Max per day $10 Flat Rate $5 NE 1 Ave & NE 5 St 8.50 4.50 13.00 0.2 8 SunTrust Bank Suntrust Garage 1/2 hr $4 Each add 1/2 hr $4 All Day $28 SW 2 St & SW 2 Av 6.00 9.75 15.75 0.6 Average 4.77 5.08 9.85 0.32

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41 Figure 4 1 VMT vs. p arking price d ifferentials

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42 Figure 4 2. VMT vs. employment d ensity

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43 Figure 4 3. VMT vs. t r ansit s tops

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44 CHAPTER 5 DISCUSSION The previous chapter detailed the quantitative findings of this research. This chapter is going to contextualize those findings in an attempt to give a greater underst anding of the research question. The underlying purpose behind the thesis was to evaluate the practicality of implementing a performance based parking program in downtown Miami. This was accomplished by asking the following two research questions: (1) is there evidence of cruising for parking in downtown Miami? and (2) her parking price differentials (off street on street parking prices) generate higher VM T, apply to downtown Miami? The two main conclusions that can be drawn from my results are that cruising for parking ory does not apply to the study area Although Experiment 1 and Experiment 4 found contradictory results, the findings from Experiment 4 hold greater weight than the findings from Experiment 1. Experiment 1 found that average VMT was lower on metered streets than unmetered streets. This can mean a number of different things. It can mean that cruising for parking did not occur in downtown Miami during t he time of the experiment. If cruising did occur than one would expect average VMT to be higher on met ered streets than on unmetered streets. It can also mean that curve parking is located on str eets with less traffic. This alludes to the fact that VMT may not be the best or the only dependent variable for measuring cruising for parking. First of all, VMT was derived from traffic counts and traffic park in a garage o r search for on street parking. The limitations of Experiment 1 were addressed by Experiment 4. Experiment 4 collected primary data on on street occupancy and the time it took to cruise for a parking spot The test for

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45 on street occupancy rates found th at the average occupancy rate during a normal business day was 93.71%. That translates into one or two spots being available on any given block within the the average trip, cruising for pa rking + walking to the destination, took about ten and a half minutes. The average search time in Miami is not as bad as other large metropolitan areas. Table 5 1 summaries the results of 16 studies of cruising in 11 cities. Between 8 and 74 percent of t raffic was searching for parking, and it took between 3.5 and 13.9 minutes to find a curb space. The studies that date back to 1927 are not relevant and probably not very accurate, but the recent studies (after 1985) show that cities like Cambridge, Cape Town, New York, San Francisco, and Sydney have an average search time that is about five minutes higher than Miami. New York tops the chart with an average search time of 13.9 minutes and San Francisco and Sydney tie for last with an average search time of 6.5 minutes. Experiment 2 found that the three independent variables, parking price differentials, jobs per square mile, and transit stops statistical sig nificance between the independent varia bles and the dependent variable This contradicts the hypothesis that higher parking price differentials generate higher VMT, and o es not apply to downtown Miami. Although no conclusive evi dence was found on what is driving VMT, nobody can deny that downtown Mi ami does not have tra ffic. The maps from Experiment 3 show that downtown Miami has an abundant supply has shown that these three factors either promote or demote driving, but unfortunately that was not captured by my results.

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46 In summary, d owntown Miami has a slew of other parking problems but this study only focused on one specific parking problem, cruising for parking One of the major limitations of or behind the th eory does not apply to downtown Miami. That does not negate the fact that cruising for parking is happening in downtown Miami, as shown in Experiment 4. Cruising for parking is an issue that ought to be addressed by the Miami Dade Transit Authority, but the issue is not seve re enough to justify implementing a performance based parking program.

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47 Table 5 1. Twentieth century cruising Year City Share of traffic cruising (percent) Average search time (minutes) 1927 Detroit (1) 19% 1927 Detroit (2) 34% 1933 Washington 8.0 1960 New Haven 17% 1965 London (1) 6.1 1965 London (2) 3.5 1965 London (3) 3.6 1977 Freiburg 74% 6.0 1984 Jerusalem 9.0 1985 Cambridge 30% 11.5 1993 Cape Town 12.2 1993 New York (1) 8% 7.9 1993 New York (2) 10.2 1993 New York (3) 13.9 1997 San Francisco 6.5 2001 Sydney 6.5 Average 30% 8.1 Source: 2005 Shoup

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48 CHAPTER 6 CONCLUSION SFPark is still an on going demonstration, so it is premature to make conclusions about the effectiveness of demand responsive pricing for parking. Before evaluating its effectiveness, people need time to become aware of rate differences between blocks, time of day, and/or between on and off street alternatives. These changes do not happen overnight. A thorough evaluation will be conducted in the near future, and if SFPark is found to be a success than other cities will probably implement similar projects in the ir respective downtowns. To other cities, I hope that this thesis laid the foundation for how to think about cruising for parking, how to measur e cruising for parking, and how to statistically measure the relationship between independent variables and VM T. The study was not without limitations. significance between the independent variables and the dependent variable. The fact that downtown Miami is an employmen t center and the results showed that the inde pendent variables only explain 1% of the variation in VMT means that there either has to be something wrong with the data or the way that I set up the regression analysis. As previously mentioned VMT was derived from traffic counts and traffic counts are not available for every road in the study area. Also, the map scale for jobs per square mile needs to be adjusted to a finer granularity. The scale that was used in Figure 4 2 is a default scale that was provided by the U.S. Census Bureau. As for the regression analysis, other focal points such as popular destinations could have been used, and the parameter for summing the adjacent VMTs and transit stops could have been expanded to one or two bl ocks. Shoup (2006), in a publication featured in Transport Policy Getting the price ( pg. 486). Every city can do

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49 it, but not every city should do it. City officials could use the methodology that was dev eloped in this study to see if performance based parking pricing is the right choice for their city.

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50 REFERENCE LIST Downs, A. ( 2004 ) Brookings Instauration, Policy Brief # 128. Downs, A ( 2006 ) Can Traffic Congestion Be Cured? Retrieved from : http://www.brookings.edu/opinions/2006/0630transportation _downs.aspx Ferguson, E. ( 1997 ) The Rise and Fall of the American Car Pool. Transportation 24: 349 376. Flori da Department of Transportation. ( 2009 ) Transportation Statistics RCI Office Handbook. A vailable at : http://www.dot.state.fl.us/planning/statistics/rci/ Giuliano, G. and Small, K. ( 1993 ) Is the Journey to Work Explained by Urban Structure? Urban Studies 30: 1485 1500. Giulian o, G and Small, K ( 1995 ) Alternative Strategies for Coping With Traffic Congestion. Urban Agglomeration and Economic Growth Ed. Herbert Giersch. Heidelberg: Springer Verlag Press, 199 225. Heikkila, E. ( 1994 ) Microeconomics and Planning: Using Simple Diagrams to Illustrate the Economics of Traffic Congestion. Journal of Planning Education and Research, 14, 29 41. Jia, W. and Martin W. ( 1998 ) Parking and Affordable Housing. Access no. 13: 22 25. Lewyn M. ( 2008 ) Why Pedestrian Friendly Street Design Is Not Negligent. University of Louisville Law Review. Miami D owntown Development Authority. ( 2009 ) Downtown Master Plan. Retrieved from: http://gallery.miamidda.com/DDA_Master_Plan_2009.pdf Morrall J. & Bolger, D. ( 1996 ) The relationship between downtown parking supply and transit use. ITE Journal 66(2), pp. 33 36. National Househo ld Transport ation Survey (NHTS). Retrieved from : http://nhts.ornl.gov/ SFpar k. ( 2011 ) Retrieved from : http://sfpark.org/ Shoup, D. ( 2005 ) The High Cost of Free Parking. Planners Press, Chicago. Shoup, D ( 2006 ) Cruising for P arking. Transport Policy 13 479 486. Small, K. ( 1997 ) Economics and urban transportation policy in the United States. Regional Sciences and Urban Economics, 27, 671 691.

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51 T exas Transportation Institute. ( 2007 ) The 2007 Urban Mobility Report. Texas Transportation Institute. September 2007. Available at: http://www.commutercars.com/downloads/UrbanMobility07.pdf Transportation Research Board. ( 2004c ) Transit Cooperative Research Program Report 108: Parking Management and Supply: Traveler Response to Transportation System Changes. Washington D.C. Accesse d from : http://onlinepubs.trb.org/onl inepubs/tcrp/tcrp_rpt_95c18.pdf

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52 BIOGRAPHICAL SKETCH Max Shmaltsuyev was born in Bobruysk Belarus in 1984. He immigrated with his family to the United States in 1990. He grew up in the north suburbs of Chicago and attended high school at Niles North High School in Skokie, IL. After high school Max attended Loyola Uni versity of Chicago and stu died f inance and rece ived a graduate certificate in data w arehousing. Since graduating from Loyola, Max has worked at two separate data warehouse consulting firms before deciding to pursue a m aster d egree in Urban and Regional Planning at the University of Florida. At the University of Florida Max worked as a research assistant and interned for the City of Gainesville. Upon finishing up his coursework, Max m oved back to Chicago to pursue a career in transportation planning, but instead found a great opp ortunity with a business intelligence start up called Clarity Solution Group.