San Diego: Transit Oriented Development or Sprawl?
San Diego has one of the largest and arguably one of the most effective light rail systems in the United States. It is widely acknowledged as one of the leaders of Transit Oriented
Development (TOD) and is an example to many other regions as to effectively implement light rail transit systems within a city. Even with such impressive transit development, 85 percent of the population living in San Diego still uses automobiles as their primary means of transportation. The main reason why the large majority of San Diegans still rely heavily on their automobiles is due to the fact that San Diego is one of the largest sprawl cities. Urban sprawl makes transit systems such as light rail highly ineffective and costly. People that find it necessary to travel into low density areas within San Diego cannot rely on public transit, due to unavailability. It is not economically feasible to create effective transit lines, such as light rail, into low density areas, due to the lack of usage. Light Rail systems are most effective in high density areas, where people can effectively travel from their living areas to places of work, leisure, and commercial businesses. This becomes increasingly important when taking into account that regional authority’s estimate that Downtown San Diego will increase population size 150 percent by the year 2050 (Freemark 2010).
Mid-Coast Corridor Transit Project
San Diego’s newest light rail transit project is called the Mid-Coast Corridor Transit
Project. This new extension of the light rail system will be a new line that starts at the Old Town
Transit Center in Downtown San Diego all the way up North to the University of California San
Diego and the surrounding University City. This 11 mile route will cost 1.2 billion dollars and is expected to be completed in 2016 (SANDAG). The Mid-Coast Corridor Transit project is an
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example of an inefficient light rail route. The University City area is not densely populated enough for people to rely solely on public transit, therefore making the light rail system inefficient compared to other, more densely populated areas. San Diego’s light rail transit system should have rather been expanded east of Downtown San Diego in the highly populated areas of
University Heights and National City. Both of these areas have a high density population, creating prime conditions for light rail transit lines. In order to support the idea that the Mid
Coast Corridor Project is not the most efficient area for a light rail project and that its allocated funds would have been better used for an expansion of the light rail system into more dense corridors, one must answer the following question: What factors take part in creating an efficient light rail system and does the Mid-Coast Corridor Project take such factors into account?
The study of integrating light rail transit into the dense corridors east of Downtown San
Diego will provide insight into the relationship between rider participation and dense urban areas, elaborate on the positive and negative aspects to light rail transit lines in urban cores, and to challenge the opinion that the Mid-Coast Corridor Transit Project was the most economical and efficient choice for a new light rail system in San Diego.
Literature Review
It is important to understand the relationship between rider participation and population density in order to maximize the efficiency of light rail transit systems. There is factual evidence that there is a relationship between walking access and public transit participation. The less amount of time it takes to walk to the transit stations, the more likely people will utilize it. Dense areas have higher concentrations of people living in a relatively small area, providing an opportunity to create access points close to residential areas (Alshalalfah & Shalaby 2007, 114).
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With this information, it becomes evident that light rail transit must be implemented in areas that have a high density of people living in a relatively small area. By doing so, rider participation would be maximized. The Mid-Coast Corridor Transit Project will be implemented in an area which is not as densely populated in comparison to other areas in San Diego. Population density is a factor which must be taken into account when implementing a light rail project; the Mid-
Coast Project was obviously not planned with population density in mind. This will be discussed in more detail in the findings and analysis section of this paper.
Studies have been done which prove that certain aspects of the Mid-Coast Corridor
Transit Project is not the most efficient implementation of a light rail system. “Light rail with its closely spaced stations attracts more riders per station when it is located in denser residential areas”
( Davis & Seskin 1996). One positive aspect of light rail transit is its closely spaced stations. By creating stations close to one another pedestrians can travel short distances within the city without worrying about driving. Studies have been conducted which support that light rail systems are best placed in high density areas in order to maximize efficiency
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Light rail, with its relatively short lines, is most effective in attracting passengers when stations are in higher-density residential areas” (Davis & Seskin 1996). The Mid-Coast Corridor Project’s light rail stations are not closely spaced stations, but rather unevenly spread throughout light rail line.
By having uneven and far spaced stations the corridor project will not attract the maximum riders per station and will therefore not be the most efficient. The Mid-Coast Corridor Project can be seen in Figure 1, located in the appendix of this paper. Another study, in the article “Effects of
Urban Density on Rail Transit,” was able to show where the most cost efficient rails are placed and what aspects make them so cost efficient:
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Cost-efficiency is measured by annual operating costs plus depreciation per vehicle mile. Effectiveness is measured by daily passenger miles per line mile.
For light rail, these measures indicate a strong positive relationship with employment size and residential density.
This suggests that medium to large cities with higher density corridors work best for light rail (Davis & Seskin 1996).
The study concludes that the most efficient light rails, when taking into account operating cost and daily passenger miles, are transit lines that are in high density areas.
Denver Colorado, like San Diego, is expected to exponentially increase in population size within 25 years. In order to cope with the expanding population the city has implemented transit oriented development plans. One of their large projects is a large light rail transit program that will span 120 miles. Denver has put a large emphasis on land use and transportation planning.
“[We] were quick to see that construction of such a comprehensive transit system provided unprecedented opportunities to catalyze transit-friendly, transit-efficient development capable of absorbing substantial growth”(Promising Practices in Transportation Efficiency 44). Denver has decided to create light rail transit in dense population areas, in order to absorb large amounts of what would otherwise be private transportation. SANDAG should have taken the same approach as Denver took, by implementing transit lines in dense areas. By doing so traffic congestion is alleviated, while giving the potential for maximizing rail car occupancy. The major problem that
Denver encountered was coming up with enough funds in order to implement light rail transit.
Although very efficient, light rail transit is expensive, Denver was able to overcome their financial problem by funding a campaign to increase sales tax, which then passed.
Another dense urban area that has created a very successful light rail system is Salt Lake
City Utah. Similar to San Diego Salt Lake City’s population is projected to increase by one million people by the year 2030, and the travel demand in the area is going up 4 percent every year. With such figures, some type of public transit had to be done in order to alleviate
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congestion and to promote smart urban growth with an emphasis on public transit. In the beginning there was resistance from the population to implement light rail projects due to cost, living displacement, and land values. After campaigning and enlightening the public about the benefits of light rail, Salt Lake City eventually was able to commence the light rail transit project. Through the light rail project Salt Lake City was able to create, “improved mobility, reduced traffic, and drove high-quality transit oriented development” (Promising Practices in
Transportation Efficiency 73). The very first month that their new transit line was operational rider participation surpassed estimates by 30 percent, and 45 percent of the riders were new to public transportation. These statistics are representative of a dense population area, where there is a direct correlation between urban density and rider participation. Unfortunately such impressive statistics will not be evident with the Mid-Coast Corridor Project due to the lack of population density in the proposed Mid-Coast area.
Maximizing light rail efficiency in San Diego
Transit oriented development, such as light rail transit, is most effective and efficient in dense areas. East/ Inner city San Diego has many dense urban areas which would be prime candidates for light rail transit. These dense areas are becoming more desirable for homeowners, there has been, “a doubling of the demand for homes within walking distance of stores and an increase in buyers who prefer dense, compact homes (this market segment is expected to account for 31% of homeowner growth between 2000 and 2010)” (Arrington 196). It is important to create efficient public transit in dense areas not only for the current population, but also for the future increase. By analyzing census data and researching population projections one can further show the benefits of installing a light rail system to the inner city area.
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Creating a light rail transit system to connect Downtown San Diego with inner city areas would have many positive aspects, but it could have a downside as well. The installation of a light rail system would improve living conditions, as well as, create monetary savings due to congestion relief, provide economic stimulus of Federal/State funds, improve air quality, greater labor force participation, reduce energy consumption, expand tourism, improve traffic safety, increase mobility, and provide higher economic use of developable land (Cervero & Duncan
2002, 2). The major negative aspect to a light rail system is the initial cost of the project. The allocation of such a large amount of money on an individual public transit line puts budgetary pressure on other transit projects in the area. Although the initial cost of the project is a very large sum of money, this can be overcome, as has been shown from the Colorado and Utah light rail system’s case studies. The positive aspects of light rail transit greatly outweighs the negative budgetary aspect. But in order to maximize the efficiency of the system, planners and developers must research the factors that go in to making an efficient light rail transit system, SANDAG seemed to greatly overlook these factors, which have been discussed throughout the literature review, when planning their Mid-Coast project.
Research Strategy
Various research methods were employed to find the data I utilized for my research project. An invaluable recourse that I utilized is ArcGIS (Global Information Systems).
SANDAG’s website has GIS files for all the existing light rail transit lines along with user data sets which can be utilized to find which existing light rail stations are accessed most frequently.
With this information, dense population access points were compared to less dense access points in order to find where light rail transit is used most heavily. With GIS data I was able to integrate transit user data with light rail routes and maps of the region through ArcGIS software. By being
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able to map out where the highest user participation is located along with dense urban areas, I was able to support my hypothesis that the Mid-Coast Corridor Transit Project is not the most efficient implementation of light rail transit. The money and resources used to create the light rail transit system would have been better used in the more densely populated areas of San Diego.
Another resource that benefitted my project is my internship position with Southeastern
Economic Development Corporation (SEDC). SEDC is a redevelopment organization that focuses on the Southeastern region of San Diego, including parts of University Heights and
National City. By working with my employers I have had the opportunity to get a better understanding of East San Diego’s dense communities. Also, through my internship opportunity
I have been able to work with ArcGIS software, which helps me better analyze the Mid-Coast
Corridor Transit project. I have been able to gain real world experience and information from my employers, along with a better understanding of the region. Statistics have also been valuable to me, by researching various light rail systems in different cities, I have found where and for what reason these transit systems have been successful or unsuccessful. Lastly peer reviewed articles and case studies have been an invaluable part of my research. There have been many case studies on light rail transit systems in various cities, some which are very successful and others which are not as much. I have been able to read these numerous articles in order to find relationships between various efficient light rail transit systems. By using the data from various case studies I was able to more accurately analyze GIS maps that I have created. The research methods which I chose to use had a few minor short comings. Firstly, although ArcGIS software if very accurate, sometimes the data sets that are available are either out dated or not pinpoint accurate. This could affect any spatial analysis that was performed on GIS maps. An example of this would be the accuracy of dot density maps that I have used in order to see both the current population of San
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Diego, and the projected population for 2030. Another shortcoming that my research methods may have had is that I relied on other city’s light rail transit for statistical information in order to correlate where the most efficient placement of light rail transit is, and what factors go into creating an effective light rail transit system. There can be problems with correlating other cities’ statistical information with one another in order to find maximum light rail transit efficiency. Not all cities are enough alike in areas such as: residents, layout, occupations, and income levels to effectively group them together in order to find similar traits. Some cities have efficient light rail transit systems, which just would not work well in any other city. I tried minimize my chances of running into this problem be reviewing cities that have similar traits.
Findings and Analysis
Identifying the percentage of the population served by a transit system entails taking into account different measures. The first thing that must be identified is a service area. A service area is the area around a transit station where potential riders will be drawn to the station. The common distance that residents are willing to walk to a transit station is .25 miles. A large portion of research has been conducted which state, that although walking distance has a contributing factor as to how many people will utilize light rail transit, access is also very important to take into account. “The more people residing, and/or employed around transit stations, the greater the probability the service will be used” (El-Geneidy 2009, 3). Areas with dense population will have more rider participation in comparison to lower populated areas, which in turn helps to maximize the efficiency of light rail transit. One major element that must be taken into consideration when analyzing walking distance is that pedestrians want to minimize both their walking distance and time it takes to get to the station. There are other characteristics that affect walking distances. Population and dwelling density have a positive
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effect on walking distance, while higher household income and a greater number of vehicles available have a negative effect” (El-Geneidy 2009, 4). Barriers also effect rider participation.
Barriers, such as unclear walkways, lack of parking spots at station, walls, and obstructed views make it difficult to access stations, and therefore affect usage in a negative way. The Mid-Coast
Corridor Transit Project, Figure 1, does not clearly take into account all of these contributing factors. The red route depicted in Figure 1 shows the Mid-Coast Corridor’s proposed route. The route starts at the Old Town Transit Center and then goes to the University City area. This proposed route is contributing to urban sprawl. Urban sprawl is the spreading outward of a city’s population into the suburbs and surrounding area. It is a very inefficient way to create cities, due to its rapid and unplanned nature, the land that is used for the sprawling growth was not developed so for maximum efficiency. Figure 2 depicts a 2010 dot density map made through
GIS software. The map clearly shows the population of both the University City area as well as the population density of East San Diego. Along with dot density, figure 2 portrays the Mid-
Coast Corridor Transit Project and the proposed transit stations. University City has predominately low levels of population density with most areas falling between the .5-2 housing units per acre (El-Geneidy 2009, 4). This is the same area the Mid-Coast Corridor Transit Project will be implemented as can be seen in figure 2. By analyzing the dot density map in relation to the proposed route, one can clearly see the population density where the Mid-Coast Project is going to be implemented is much more sparse than many areas in San Diego, such as in the east.
It becomes obvious that this area does not have a dense population of people, which is a major contributing factor to an efficient light rail system. Without a dense population of people living within the community, rider participation would not be at an efficient level. Since population density is such an integral part of light rail efficiency, more research had to be done in order to
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further critique the Mid-Coast Transit Project. By compiling data, I have also been able to create a 2030 projected population density map with GIS software (Figure 3). This map is able to clearly show that the Mid-Coast Corridor Transit Project does not utilize the best area to implement a new light rail system. Population densities in East San Diego are projected to triple in some areas, whereas the University City area has little growth. It makes little sense to create a transit system in an area which is not too dense, and there is little in the way of projected growth.
SANDAG should have seen these factors while implementing a new light rail system, by not doing so they are unable to maximize efficiency.
A study in 1995 showed that both residential and commercial property values near light rail transit stations in San Diego benefitted from a 272 dollar increase for every hundred meters the property was to the light rail station (El-Geneidy 2009, 5). Light rail transit systems affect property values. The closer a property is to a light rail transit system the more the property goes up in value. Income levels in the University City area are substantially higher than in the East
San Diego region. Higher income levels of a population correlate to higher property values.
Figure 4 shows the median income level of the San Diego region, along with the proposed Mid-
Coast Corridor Transit Project. In order to better benefit the population of San Diego, SANDAG should have created a light rail system in an area with lower median income, this has multiple purposes. Firstly, lower income areas directly correlate with lower property values. By creating a transit system in lower income areas, property values will benefit and prices will rise, by doing so low income areas of San Diego will be revitalized. Secondly, higher income residents are much more likely to use private vehicles to get to their places of work and leisure. If private vehicles are utilized, public transit will not be used as efficiently. Lastly, light rail transit has shown to increase commercial property values, along with the businesses associated with the
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property. Figure 4 depicts that the University City area has some of the highest median income in the San Diego region, and would therefore not be an efficient area to place a light rail transit line.
Whereas, East San Diego’s inner city has some of the lowest median income areas which would be a prime area to maximize light rail efficiency. The lower median income areas of San Diego would be the best place to build light rail transit. Property values within these lower income areas would rise with light rail transit, rider participation would be maximized, and congestion would be lessened creating a more desirable way to live, and ultimately reinvigorating areas of dilapidation.
Conclusion
Transit oriented development is necessary for San Diego to thrive. Light rail transit is a great and efficient form of public transit, which is perfect for dense areas where people can substitute private transportation for public. Through different research methods, I have been able to conclude where the most efficient areas are for light rail development. Population density is a very large component in creating an efficient light rail system. The Mid-Coast Corridor Transit
Project does not have a dense enough population to maximize light rail efficiency as has been shown with GIS analysis. Access to transit stations also affects the efficiency of a light rail system, by creating easily accessible stations in which people can travel easily throughout the city. The money allocated to the Mid-Coast Corridor Project would have been better spent on light rail transit systems in East San Diego’s inner city areas. Residents of these areas have lower income rates and need public transportation in order to offset costs of private automobiles.
Property values in these areas would also rise, ultimately benefitting the residents and the community. This study gave more insight into user participation and dense urban corridors in relation to transit station access, income levels and population densities. The Mid-Coast Corridor
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Transit project is an example of a light rail system that has not been fully researched in order to maximize efficiency. This research has further supported the hypothesis that the SANDAG light rail system should have expanded into East San Diego, rather than the planned University City area. By incorporating new light rail transit systems to the existing downtown station, dense areas could be integrated with the many benefits light rail public transit brings.
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Appendix
Figure 1
SANDAG Mid-Coast Corridor Project www.sandag.org
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Figure 2
2010 Population Dot Density Map with Mid Coast Corridor Project
(SANDAG Layers)
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Figure 3
2030 Projected Population Dot Density Map with Mid Coast Corridor Project
(SANDAG Layers)
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Figure 4
2010 Median Income Map with Mid Coast Corridor Project
(SANDAG Layers)
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