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- Genre: Masters Thesis
Description
It has been identified in the literature that there exists a link between the built environment and non-motorized transport. This study aims to contribute to existing literature on the effects of the built environment on cycling, examining the case of the whole State of California. Physical built environment features are classified into six groups as: 1) local density, 2) diversity of land use, 3) road connectivity, 4) bike route length, 5) green space, 6) job accessibility. Cycling trips in one week for all children, school children, adults and employed-adults are investigated separately. The regression analysis shows that cycling trips is significantly associated with some features of built environment when many socio-demographic factors are taken into account. Street intersections, bike route length tend to increase the use of bicycle. These effects are well-aligned with literature. Moreover, both local and regional job accessibility variables are statistically significant in two adults' models. However, residential density always has a significant negatively effect on cycling trips, which is still need further research to confirm. Also, there is a gap in literature on how green space affects cycling, but the results of this study is still too unclear to make it up. By elasticity analysis, this study concludes that street intersections is the most powerful predictor on cycling trips. From another perspective, the effects of built environment on cycling at workplace (or school) are distinguished from at home. This study implies that a wide range of measures are available for planners to control vehicle travel by improving cycling-level in California.
ContributorsWang, Kailai, M.U.E.P (Author) / Salon, Deborah (Thesis advisor) / Rey, Sergio (Committee member) / Li, Wenwen (Committee member) / Arizona State University (Publisher)
Created2015
Description
The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The objective of this research study was to develop a methodology for calculating a pavement condition index (PCI) based on historical distress data collected in the databases from Long-Term Pavement Performance (LTPP) program and Minnesota Road Research (Mn/ROAD) project. Excel™ templates were developed and successfully used to import distress data from both databases and directly calculate PCIs for test sections. Pavement performance master curve construction and verification based on the PCIs were also developed as part of this research effort. The analysis and results of LTPP data for several case studies indicated that the study approach is rational and yielded good to excellent statistical measures of accuracy.
It is believed that the InfoPaveTM LTPP and Mn/ROAD database can benefit from the PCI templates developed in this study, by making them available for users to compute PCIs for specific road sections of interest. In addition, the PCI-based performance model development can be also incorporated in future versions of InfoPaveTM. This study explored and analyzed asphalt pavement sections. However, the process can be also extended to Portland cement concrete test sections. State agencies are encouraged to implement similar analysis and modeling approach for their specific road distress data to validate the findings.
ContributorsWu, Gan (Author) / Kaloush, Kamil (Thesis advisor) / Zhou, Xuesong (Committee member) / Underwood, Benjamin Shane (Committee member) / Arizona State University (Publisher)
Created2015
Description
Informal public transport is commonplace in the developing world, but the service exists in the United States as well, and is understudied. Often called "dollar vans", New York's commuter vans serve approximately 120,000 people every day (King and Goldwyn, 2014). While this is a tiny fraction of the New York transit rider population, it is comparable to the total number of commuters who ride transit in smaller cities such as Minneapolis/St Paul and Phoenix. The first part of this study reports on the use of commuter vans in Eastern Queens based on a combination of surveys and a ridership tally, all conducted in summer 2016. It answers four research questions: How many people ride the vans? Who rides the commuter vans? Why do they ride commuter vans? Do commuter vans complement or compete against formal transit? Commuter van ridership in Eastern Queens was approximately 55,000 with a high percentage of female ridership. Time and cost savings were the main factors influencing commuter van ridership. Possession of a MetroCard was shown to negatively affect the frequency of commuter van ridership. The results show evidence of commuter vans playing both a competing and complementary role to MTA bus and subway transit. The second part of this study presents a SWOT analysis results of commuter vans, and the policy implications. It answers 2 research questions: What are the main strengths, weaknesses, opportunities and threats of commuter vans in Eastern Queens? and How do the current policies, rules and regulations affect commuter van operation? The SWOT analysis results show that the commuter van industry is resilient, performs a necessary service, and, with small adjustments that will help reduce operating costs and loss of profits have a chance of thriving in Eastern Queens and the rest of New York City. The study also discusses the mismatch between policy and practice offering recommendations for improvement to ensure that commuter vans continue to serve residents of New York City.
ContributorsMusili, Catherine (Author) / Salon, Deborah (Thesis advisor) / King, David (Committee member) / Kelley, Jason (Committee member) / Arizona State University (Publisher)
Created2017
Description
Bicycle sharing systems (BSS) operate on five continents, and they change quickly with technological innovations. The newest “dockless” systems eliminate both docks and stations, and have become popular in China since their launch in 2016. The rapid increase in dockless system use has exposed its drawbacks. Without the order imposed by docks and stations, bike parking has become problematic. In the areas of densest use, the central business districts of large cities, dockless systems have resulted in chaotic piling of bikes and need for frequent rebalancing of bikes to other locations. In low-density zones, on the other hand, it may be difficult for customers to find a bike, and bikes may go unused for long periods. Using big data from the Mobike BSS in Beijing, I analyzed the relationship between building density and the efficiency of dockless BSS. Density is negatively correlated with bicycle idle time, and positively correlated with rebalancing. Understanding the effects of density on BSS efficiency can help BSS operators and municipalities improve the operating efficiency of BSS, increase regional cycling volume, and solve the bicycle rebalancing problem in dockless systems. It can also be useful to cities considering what kind of BSS to adopt.
ContributorsCui, Wencong (Author) / Kuby, Michael (Thesis advisor) / Salon, Deborah (Committee member) / Thigpen, Calvin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Managed Lanes (MLs) have been increasingly advocated as a way to reduce congestion. This study provides an innovative new tolling strategy for MLs called the travel time refund (TTR). The TTR is an “insurance” that ensures the ML user will arrive to their destination within a specified travel time savings, at an additional fee to the toll. If the user fails to arrive to their destination, the user is refunded the toll amount.
To gauge interest in the TTR, a stated preference survey was developed and distributed throughout the Phoenix-metropolitan area. Over 2,200 responses were gathered with about 805 being completed. Exploratory data analysis of the data included a descriptive analysis regarding individual and household demographic variables, HOV usage and satisfaction levels, HOT usage and interests, and TTR interests. Cross-tabulation analysis is further conducted to examine trends and correlations between variables, if any.
Because most survey takers were in Arizona, the majority (53%) of respondents were unfamiliar with HOT lanes and their practices. This may have had an impact on the interest in the TTR, although it was not apparent when looking at the cross-tabulation between HOT knowledge and TTR interest. The concept of the HOT lane and “paying to travel” itself may have turned people away from the TTR option. Therefore, similar surveys implementing new HOT pricing strategies should be deployed where current HOT practices are already in existence. Moreover, introducing the TTR concept to current HOT users may also receive valuable feedback in its future deployment.
Further analysis will include the weighting of data to account for sample bias, an exploration of the stated preference scenarios to determine what factors were significant in peoples’ choices, and a predictive model of those choices based on demographic information.
To gauge interest in the TTR, a stated preference survey was developed and distributed throughout the Phoenix-metropolitan area. Over 2,200 responses were gathered with about 805 being completed. Exploratory data analysis of the data included a descriptive analysis regarding individual and household demographic variables, HOV usage and satisfaction levels, HOT usage and interests, and TTR interests. Cross-tabulation analysis is further conducted to examine trends and correlations between variables, if any.
Because most survey takers were in Arizona, the majority (53%) of respondents were unfamiliar with HOT lanes and their practices. This may have had an impact on the interest in the TTR, although it was not apparent when looking at the cross-tabulation between HOT knowledge and TTR interest. The concept of the HOT lane and “paying to travel” itself may have turned people away from the TTR option. Therefore, similar surveys implementing new HOT pricing strategies should be deployed where current HOT practices are already in existence. Moreover, introducing the TTR concept to current HOT users may also receive valuable feedback in its future deployment.
Further analysis will include the weighting of data to account for sample bias, an exploration of the stated preference scenarios to determine what factors were significant in peoples’ choices, and a predictive model of those choices based on demographic information.
ContributorsArcher, Melissa (Author) / Lou, Yingyan (Thesis advisor) / Chester, Mikhail (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2015
Description
Given that more and more planned special events are hosted in urban areas, during which travel demand is considerably higher than usual, it is one of the most effective strategies opening public rapid transit lines and building park-and-ride facilities to allow visitors to park their cars and take buses to the event sites. In the meantime, special event workforce often needs to make balances among the limitations of construction budget, land use and targeted travel time budgets for visitors. As such, optimizing the park-and-ride locations and capacities is critical in this process of transportation management during planned special event. It is also known as park-and-ride facility design problem.
This thesis formulates and solves the park-and-ride facility design problem for special events based on space-time network models. The general network design process with park-and-ride facilities location design is first elaborated and then mathematical programming formulation is established for special events. Meanwhile with the purpose of relax some certain hard constraints in this problem, a transformed network model which the hard park-and-ride constraints are pre-built into the new network is constructed and solved with the similar solution algorithm. In doing so, the number of hard constraints and level of complexity of the studied problem can be considerable reduced in some cases. Through two case studies, it is proven that the proposed formulation and solution algorithms can provide effective decision supports in selecting the locations and capabilities of park-and-ride facilities for special events.
This thesis formulates and solves the park-and-ride facility design problem for special events based on space-time network models. The general network design process with park-and-ride facilities location design is first elaborated and then mathematical programming formulation is established for special events. Meanwhile with the purpose of relax some certain hard constraints in this problem, a transformed network model which the hard park-and-ride constraints are pre-built into the new network is constructed and solved with the similar solution algorithm. In doing so, the number of hard constraints and level of complexity of the studied problem can be considerable reduced in some cases. Through two case studies, it is proven that the proposed formulation and solution algorithms can provide effective decision supports in selecting the locations and capabilities of park-and-ride facilities for special events.
ContributorsZhu, Nana (Author) / Zhou, Xuesong (Thesis advisor) / Lou, Yingyan (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2016
Description
Bicyclist and pedestrian safety is a growing concern in San Francisco, CA,
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
ContributorsMcIntyre, Andrew (Author) / Salon, Deborah (Thesis advisor) / Kuby, Mike (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2016
Description
With high potential for automobiles to cause air pollution and greenhouse gas emissions, there is concern that automobiles accessing or egressing public transportation may cause emissions similar to regular automobile use. Due to limited literature and research that evaluates and discusses environmental impacts from first and last mile portions of transit trips, there is a lack of understanding on this topic. This research aims to comprehensively evaluate the life cycle impacts of first and last mile trips on multimodal transit. A case study of transit and automobile travel in the greater Los Angeles region is evaluated by using a comprehensive life cycle assessment combined with regional household travel survey data to evaluate first-last mile trip impacts in multimodal transit focusing on automobile trips accessing or egressing transit. First and last mile automobile trips were found to increase total multimodal transit trip emissions by 2 to 12 times (most extreme cases were carbon monoxide and volatile organic compounds). High amounts of coal-fired energy generation can cause electric propelled rail trips with automobile access or egress to have similar or more emissions (commonly greenhouse gases, sulfur dioxide, and mono-nitrogen oxides) than competing automobile trips, however, most criteria air pollutants occur remotely. Methods to reduce first-last mile impacts depend on the characteristics of the transit systems and may include promoting first-last mile carpooling, adjusting station parking pricing and availability, and increased emphasis on walking and biking paths in areas with low access-egress trip distances.
ContributorsHoehne, Christopher G (Author) / Chester, Mikhail V (Thesis advisor) / Salon, Deborah (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2016
Description
Economic inequality is always presented as how economic metrics vary amongst individuals in a group, amongst groups in a population, or amongst some regions. Economic inequality can substantially impact the social environment, socioeconomics as well as human living standard. Since economic inequality always plays an important role in our social environment, its study has attracted much attention from scholars in various research fields, such as development economics, sociology and political science. On the other hand, economic inequality can result from many factors, phenomena, and complex procedures, including policy, ethnic, education, globalization and etc. However, the spatial dimension in economic inequality research did not draw much attention from scholars until early 2000s. Spatial dependency, perform key roles in economic inequality analysis. The spatial econometric methods do not merely convey a consequence of the characters of the data exclusively. More importantly, they also respect and quantify the spatial effects in the economic inequality. As aforementioned, although regional economic inequality starts to attract scholars' attention in both economy and regional science domains, corresponding methodologies to examine such regional inequality remain in their preliminary phase, which need substantial further exploration. My thesis aims at contributing to the body of knowledge in the method development to support economic inequality studies by exploring the feasibility of a set of new analytical methods in use of regional inequality analysis. These methods include Theil's T statistic, geographical rank Markov and new methods applying graph theory. The thesis will also leverage these methods to compare the inequality between China and US, two large economic entities in the world, because of the long history of economic development as well as the corresponding evolution of inequality in US; the rapid economic development and consequent high variation of economic inequality in China.
ContributorsWang, Sizhe (Author) / Rey, Sergio J (Thesis advisor) / Li, Wenwen (Committee member) / Salon, Deborah (Committee member) / Arizona State University (Publisher)
Created2016
Description
Walking and bicycling bring many merits to people, both physically and mentally.
However, not everyone has an opportunity to enjoy healthy and safe bicycling and
walking. Many studies suggested that access to healthy walking and bicycling is heavily
related to socio-economic status. Low income population and racial minorities have
poorer transportation that results in less walking and bicycling, as well as less access to
public transportation. They are also under higher risks of being hit by vehicles while
walking and bicycling. This research quantifies the relationship between socioeconomic
factors and bicyclist and pedestrian involved traffic crash rates in order to establish an
understanding of how equitable access to safe bicycling and walking is in Phoenix. The
crash rates involving both bicyclists and pedestrians were categorized into two groups,
minor crashes and severe crashes. Then, the OLS model was used to analyze minor and
severe bicycle crash rates, and minor and severe pedestrian crash rates, respectively.
There are four main results, (1) The median income of an area is always negatively
related to the crash rates of bicyclists and pedestrians. The reason behind the negative
correlation is that there is a very small proportion of people choosing to walk or ride
bicycles as their commuting methods in the high-income areas. Consequently, there are
low crash rates of pedestrians and bicyclists. (2) The minor bicycle crash rates are more
related to socio-economic determinants than the severe crash rates. (3) A higher
population density reduces both the minor and the severe crash rates of bicyclists and
pedestrians in Phoenix. (4) A higher pedestrian commuting ratio does not reduce bicyclist
and pedestrian crash rates in Phoenix. The findings from this study can provide a
reference value for the government and other researchers and encourage better future
decisions from policy makers.
However, not everyone has an opportunity to enjoy healthy and safe bicycling and
walking. Many studies suggested that access to healthy walking and bicycling is heavily
related to socio-economic status. Low income population and racial minorities have
poorer transportation that results in less walking and bicycling, as well as less access to
public transportation. They are also under higher risks of being hit by vehicles while
walking and bicycling. This research quantifies the relationship between socioeconomic
factors and bicyclist and pedestrian involved traffic crash rates in order to establish an
understanding of how equitable access to safe bicycling and walking is in Phoenix. The
crash rates involving both bicyclists and pedestrians were categorized into two groups,
minor crashes and severe crashes. Then, the OLS model was used to analyze minor and
severe bicycle crash rates, and minor and severe pedestrian crash rates, respectively.
There are four main results, (1) The median income of an area is always negatively
related to the crash rates of bicyclists and pedestrians. The reason behind the negative
correlation is that there is a very small proportion of people choosing to walk or ride
bicycles as their commuting methods in the high-income areas. Consequently, there are
low crash rates of pedestrians and bicyclists. (2) The minor bicycle crash rates are more
related to socio-economic determinants than the severe crash rates. (3) A higher
population density reduces both the minor and the severe crash rates of bicyclists and
pedestrians in Phoenix. (4) A higher pedestrian commuting ratio does not reduce bicyclist
and pedestrian crash rates in Phoenix. The findings from this study can provide a
reference value for the government and other researchers and encourage better future
decisions from policy makers.
ContributorsWu, Feiyi (Author) / Nelson, Trisalyn (Thesis advisor) / Salon, Deborah (Committee member) / Kuby, Michael (Committee member) / Arizona State University (Publisher)
Created2020