Abstract

This dissertation examines the relationship between transportation access and industrial and office property rents. The primary purpose of this research is to evaluate two sparsely studied topics in the transportation-land use literature: the impacts of light rail transit on property values, and the effect of transportation facilities on non-residential land uses.

Multivariate regression analysis is used on longitudinal data for approximately five hundred and twenty office properties and five hundred industrial properties collected from the San Diego metropolitan region over the period from 1986 to 1995. Asking rents ($/square foot/month) is the dependent variable. Straight-line distance of each property to the nearest freeway on/off ramp, the nearest light rail station, and to the San Diego central business district provide measures of access. Other independent variables include building and neighborhood characteristics.

The findings show that access to freeways is consistently significant in predicting office rents. This result indicates that freeways are important in shaping office property values, and by extension office land use patterns. Light rail transit did not have a significant effect on office rents. Access to the CBD was only significant for downtown office properties. The CBD variable in this case may be a proxy for the effect of localization economies. None of the measures of access was significant for industrial properties.

This research underscores the importance of refining measures of access in order to capture and better understand the transportation-land use relationship. In particular, if the distance of an industrial firm to freeways, light rail transit, and the CBD is not important, then what kinds of access do matter? This research also has important implications for planning light rail transit systems. There is strong evidence that light rail systems do not provide enough travel cost savings to increase non-residential property values. This finding should be taken seriously in planning alignments for future light rail systems. Light rail systems need to be aligned with existing activity centers, rather than expected to stimulate new development or the redevelopment of distressed urban areas.

Abstract

Mobility systems are undergoing a major transformation due to emerging autonomous and shared mobility technologies. A primary aspect of such technologies is improving the mobility system inefficiencies via a reduction in the number of vehicles needed to fulfill the transportation needs. This would impact the use of vehicles and their expected lifetime. This dissertation is focused on the importance of the increased usage of vehicles and how the system can benefit from an optimization with a vehicle point of view. The improvements come from mainly two aspects of shared mobility – carsharing and ridesharing – which are both implemented in the modeling and optimization framework. An analysis of the current vehicle ownership and trip distributions is presented. A vehicle usage cost function is designed to incorporate the changed relative importance of fixed and usage-based variable costs. It presents a framework that analyzes the interactions between all the elements, including a pricing scheme for benefit-cost analysis and optimizations from a service provider perspective. With shared mobility, ownership paradigms can also change to subscription-based use of vehicles from fleet service providers, as included implicitly in the interaction framework. Modeling is carried out for idealized networks, as well as a real-world network of a reasonable size from the city of Irvine, CA. The results capture the increased use of shared and/or autonomous vehicles and the benefits of optimizing the system with properly updated costs. Results and conclusions are provided on the viability of service provider plans as well as on system benefits in terms of the replacement ratio indicating how many personal vehicles can be removed using autonomous fleets.

Abstract

There have been laws passed in California (SB32) that would require the State to cut its Greenhouse Gas Emissions (GHG) to 40% of 1990 levels by 2030 to combat climate change. With cars contributing to 43% of GHG emissions in California to reach that goal there will need to be a significant reduction in Vehicle Miles Travelled (VMT). A way to quickly reduce VMT is to invest in existing rail systems specifically commuter rails. An investigation was conducted to model the potential effects of improving commuter rail services on a state vs. national level, station-by-station level, and a regional level. To conduct the research data was gathered from the National Transit Database, Longitudinal Employer-Household Dynamics site, and the Environmental Protection Agencies Smart Location Database (EPA-SLD) for the year 2014. The California Model unlinked passenger trips are more sensitive to the hours of service than the National Model. Also, the California Model is more sensitive to log peak vehicles operated which would imply that the more vehicles or frequency of the vehicles servicing people can have a large impact on passenger trips. The Station boarding and egress models were the best when there were exogenous latent variables in the regression model. The latent variables Mixed-Use Density and Work Opportunity play a significant role in transit boardings and egress by stating that if the mixed-use density increases the employment, employment entropy, and ratio of jobs accessible in 45 minutes increases. Model 2 is superior of the SEM models created. The ridership factors that the passenger rates to all the observed variables and the measure of their satisfaction with the variables can be a tool to use for improving service quality and for planning for future services. In the long run, this could have cost savings because if there is information about the riders’ preferences there can be improvements made specific to what is valued as important. This model can be easily modified to fit other transit services in many different regions or countries because of the framework structure which can be used for analyzing any type of service from survey responses.

Abstract

Smart Urban Mobility in the future demands a paradigm shift. Transportation supply needs to be designed to incorporate individual-level preferences in an era of readily-available information about other users and network performance. It is, therefore, reasonable to expect that an individual would have information to compare his/her transportation allocation with other users. For individuals having the same goal (e.g., the shortest path to the destination from the same departure location and time), the peer to peer comparison may induce ‘envy’ if the user perceives his/her assigned travel option to be worse than that of his/her peers. In turn, a user may adjust his/her travel options until he/she does not feel envy. This concept is an extension of the well-known travel behavior assumption called “User Equilibrium”. Existing behavior models, however, do not allow users to compare their allocations with others on an individual basis. Furthermore, it is assumed that users have perfect information about their own alternative and all users are homogeneous. A smart mobility system of the future may also include users who are not human but machines such as logistics, an autonomous vehicle that may have programmed behavior, and thus they too can be considered “agents” in our analysis. This dissertation is dedicated to modeling a smart mobility system which accounts for individual level of allocation. Mobility systems that include connected, autonomous, and subscribed components to various extents will all qualify as smart systems in this context. More specifically, we focus on the optimization of the allocation problem to achieve both system-wide efficiency and minimum envy among individuals. We consider envy to be an important allocation aspect in the transportation system. Maximizing the efficiency of a system necessarily brings about some level of unfairness where some users (or agents) are allocated to inferior alternatives. When agents having superior alternatives can compensate the envy of groups having inferior alternatives, an envy-free state can be achieved-which can be shown to be Pareto efficient state. Using a combination of pricing and incentives, we propose an optimization model to arrive at this new equilibrium. This research has significant contributions in that the proposed model provides a framework to combine system-wide objectives with individual users’ utility objectives. Furthermore, we consider user heterogeneity, which has not been researched in the general area of transportation assignment. The proposed optimization model can be applied to pricing strategies both for commercial and public agencies, who have real-time information about customer characteristics and system performance. Numerical results from running our optimization on both illustrative and real networks show that the proposed model converges to both envy-free and system optimum states with appropriate allocation and pricing schemes. Our findings show that the proposed smart mobility system technically works efficiently without governmental subsidy since the budget-balance mechanism trades off credits among users. In addition, the level of user heterogeneity affects the amount of credits charged or disbursed.