Location-Based Service Data for Transit Agency Planning and Operations: Market Scan and Feasibility Analysis

This report provides a primer on location-based service (LBS) data and its uses in public transportation. It defines LBS data, describes the techniques for collecting and processing the data, and the key parties involved. The report highlights opportunities, limitations, and potential risks of using LBS data, based on the literature and interviews with transit agencies, data providers, and data privacy experts. Finally, this report provides recommendations to transit agencies on the prudent, safe, and effective use of LBS data

System Dynamics Models of Automated Vehicle Impacts

693JJ320N300046The many potentially transformative changes to the transportation system, such as automated vehicles, electric vehicle adoption, increased telework, and new travel modes, are creating increasing uncertainties for the future. These uncertainties call for fast, flexible models. System dynamics (SD) is emerging as a research modeling focus area for changes to the transportation system that may have transformative impacts, including those from vehicles using automated driving systems (ADS). System dynamics provides both qualitative methods to bring diverse stakeholders to a common understanding of the problem, and quantitative methods for modeling complex systems that consider feedback effects and changes over time. Qualitative methods include those for representing systems, such as causal loop diagrams, and for collecting information to determine that representation, such as working with stakeholders via group model building techniques. This project developed causal loop diagrams for several \u201cbuilding blocks\u201d (archetypes) that affect how automated vehicles might be used. These building blocks include new product adoption, sustainability of business model, mode choice, scale effects, congestion and residential relocation

Household moving and tenure behavior : translating retrospective "Recent Mover" surveys into prospective moving decisions

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 263-270).To assist policy makers with evaluating urban development policies and anticipating trends in the evolution of cities, researchers have significantly improved modem urban land-use-and-transportation (LUT) simulations. Despite extensive studies regarding the interdependency of household life cycle stages and moving decisions in demography, most existing LUT simulations do not address households changing life cycle stages when modeling residential relocation behavior. The reasons include 1) the data that capture households and housing transitions is hard to obtain, and 2) the analysis methods are mainly for cross-sectional datasets. This dissertation focuses on these issues and contributes to the literature in three respects: behavior exploration, methodology, and applications to housing and transportation policy analysis. The ultimate goal of this study is to have a better understanding of the relationship between household life cycle stages and their moving decisions when the housing market is heavily regulated with incentives based on age, family structure, and income. This research focuses on the housing market in Singapore as a case and utilizes a new dataset of recent movers. First, this study generates sampling weights both at the individual and household levels to correct sample bias. Then, this study uses discrete choice models to identify key household and housing factors that influence households' moving behavior at the household-level. In order to capture household characteristics at the time of decision-making, the household characteristics for those households that changed structure when moving had to be reconstructed. The results show that household moving decisions are mainly influenced by three sets of factors: life cycle stages, tenure choices and housing submarkets. Finally, this research adopts a Markov Chain Model (MCM) approach to estimate a set of forward-looking moving and tenure transition rates accounting for various issues, such as sample bias and "missing-move" problems. The final results improve the estimate of moving and tenure transition rates in several ways: adding more demographic factors, handling household structure changes, and relaxing the memoryless assumption to accommodate a special feature of the public housing sector in Singapore. I expect that this study will have important implications for LUT microsimulations as well as housing and transportation policymaking. It demonstrates a method to analyze a retrospective dataset of recent movers in order to obtain detailed forward-looking moving and tenure transition rates (which are required for microsimulations). It also demonstrates a way to model household structure changes at the household level without introducing a full set of demographic models at the individual level. This study shows that with detailed moving and tenure transition rates, researchers can better capture the critical interactions between households' moving decisions and government intervention on the housing market. This can improve the current LUT simulations in a way that they can be more sensitive to government housing regulation and support long-term policymaking regarding the spatial distribution of housing and transportation infrastructure.by Jingsi Xu Shaw.Ph. D

Car-lite impacts on housing market and vehicle ownership

In this paper, the authors examine the sensitivity of housing market behaviors and identify the key factors and market forces that result from the adoption of car-lite neighborhoods. In particular, the authors seek to answer the following questions: How will households’ perception of transportation system performance change due to car-lite policies such as autonomous vehicle (AV) implementation? How will households’ long-term decisions change (e.g. residential location and vehicle ownership)? How will the urban form change in response to the changes of agents’ attitudes/preferences regarding AV services

Representing Accessibility: Evidence from Vehicle Ownership Choices and Property Valuations in Singapore

This paper compares the relative performance of different measures of accessibility in relevant models. Specifically, the authors formulated three measures of accessibility: gravity-based accessibility, an aggregate measure of potential; trip-based accessibility, a disaggregate, utility-based measure of the value of travel alternatives; and activity-based accessibility, a theoretically richer disaggregate, utility-based measure of the value of alternative activities (including travel). These accessibility measures were used as explanatory variables in household vehicle ownership models and real estate market price models, comparing the explanatory power of each accessibility measure in each model as expressed by the confidence in the coefficient estimates and captured by the models’ goodness-of-fit statistics. It was found that trip-based accessibility best represents preferences for accessibility in both vehicle ownership decisions and property valuations. This supports the theoretical value of disaggregate, utility-based accessibility measures over aggregate, potential-based measures. The fact that trip-based measures perform better than activity-based accessibility measures underscores several empirical and technical limitations. Finally, the authors noted that accurately representing accessibility preferences requires congruence between the granularity of the accessibility measure and that of the explained behavior. This emphasizes the importance of understanding what accessibility measures actually capture and ensuring that they align with the analysis purpose. Accessibility has attracted increasing interest as transportation and planning professionals have shifted their attention to the interactions between the transportation system and the larger urban system. Its importance and usefulness as an urban performance measure have been demonstrated widely. Glaeser noted that “all of the benefits of cities come ultimately from reduced transport costs for goods, people and ideas” (1). Although Glaeser did not use the term accessibility, his quote mirrors the accessibility concept in emphasizing the interaction between the transportation system (reduced transport costs) and the activity and land-use systems (goods, people, and ideas), as opposed to one or the other in isolation. The benefits Glaeser refers to can take many different forms. Investments in transportation infrastructure are often seen as a means of generating economic development. However, for project evaluation these benefits have traditionally been measured in travel time savings. Accessibility offers a more nuanced perspective aligned with the transportation as a derived demand paradigm. Specifically, accessibility captures how the transportation and land-use systems interact to generate opportunities for individuals (e.g., access to employment and other urban amenities). Essentially, accessibility is the raison d’être of an urban system. In practice, however, it remains relatively abstruse. Even professionals within the fields of transportation and urban planning are not always clear—or at least not in agreement—about what it encompasses or how it should be measured. Numerous increasingly sophisticated and complex measures of accessibility have been proposed, but how well can we operationalize them? Do they improve our abilities to predict relevant behaviors in the system? This paper examines how accessibility performs in predicting household vehicle ownership choices and estimating residential property valuations. Specifically, the appropriateness, advantages, and limitations of three different accessibility measures are explored—gravity-based, trip-based, and activity-based—in representing household and market preferences in models of these long-term choices. This study is part of our recent work in developing meaningful accessibility measures that capture the interaction between the day-to-day performance of the transportation system and people’s long-term choices