This January’s Transportation Research Board meeting will feature ten presentations from members of the JTL, the urban mobility research group led by DUSP Assistant Professor Jinhua Zhao. The papers, on topics ranging from developing population-targeted strategies of demand management to subconscious biases explain bus ridership choices, foreground the behavioral foundations of mobility and transportation systems. Consistent with the lab’s focus on student research, all ten publications involve MIT graduate students—MCP, MST, and PhD candidates—as primary authors.
This year’s JTL papers focus on three research themes: analyzing how emotion and perceptions produce travel choices; modeling travel behavior using new datasets and methods; and developing transport policy tools that are sensitive to behavioral responses. Many were authored in conjunction with students’ graduate theses to help advance the their primary research findings for professional audiences. The Transportation Research Board’s annual meeting is the largest gathering of transportation professionals in the world.
Many of the papers were developed and refined in two courses taught by Professor Zhao: a spring course on behavior-policy connections in transportation, and a year-round seminar in which students present their current research. Students interested in developing their own research agenda are welcome to contact Professor Zhao.
Emotional Travel
Travel experiences and social perceptions produce emotional responses, which in turn produce travel choices. By applying innovations in social psychology to the realm of transport, the JTL is developing a new vocabulary for the emotional motivations behind aggregate travel demand.
Mode-ism: Explicit and Implicit Social Status Bias in Car vs. Bus Choice
Joanna Moody, Gabriel Goulet-Langlois, Lauren Alexander, Jonathan Campbell, Jinhua Zhao
Traditional transportation survey methods can capture self-reported preferences, but not necessarily instinctive perceptions or subconscious biases. Psychological instruments that gauge implicit bias reveal stronger social status biases for car use and against bus use among respondents than reported in traditional survey methods. These implicit biases are significant predictors of users’ actual mode choices, whereas self-reported biases are not.
A Ride to Remember: Experienced vs. Remembered Emotion on Public Transit
Nate Bailey, Patton Doyle, Tolu Ogenbekun, Jinhua Zhao
Behavioral psychologists have found that emotions associated with remembered experiences vary significantly from emotions reported during the experience. In the transit domain, riders in Boston’s subway system report current trips as better than average, but will remember the same trip as more boring, less comfortable, and closer to average when asked several days later.
Normative and Image Motivations for Transportation Policy Compliance
Jake Gao, Jinhua Zhao
Crafting policies that appeal to car owners’ motivations to comply with sustainable transportation policies could yield higher rates of compliance. Here, a case study finds normative and image-based motivations more powerful in determining users’ willingness to comply with Shanghai’s local vehicle licensing policy, though motivations to comply and levels of compliance vary by residency status (hukou) and other sociodemographic characteristics.
Modeling Travel Behavior: New Data and Methods
Describing and modeling travel behavior is constrained by the hard problem of accurately detecting mobility over time, and mode, and large spatial scales. As user-scale data proliferate, the JTL members and colleagues are constantly experimenting with emerging data sets and data collection techniques.
Clustering the Multi-week Activity Sequences of Public Transport Users
Gabriel Goulet Langlois, Haris N. Koutsopoulos, Jinhua Zhao
Transit activity patterns are often observed over daylong and weeklong intervals, but rarely over multi-week periods. A multi-week analysis preserving the ordered sequence of users’ activities discovers new clusters of users in London, including predictable commute patterns that vary by week and a variety of patterned travel not structured by the working day.
Supervised Statistical Learning for Trip Detection Using Sparse CDR Data
Zhan Zhao, Jinhua Zhao, Haris N. Koutsopoulos
Call Detail Record (CDR) data are among the most pervasive records of individual mobility, but methods for inferential trip detection and the limitations of such inferences are still poorly understood. Statistical learning techniques can improve the detection of omitted trip features that are routinely neglected in CDR-based transportation studies.
Cross-City Comparison: Impacts of Madrid-Seville High-Speed Rail on Population Growth
Yu Shen, Jinhua Zhao, João de Abreu e Silva, Luis Miguel Martínez
High-speed rail reshapes accessibility and associates with population growth at the urban regional scale. In the Madrid-Seville region of Spain, HSR-related accessibility has been concentrated in the cities with HSR station, but the impacts on population increases are more varied in these areas.
Behavior-sensitive Transport Planning
Human response to policy design options is one of the most neglected problems in transportation planning and a major barrier to experimentation. In the JTL, we analyze these behavioral effects to improve the efficacy of policy choices, to fully realize their normative implications and trade offs, and to enlarge the domain of plausible responses to transport problems.
Reducing Subway Crowding: Analysis of an Off-peak Discount Experiment in Hong Kong
Anne Halvorsen, Haris N. Koutsopoulos, Jinhua Zhao
Analysis of user groups and systems segments can allow transit operators to develop more targeted demand management tools. In a Hong Kong-based natural experiment, subway commuters were insensitive to new pre-peak discounts and thus its system-wide impact was small, but other users were more flexible and crowding in the most congested links was reduced.
Transit-Oriented Development on the Ground—A Case of Implementing TOD in China
Ruishan Zheng, Jinhua Zhao
Despite major investments in mass transit and real estate across China, examples of TOD in China remain scarce. Interviews with stakeholders uncover the knowledge, financing, and regulatory barriers to its implementation, as well as a need to educate municipal leadership on the benefits of TOD strategies.
Policy Knowledge and Attitude: Can we Improve Policy Acceptance by Informing the Public?
Menghan Li, Jinhua Zhao
Low levels of public acceptance are a barrier to implementing transportation demand management schemes, but it is unclear how received knowledge of such policies shapes public opinion. With respect to the Shanghai license plate auction, greater knowledge of the policy's driving restrictions is negatively correlated with positive attitudes towards the policy, with the least positive attitudes found among those unable to bid on licenses at all.
Ghosts in the machine: The influence of proximity to past rail on current auto ownership
David Block-Schachter, Jinhua Zhao, Yu Shen
Rail infrastructure has well-known, persistent effects on urban form and residential land markets. Correcting for endogeneity, this study finds that rail proximity also has persistent effects on household-level travel behavior, particularly car ownership.