Advances in behavioral science offer a new toolkit of theories, models, and empirical methods for designing transportation programs. At JTL, we fuse them with the latest data technology and analytics to achieve a specific purpose: promoting sustainable travel behavior. We develop methods that allow us to understand user heterogeneity, segment users based on their patterns, and predict individual travel sequences.
We also develop frameworks for generating, implementing, and evaluating different interventions in the transportation system. For example, we have evaluated and designed real-world applications, such as before-the-peak-hour rider discounts for Hong Kong MTR customer; a bike-to-work program in Vancouver; a subway map redesign for the Washington, D.C. Metro; fare subsidies for low-income transit riders in Boston; and incentives for carpooling at MIT.
JTL’s current research in this field includes clustering analysis in Chicago to identify riders who may be susceptible to changing their long-term travel patterns, while developing new types of integrated fare products. We are also evaluating public-transit network gap bridging via e-scooter sharing in Singapore’s downtown area for more direct and convenient connections.
|A Randomized Controlled Trial in Travel Demand Management, , Transportation, p.1-26, (2019)||
This paper presents a trial aimed at reducing parking demand at a large urban employer through an informational campaign and monetary incentives. A 6-week randomized controlled trial was conducted with (N = 2000) employee commuters at the Massachusetts Institute of Technology, all of whom frequently drove to campus. Split into four arms of five hundred each, one group received weekly informational emails highlighting MIT’s various new transportation benefits; a second group received monetary more
|Demand Management of Congested Public Transport Systems: A Conceptual Framework and Application Using Smart Card Data, , Transportation, (2019)||
Transportation Demand Management (TDM), long used to reduce car traffic, is receiving attention among public transport operators as a means to reduce congestion in crowded public transportation systems. Though far less studied, a more structured approach to Public Transport Demand Management (PTDM) can help agencies make informed decisions on the combination of PTDM and infrastructure investments that best manage crowding. Automated fare collection (AFC) data, readily available in many more
|Understanding the Usage of Stationless Bike Sharing in Singapore, , International Journal of Sustainable Transportation, (2018)||
A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted more
|Demand Management in Public Transportation: A Framework and Application, , Working paper, (2018)||
Transportation demand management (TDM), long used to reduce car traffic, receives increasing attention as means to ease congestion in overcrowded public transit systems. A more structured approach to transit-specific TDM can help agencies find better combinations of demand management and infrastructure investments to satisfy customer need. This paper develops a framework for public transportation demand management (PTDM) including problem identification and formulating program goals, program more
|Redesigning Subway Map to Mitigate Bottleneck Congestion: An Experiment in Washington DC Using Mechanical Turk, , Transportation Research Part A, Volume 106, p.158–169, (2017)||
This paper explores the possibility of using subway maps as a planning tool to influence passenger route choice to mitigate congestion. Specifically, it tests whether extending the appearance of an overcrowded subway line on the Washington DC subway map would encourage passengers to use other underutilized lines. The experiment was conducted through the Mechanical Turk, a crowdsourcing platform, with 3056 participants, producing 21,240 route choice decisions on the official and six more
|Reducing Subway Crowding: Analysis of an Off-peak Discount Experiment in Hong Kong, , Transportation Research Record: Journal of the Transportation Research Board, Washington, D.C., (2016)||
Increases in ridership are outpacing capacity expansions in a number of transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system, getting more out of the capacity they already have. This paper uses Hong Kong's MTR system as a case study to explore the effects of crowding-reduction strategies as well as methods to use automatically collected fare data to support these measures. MTR introduced a pre-peak discount in September more
|"Nudging" Active Travel: A Framework For Behavioral Interventions Using Mobile Technology, , Transportation Research Board 93rd Annual Meeting, Washington, D.C., (2014)||
Advances in behavioral economics have begun to provide a new toolkit of theories, models, and empirical methods for designing and evaluating policy. While many of these techniques are highly relevant to behavioral problems that planners encounter when consulting with the public, crafting policy and regulations, and promoting sustainable patterns of behavior, it has received only limited attention in the planning and transportation literature. The authors review this literature and present a more
|A WebApp Design to Implement Travel Behavioral Nudging using MOVES, , Transportation Research Board 93rd Annual Meeting, Washington, D.C., (2014)||
The democratization of ICT in the form of GPS, motion detection technologies, and internet connectivity in smartphones has led to a proliferation of mobile applications which can detect and record an individual’s travel behaviors. Compared with common methods of collecting transportation data, such as travel diaries and single-purpose gadgets (e.g. pedometers), the use of smartphone features can make data collection both more accurate and easier for both researcher and participants. In order more