Nudging Sustainable Travel

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.


E-scooter sharing to serve short-distance transit trips: a Singapore Case, Zhejing Cao, Xiaohu Zhang, Chua Kelman, Yu Honghai, and Jinhua Zhao , Transportation Research Part A, (2021)

E-scooter sharing provides a last-mile solution to complement transit services, but less was known about its effectiveness in serving short-distance transit trips. We investigate the potential of using e-scooter sharing to replace short-distance transit trips of excessive indirectness, multiple transfers, and long access-egress walking. First, we conducted a stated preference survey on e-scooter users in the Central Area of Singapore and estimated mixed logit models to examine factors more

Behavioral Response to Discounted Fares for Low-income Transit Riders in Boston, Jeffrey Laurence Rosenblum, Jinhua Zhao, Arcaya Mariana, and Steil Justin , Transportation Research Board 99th Annual Meeting, Washington, D.C., (2020)

As public transit agencies across the United States raise fares, transit affordability has emerged a salient equity issue on the political agenda. With few exceptions, transit agencies do not provide means-tested discounts for low-income riders (federal policy only mandates senior and disability discounts). Our research investigates how the cost of public transit influences transit use and access to goods and services among low-income riders, and whether a low-income fare policy instrument more

Bundled Mobility Passes in Chicago: Consumer Preference and Revenue Implications, Apaar Bansal, and Jinhua Zhao , Transportation Research Board 99th Annual Meeting, Washington, D.C., (2020)

Competition provided by “new” mobility services to public transit has often soured the relationship between the two transportation players. This paper proposes bundled mobility passes between public transit, bikesharing, and Transportation Network Companies (TNCs), as a potential framework in which the popularity of new mobility can be tapped to increase public transit revenue and pass sales while at the same time enabling public institutions to regulate these services more effectively. 1467 more

Estimating the Potential for Shared Autonomous Scooters, Kondor, Dániel, Xiaohu Zhang, Meghjani Malika, Paolo Santi, Jinhua Zhao, and Carlo Ratti , IEEE Transactions on Intelligent Transportation Systems, (2020)

Recent technological developments have shown sig- nificant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the more

Evaluating Commuter Benefits Reforms at the Massachusetts Institute of Technology, Adam Rosenfield, John Attanucci, and Jinhua Zhao , Transportation Research Board 98th Annual Meeting, Washington, D.C., (2019)

In 2016, the Massachusetts Institute of Technology (MIT) introduced a series of commuter benefits reforms for its ten thousand employees. Motivated by aging parking facilities and pressures for alternative land uses, as well as the Institute's climate goals, MIT sought to reduce parking demand by ten percent through a series of enhanced benefits. Branded as AccessMIT, the program included providing each employee with a fully subsidized local transit pass built into their MIT ID card, paid more

A Randomized Controlled Trial in Travel Demand Management, Adam Rosenfield, John Attanucci, and Jinhua Zhao , 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, Anne Halvorsen, Haris Koutsopoulos, Zhenliang Ma, and Jinhua Zhao , 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, Yu Shen, Xiaohu Zhang, and Jinhua Zhao , 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, Anne Halvorsen, Haris Koutsopoulos, Zhenliang Ma, and Jinhua Zhao , 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, Zhan Guo, Jinhua Zhao, Chris Whong, Prachee Mishra, and Lance Wyman , 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, Anne Halvorsen, Haris Koutsopoulos, and Jinhua Zhao , 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, Jinhua Zhao, and Tim Baird , 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, Brittany Welsh, Tim Baird, Jinhua Zhao, and David Block-Schachter , 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

Team Members

Abhishek Basu's picture
MST Student
Anne Halvorsen's picture
MST Student
Adam Rosenfield's picture
MST/MCP Student
Jeffrey Rosenblum's picture
PhD Candidate
Max Arnell's picture
MCP Student
Rachel Luo's picture
MCP/MST Student
Jinhua Zhao's picture
Edward H. and Joyce Linde Associate Professor