Nudging Sustainable Travel

Advances in behavioral science have begun to provide a new toolkit of theories, models, and empirical methods for designing transportation programs. We fuse them with the latest data technology and analytics to achieve a specific purpose: promoting sustainable travel behavior. We develop the methods of understanding user heterogeneity, segmenting users, and predicting individual travel sequences; propose a framework for generating, implementing, and evaluating different interventions; and evaluate and design real world programs such as pre-peak MTR discount in Hong Kong, bike to work in Vancouver, subway map redesign in D.C. Subway, fare subsidies for low-income transit riders in Boston, and incentivizing car pooling at MIT. 

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

Understanding the Usage of Stationless Bike Sharing in Singapore, Yu Shen, Xiaohu Zhang, and Jinhua Zhao , Working paper, (2017)
Demand Management in Public Transportation: A Framework and Application, Anne Halvorsen, Koutsopoulos Haris, and Jinhua Zhao , Working paper, (2017)

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

Reducing Subway Crowding: Analysis of an Off-peak Discount Experiment in Hong Kong, Anne Halvorsen, Koutsopoulos Haris, 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

Anne Halvorsen's picture
MST Student
Adam Rosenfield's picture
MST/MCP Student
Jeffrey Rosenblum's picture
PhD Candidate