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.

  • Evaluating the travel impacts of a shared mobility system for remote workers

    Transportation Research Part D
    (
    2023
    )

    Given the rapid rise of remote work, there is an opportunity for new shared mobility services designed to meet the needs of passengers with multiple possible work locations. This paper develops a new optimization model to enable shared mobility systems to match drivers and passengers when passengers have flexible destinations. Constraints representing employer policies, such as mandatory co-location of colleagues and limited capacity of satellite offices are introduced in order to explore the impact of employer remote work policies on travel demand. A case study using historical demand data demonstrates that incorporating flexible work locations can increase ride-pooling participation by up to 6.7% and reduce vehicle-kilometers travelled by 4.9%. Outcomes are found to be significantly affected by employer policies. The implications of the results for shared mobility business models, employer remote work plans and local transportation policy are discussed.

  • Impacts of congestion pricing on ride-hailing ridership: Evidence from Chicago

    Transportation Research Part A: Policy and Practice
    (
    2023
    )

    To combat congestion, promote sustainable forms of transportation, and support the public transit system, Chicago introduced a congestion pricing policy targeting transportation network company (TNC) services on January 6, 2020. This policy aimed to discourage single-occupant and peak-period TNC travel, particularly in high-congestion areas. Using TNC trip record data collected from the Chicago Data Portal, we quantify the impacts of the congestion pricing policy on TNC ridership in Chicago, differentiating between shared and single-occupant trips. Employing a Difference-in-Differences identification strategy, we find that the implementation of the congestion pricing policy led to an increase in shared TNC trip counts and a much larger decrease in single-occupant trip counts. Overall, the policy implementation is associated with a 7.1% reduction of total TNC pickup trips, a 16.4% increase of shared TNC pickup trips and a 11% reduction of single TNC pickup trips. Given the estimated policy effects, we find that the price elasticity of the TNC trip volume in the downtown areas is roughly -0.48. In terms of spatial variation, we find that the lost TNC trips were mainly trips that began and ended in the central business district. The south side of Chicago, which has a high proportion of African-American and low-income residents, shows evidence of single trip reduction for trips that began or ended in the downtown areas due to the policy implementation, but the policy did not seem to incentivize pooling to or from the downtown areas as effectively in the south side as in other regions of Chicago. Regarding the time-of-day variation, we find that the policy is more effective in encouraging trip sharing for off-peak travels than for peak-time travels. Our research provides local planners and policymakers with valuable insights into the impacts of the congestion pricing policy. The method and findings of this research can also be used for other cities that are considering adopting congestion pricing policies on TNCs in the future.

  • The Mobility Pattern of Dockless Bike Sharing: A Four-month Study in Singapore

    Transportation Research Part D
    (
    2021
    )

    Many cities around the world have adopted dockless bike-sharing programs with the hope that this new ser- vice could enhance last-mile public transit connections. However, our understanding of the travel patterns using dockless bike sharing is still limited. To advance the knowledge on the new service, this study inves- tigates mobility patterns of dockless bike sharing in Singapore using a four-month dataset. An exploratory spatiotemporal analysis is conducted to show daily travel patterns, while community detection of networks is used to explore the spatial clusters emerged from cycling behaviors. A series of Poisson regression models are then estimated to characterize the generation, attraction and resistance factors of bike trips in different periods of a day. The proposed regression model, which considers built environment factors of origin and destination simultaneously, is proved to be effective in deciphering mobility. The empirical findings shed light on policy implications in sustainable transportation planning.

  • Robust Matching-Integrated Vehicle Rebalancing in Ride-hailing System with Uncertain Demand

    Transportation Research Part B
    (
    2021
    )

    With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver-customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver-customer matching into vehicle rebalancing problems to produce better rebalancing strategies. The MIVR model treats the driver-customer matching component at an aggregate level and minimizes a generalized cost including the total vehicle miles traveled (VMT) and the number of unsatisfied requests. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The proposed MIVR model is tested against two benchmark vehicle rebalancing models using real ride-hailing demand and travel time data from New York City (NYC). The MIVR model is shown to have better performances by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model.

  • E-scooter sharing to serve short-distance transit trips: a Singapore Case

    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 influencing the choice of e-scooters and transit. We then calculated the number of transit trips that can be replaced by e-scooters. Second, we analyzed the decision of e-scooter companies in terms of the trade-offs between serving more e-scooter trips and making more revenue under varying fares. The results show that fare, MRT transfer, and MRT access-egress walking distance have significantly negative impacts on mode utilities with random tastes among respondents. Male, young and high-income groups are more heterogeneous in e-scooter preferences compared with other groups. The loss of mode share can be nearly 17% if maximizing the revenue. We classify trade-off situations into five categories and provide suggestions of how to balance between mode share and revenue for each category. Several implications are drawn for better harnessing and regulating this new mobility service, including where to deploy e-scooters to satisfy the demand unmet by the transit and how to reach a proper balance between private operators and public welfare.

  • Estimating the Potential for Shared Autonomous Scooters

    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 industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.

  • Bundled Mobility Passes in Chicago: Consumer Preference and Revenue Implications

    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 employees in the Chicago area answered a stated preference (SP) survey to gauge preferences towards a hypothetical bundled “Superpass” offered by the Chicago Transit Authority (CTA). The bundled mobility pass would include a CTA bus and rail pass, a bikeshare pass, a fixed number of shared ridehail rides per month, and could potentially be added on to an existing commuter rail pass for a discounted price. A discrete choice model was created to estimate Superpass demand under different scenarios. This analysis found that the CTA, bikeshare operator, and TNC operator can all increase either the number of passes they sell or the number of rides they provide to the market. They can all also increase their revenue or at least remain revenue neutral. This result shows that there is room for mutual benefit across all stakeholders through partnership in mobility bundles. This paper ends with five key recommendations for policymakers regarding bundled fare products, including the need to conduct innovative fare policy pilots.

  • Behavioral Response to Discounted Fares for Low-income Transit Riders in Boston

    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 could improve the quality of life of low income transit users. A two-month randomized controlled evaluation was conducted to study the effect of providing a 50% discounted MBTA fare to low-income individuals in the Boston region. Individuals receiving food stamps (SNAP) benefits were recruited and randomly assigned to either receive a 50% discount smartcard or a regular smartcard. All participants provided daily travel diary information on the purposes of their transit trips via a custom developed automated SMS/text-based mobile-phone ChatBot software tool. Compared to the control group receiving a standard smartcard, those in the treatment group with a 50% discounted smartcard took, on average, approximately 30% more transit trips, as well as more trips to health care and social services. The research also indicates that compared to the average MBTA rider, the low-income individuals participating in the study took more of their trips during off-peak times and were more likely to pay with their smartcard using stored value (“pay as you go”) rather than purchasing seven-day or monthly passes.

  • Evaluating Commuter Benefits Reforms at the Massachusetts Institute of Technology

    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 for by MIT on a per- use basis to the transit agency. For drivers, monthly and annual parking permits were replaced with daily, pay-as-you-park pricing. Subsidies for commuter rail were increased, and a new 50% subsidy on parking fees at transit stations was introduced to encourage last-mile transit commuting. An online commuter dashboard was launched with incentives and gamification to aid with program outreach.
    The net result was an eight percent reduction in parking demand in the first year, at a net cost to MIT of about $200 per employee. Transit agency revenue increased as ridership among MIT employees rose approximately ten percent, and the overall single-occupant vehicle mode share declined to 25% while overall employee satisfaction increased. The program helped allow MIT to manage the closure of a 372- space garage in 2017 without denying parking to any employee drivers, and will help accommodate future parking supply reductions. The program serves as a successful case study of how transit-oriented employer benefits can effect a mode shift away from drive-alone commuting in a cost-effective manner.

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    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 public transport agencies, provide a unique platform to advance systematic approaches for the design and evaluation of PTDM strategies. The paper discusses the main steps for developing PTDM programs: a) problem identification and formulation of program goals; b) program design; c) evaluation; and d) monitoring. The problem identification phase examines bottlenecks in the system based on a spatiotemporal passenger flow analysis. The design phase identifies the main design parameters based on a categorization of potential interventions along spatial, temporal, modal, and targeted user group parameters. Evaluation takes place at the system, group, and individual levels, taking advantage of the detailed information obtained from smart card transaction data. The monitoring phase addresses the longterm sustainability of the intervention and informs potential changes to improve its effectiveness. A case study of a pre-peak fare discount policy in Hong Kong’s MTR network is used to illustrate the application of the various steps with focus on evaluation and analysis of the impacts from a behavioral point of view. Smart card data from before and after the implementation of the scheme from a panel of users was used to study policy-induced behavior shifts. A cluster analysis inferred customer groups relevant to the analysis based on their usage patterns. Users who shifted their behavior were identified based on a change point analysis and a logit model was estimated to identify the main factors that contribute to this change: the amount of time a user needed to shift his/her departure time, departure time variability, fare savings, and price sensitivity. User heterogeneity suggests that future incentives may be improved if they target specific groups.

  • Induction_bus_and_car

    A Randomized Controlled Trial in Travel Demand Management

    Transportation
    (
    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 rewards for reducing their frequency of parking; a third group received both interventions, while a control group was monitored with no intervention. The paper aims to examine how behavioral incentives, namely targeted information provision and monetary rewards, can be used independently or in combination to encourage alternatives to drive-alone commuting. Success was measured as the extent to which drivers decreased their frequency of parking and increased their use of alternative modes during and after the campaign. While the combined treatment group contained the highest number of top-performing participants, no statistically significant differences-in-differences were observed amongst the treatment arms compared to the control. A post-experiment survey indicated a widespread increase in awareness of employer transportation benefits, and a much larger stated shift from driving towards transit than was supported by passively-collected data. Survey results suggested that while intent to reduce car use existed, complaints of insufficient quality of transit service and relative convenience of driving suppressed modal shifts. Most importantly, the discrepancy between self-reported and actual behavior change highlights important limitations and biases of survey-based travel behavior research.

    Cite as: "Adam Rosenfield, John Attanucci and Jinhua Zhao (2019) A Randomized Controlled Trial in Travel Demand Management, Transportation, DOI: 10.1007/s11116-019-10023-9"

  • Induction_bus_and_car

    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 spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.

  • Induction_bus_and_car

    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 design, and program evaluation. The problem identification phase includes a spatio-temporal passenger flow analysis, while the design phase categorizes and integrates possible interventions along spatial, temporal, modal, and targeted user group parameters. The evaluation examines effectiveness, efficiency, and acceptability, and utilizes detailed smart card transaction data for analysis at system-wide, group, and individual levels. We apply the framework in a case study of the pre-peak pricing policy in Hong Kong’s MTR network. Contrasting data from before and after the implementation of the scheme, we identified six customer groups using cluster analysis. We used a panel of 20,000 users and the change-point analysis to to study policy-induced behavior shifts at the individual level. Estimating a logit model we identified that the duration of required departure time shift, departure time variability, fare savings, and price sensitivity are key factors influencing behavioral change.

    A public transportation demand management framework helps transit agencies structure policy design and evaluation. Multi-level evaluation using disaggregate smart card data reveals heterogeneous policy responses among disparate groups, implying that targeting specific groups can improve incentives. PTDM complements infrastructure or service expansions, providing additional policy tools for transportation planners.

  • Induction_bus_and_car

    Redesigning Subway Map to Mitigate Bottleneck Congestion: An Experiment in Washington DC Using Mechanical Turk

    Transportation Research Part A
    106
    ,
    (
    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 alternative maps. Results show that redesigned maps significantly affect participants’ route choices. Depending on the specific design, a 20% length increase of the overcrowded line could move 1.9–5.7 percentage points of ridership to an alternative, underutilized line. The change could remove up to 10 passengers per car during the highest peak, reducing the number of highly congested half-hour periods (max load = 100–120 passengers per car) on the overcrowded line from 4 to 1, and the number of crush periods (max load > 120 passengers per car) from 3 to 1. This is done at minimal or zero cost. The paper calls for more attention from transit agencies to the planning potential of transit maps.

  • Induction_bus_and_car

    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 2014 to encourage users to travel before the peak hour and reduce on-board crowding. To understand the impacts of this intervention, existing congestion patterns were first reviewed and a clustering analysis was performed to reveal typical travel patterns among MTR users. Then changes to when users chose to travel were studied at three levels to evaluate the program’s effects. Patterns among all users were measured across both the whole system and for specific rail segments. The travel patterns of the user groups, who have more homogeneous usage characteristics, were also evaluated, revealing differing responses to the promotion among groups. The incentive was found to have small impacts on morning travel, particularly at the beginning of the peak hour and among users with commuter-like behavior. Aggregate and group-specific elasticities were developed to inform future promotions and the results were also used to suggest other potential incentive designs.

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    “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 framework for generating, implementing, and testing the results of different interventions designed to affect users’ travel behavior by delivering behavioral feedback via an activity-tracking smartphone application. The results of this promotional strategy are tested in two pilot projects among university students and “Bike to Work Week” participants in British Columbia and Minnesota. Implications for program evaluation and funding and future directions for research on behavioral interventions are discussed.

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    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 to enable these benefits, ongoing research has focused on the software design for the collection of this data as a primary effort. Commercially available apps with similar functionality have found market niches in the ‘fully instrumented’ or ‘Quantified Self’ movement as enablers of physical fitness tracking. This paper outlines a web application prototype that interfaces with one such third-party tracking application, Moves, to automatically collect travel data in a format convenient to both users and researchers. The prototype application registers a participant with a research study and guides them through the process of authenticating researcher access of their Moves data. While dedicated travel survey apps aim to replicate the functionality of apps like Moves, they require separate installation and additional attention from study participants. The described web application, combined with Moves, requires almost no additional user interaction. This method is a lightweight, flexible solution for researchers looking to quickly test new hypotheses, allowing the researcher to progress rapidly from concept to research. Using data in this fashion expands the universe of travelers that researchers can reach.

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