Transport for London

Funding: Transport for London

JTL is a key research group in the longstanding partnership between MIT and Transport for London (TfL). A global leader in transportation data and technology, TfL is the integrated authority responsible for London’s Underground, Overground, Buses, Docklands Light Railway, strategic road network, and other transportation modes. MIT’s expertise in transport, behavior, and big data has made a recognized impact improving public transport in London. In partnership with teams throughout TfL, our work has focused on fare payment data and customer analytics, operations and disruption management, and strategic planning and policy.

Many of our research insights are based on London’s Oyster fare payment system. Gabriel Goulet-Langois (MST '15, now working with the Customer Experience Analytics team at TfL) developed a methodology to transform 20 million daily Oyster records into behavioral clusters that inform the operation and design of the transport network. Our innovative work on predictive analytics and demand has the potential to transform how TfL can guide its customers in response to service disruptions. At a strategic level, JTL is developing simulation and analysis platforms to understand and communicate the impact of new infrastructure and transformative technologies such as autonomous vehicles. Some of these methodologies and tools have been featured at the UK Science Museum’s Our Lives in Data exhibit. The strong record and commitment of JTL and TfL make this an ideal research partnership for understanding and shaping the future of urban transport.

 

Integrating Shared Autonomous Vehicle in Public Transportation System, Yu Shen, Hongmou Zhang, and Jinhua Zhao , Transportation Research Part A, (Submitted)

This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation system. After discussing the attributes of and the interaction among the potential stakeholders in the system, we identify possible opportunities for synergy between AVs and the public transportation system based on Singapore’s organizational structures. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus...

An Urban Agenda for Autonomous Vehicles: Embedding Planning Principles into Technological Deployment, Yonah Freemark, and Jinhua Zhao , Working paper, (2017)

The deployment of autonomous vehicles (AVs) has spawned a considerable literature on the role of national and state-level governments in regulating components of AV manufacturing, emissions, safety, licensing, and data sharing. These provide insight into how AVs can be integrated into the current transportation system. Yet the potential for local governments to shape their futures through AV policies is underexplored. This paper argues that it is both necessary and feasible for...

Measuring Regularity of Individual Travel Patterns, Gabriel Goulet-Langlois, Haris N. Koutsopoulos, Zhan Zhao, and Jinhua Zhao , IEEE Transactions on Intelligent Transportation Systems, (2017)

Regularity is an important property of individual travel behavior, and the ability to measure it enables advances in behavior modeling, mobility prediction, and customer analytics. In this paper, we propose a methodology to measure travel behavior regularity based on the order in which trips or activities are organized. We represent individuals’ travel over multiple days as sequences of “travel events”—discrete and repeatable behavior units explicitly defined based on the research question...

Inferring patterns in the multi-week activity sequences of public transport users, Gabriel Goulet Langlois , Transportation Research Part C: Emerging Technologies, Volume 64, p.1-16, (2016)

The public transport networks of dense cities such as London serve passengers with widely different travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to a number of applications core to public transport agencies’ function. In this study,...

Unified Estimator for Excess Journey Time under Heterogenous Passenger Incidence Behavior using Smartcard Data, Jinhua Zhao, Michael Frumin, Nigel Wilson, and Zhan Zhao , Transportation Research Part C, Volume 34, p.70–88, (2013)

Excess journey time (EJT), the difference between actual passenger journey times and journey times implied by the published timetable, strikes a useful balance between the passenger’s and operator’s perspectives of public transport service quality. Using smartcard data, this paper tried to characterize transit service quality with EJT under heterogeneous incidence behavior (arrival at boarding stations). A rigorous framework was established for analyzing EJT, in particular for reasoning...

A Subjective Measure of Car Dependence, Jinhua Zhao , Journal of the Transportation Research Board, Volume 2231, p.44–52, (2011)

A subjective measure of car dependence was developed on the basis of people's own assessment of their reliance on car use. The measure supplements the commonly used objective measure on the basis of actual car use. Structural equation models (SEMs) were estimated to quantify the subjective dependence and to examine its determinants: demographics, socioeconomics, and land use and transit access. The comparison between subjective dependence and actual car use disclosed significant differences...

Real time transit demand prediction capturing station interactions and impact of special events, Peyman Noursalehi, Haris N. Koutsopoulos, and Jinhua Zhao , Transportation Research Part B, (Submitted)

 

Demand for public transportation is highly affected by passengers' experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A...

Trip Detection using Sparse Call Detail Record Data, Zhan Zhao, Haris N. Koutsopoulos, and Jinhua Zhao , IEEE Transactions on Intelligent Transportation Systems, (Submitted)

 

Despite a large body of literature on trip detection using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, the sparse nature of CDR data is not well addressed. This study defines a process that allows physical travel patterns (important to the transportation community) to be inferred from telecommunication patterns captured by CDRs. To reduce the reliance of existing CDR-based trip detection methods on heuristics and...

Rebalancing Shared Mobility-on-Demand Systems: a Reinforcement Learning Approach, Jian Wen, Jinhua Zhao, and Patrick Jaillet , IEEE ITSC Workshop on Intelligent Public Transport 2017, (Submitted)

Shared mobility-on-demand systems have very promising prospects in making urban transportation efficient and affordable. However, due to operational challenges among others, many mobility applications still remain niche products. This paper addresses rebalancing needs that are critical for effective fleet management in order to offset the inevitable imbalance of vehicle supply and travel demand. Specifically, we propose a reinforcement learning approach which adopts a deep Q network and...

Mobility as A Language: Predicting Individual Mobility In Public Transportation Using N-Gram Models, Zhan Zhao, Koutsopoulos Haris, and Jinhua Zhao , Transportation Research Board 96th Annual Meeting, (2017)

For public transportation agencies, the ability to provide personalized and dynamic passenger information is crucial for improving the efficiency of demand management and enhancing customer experience. This requires understanding and especially predicting individual travel behavior in the public transportation system, which is challenging because of the heterogeneity among passengers and the variability of their behaviors. This paper presents, to the best of our knowledge, the first attempt...

Dynamic Pricing in Shared Mobility on Demand Service and its Social Impacts, Han Qiu, Ruimin Li, and Jinhua Zhao , Working paper, (2017)

 

We consider a daily-level profit maximization of a shared mobility on-demand (MoD) service with request-level control, and possible government interventions to improve system efficiency. We use discrete choice models to describe traveler behavior, apply the assortment and price optimization framework to model the request-level dynamics, and leverage insights from dynamic programming to develop daily-level optimization problem. We solve this problem by designing parametric rollout...

Incorporating Mobile Activity Tracking Data In A Transit Agency: Collecting, Comparing, And Trip Mode Inference, Tim Scully, John Attanucci, and Jinhua Zhao , Transportation Research Board 96th Annual Meeting, (2017)

The near ubiquity of smartphones has the potential to transform how researchers, companies, and public transit agencies understand travel behavior. This research analyzes how an emerging class of automatically-collected data based on smartphone GPS and sensor information – referred to here as mobile activity-tracking data – can be used in a transit agency to better understand travel behavior. Through a collaboration with Transport for London, multiple weeks of mobile activity-tracking data...

Simulating the First Mile Service to Access Train Stations by Shared Autonomous Vehicle, Yu Shen, Hongmou Zhang, and Jinhua Zhao , Transportation Research Board 96th Annual Meeting, (2017)

This paper studies the potential impacts of autonomous vehicle (AV) sharing with mobility-on demand service on the public transit system. We analyze the current travel demand in the public transit system in Singapore with a special focus on the first-/last-mile problem during morning peak hours. The first-/last-mile in this paper is defined as the gap between origin/destination and the heavy rail stations. A feasible method to integrate AV sharing in current transit system is proposed, which...

Mapping transit accessibility: Possibilities for public participation, Anson Stewart , Transportation Research Part A: Policy and Practice, 04/2017, (2017)

The value of accessibility concepts is well-established in transportation literature, but so is the low adoption of accessibility-based instruments by practitioners. Based on the premise that leveraging accessibility concepts to address public involvement challenges could promote their adoption in planning practice, this research investigates mechanisms to promote social learning among participants in public workshops. Potential mechanisms of learning include specific tool-based interactions...

Clustering the Multi-week Activity Sequences of Public Transport Users, Gabriel Goulet Langlois, Koutsopoulos Haris, and Jinhua Zhao , 95th Transportation Research Board Annual Meeting, 08/2015, Washington, D.C., (2016)

The public transport networks of dense cities such as London serve passengers with widely dierent travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to an number of applications core to public transport agencies' function. In this study,...

Automatic Data for Applied Railway Management: A Case Study on the London Overground, Michael Frumin, Jinhua Zhao, Nigel Wilson, and Zhan Zhao , Journal of the Transportation Research Board, Volume 2353, p.47–56, (2013)

In 2009, London Overground management implemented a new tactical plan for a.m. and p.m. peak service on the North London Line (NLL). This paper documents that tactical planning intervention and evaluates its outcomes in terms of certain aspects of service delivery (the operator's perspective on system performance) and service quality (the passenger's perspective). Analyses of service delivery and quality and of passenger demand contributed to the development, proposal, and implementation of...

Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment, Michael Frumin, and Jinhua Zhao , Journal of the Transportation Research Board, Volume 2274, p.52–60, (2012)

Passenger incidence (station arrival) behavior has been studied primarily to understand how changes to a transit service will affect passenger waiting times. The impact of one intervention (e.g., increasing frequency) could be overestimated when compared with another (e.g., improving reliability), depending on the assumption of incidence behavior. Understanding passenger incidence allows management decisions to be based on realistic behavioral assumptions. Earlier studies on passenger...

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Senior Postdoctoral Associate
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MST 2015
PhD 2017