Transport for London

Funding: 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.

 

Value of Demand Information in Autonomous Mobility-on-Demand Systems, Jian Wen, Nassir Neema, and Jinhua Zhao , Transportation Research Part A, (Submitted)

Effective management of demand information is a critical factor in the successful operation of autonomous mobility-on-demand (AMoD) systems. This paper classifies, measures and evaluates the demand information for an AMoD system. First, the paper studies demand information at both individual and aggregate levels and measures two critical attributes: dynamism and granularity. We identify the trade-offs between both attributes during the data collection and information inference processes and...

Transit-Oriented Autonomous Vehicle Operation with Integrated Demand-Supply Interaction, Jian Wen, Chen Leo, Nassir Neema, and Jinhua Zhao , Transportation Research Part C, (In Press)

Autonomous vehicles (AVs) represent potentially disruptive and innovative changes to public transportation (PT) systems. However, the exact interplay between AV and PT is understudied in existing research. This paper proposes a systematic approach to the design, simulation, and evaluation of integrated autonomous vehicle and public transportation (AV+PT) systems. Two features distinguish this research from the state of the art in the literature: the first is the transit-oriented AV operation...

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

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...

Individual mobility prediction using transit smart card data, Zhan Zhao, Haris N. Koutsopoulos, and Jinhua Zhao , Transportation Research Part C, Volume 89, p.19-34, (2018)

For intelligent urban transportation systems, the ability to predict individual mobility is crucial for personalized traveler information, targeted demand management, and dynamic system operations. Whereas existing methods focus on predicting the next location of users, little is known regarding the prediction of the next trip. The paper develops a methodology for predicting daily individual mobility represented as a chain of trips (including the null set, no travel), each defined as a...

Detecting Pattern Changes in Individual Travel Behavior: A Bayesian Approach, Zhan Zhao, Haris N. Koutsopoulos, and Jinhua Zhao , Transportation Research Part B, (2018)

Although stable in the short term, individual travel patterns are subject to changes in the long term. The ability to detect such changes is critical for developing behavior models that are adaptive over time. We define travel pattern change as "abrupt, substantial, and persistent changes in the underlying pattern of travel behavior" and develop a methodology to detect such changes in individual travel patterns. We specify one distribution for each of the three dimensions of travel behavior...

Integrating Shared Autonomous Vehicle in Public Transportation System: A Supply-Side Simulation of the First-Mile Service in Singapore, Yu Shen, Hongmou Zhang, and Jinhua Zhao , Transportation Research Part A, (2018)

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

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...

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...

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...

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...

The Potential Impact of Automated Data Collection Systems on Urban Public Transport Planning, Nigel Wilson, Jinhua Zhao, and Adam Rahbee , Schedule-Based Modeling of Transportation Networks: Theory and Applications, p.75–99, (2009)

Automated data collection systems are becoming increasingly common in urban public transport systems, both in the US and throughout the developed world. These systems, which include Automatic Vehicle Location (AVL), Automatic Passenger Counting (APC), and Automatic Fare Collection (AFC), are often designed to support specific and fairly narrow functions within the transport agency. However, it is clear that the data obtained from these systems can have wide-ranging applications within public...

Detecting Changes in Individual Travel Behavior Patterns, Zhan Zhao, Jinhua Zhao, and Haris N. Koutsopoulos , Transportation Research Board 97th Annual Meeting, Washington, D.C., (2018)

Although stable in the short term, individual travel behavior patterns are subject to changes in the long term. The ability to detect such changes is critical for developing behavior models that are adaptive over time. However, no sufficient method has been developed in the existing literature. The objective of this paper is to develop a methodology to detect changes in individual travel behavior patterns, which are defined as “the significant, abrupt and persistent changes in the underlying...

Rebalancing Shared Mobility-on-Demand Systems: A Reinforcement Learning Approach, Jian Wen, Jinhua Zhao, and Patrick Jaillet , Transportation Research Board 97th Annual Meeting, Washington, D.C., (2018)

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, the authors propose a reinforcement learning approach which adopts a deep Q network...

An Urban Agenda for Autonomous Vehicles: Embedding Planning Principles into Technological Deployment, Yonah Freemark, and Jinhua Zhao , Transportation Research Board 97th Annual Meeting, Washington, D.C., (2018)

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...

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...

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, (2017)

 

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...

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...

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...

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,...

People

Assistant Professor at University of Melbourne
MST Student
PhD Candidate
PhD Candidate
MST Student
MST 2015
Post-Doctoral Associate
PhD 2017
Edward H. and Joyce Linde Associate Professor