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.

  • Automated Information Extraction From Textual Data: Application In Transit Disruption Management

    Transportation Research Board 99th Annual Meeting
    Washington, D.C.
    ,
    (
    2020
    )

    Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information contained in such reports can be valuable for many reasons: identification of issues with response actions, underlying causes of each incident, impacts on the system, etc. In this paper, we develop a comprehensive, pragmatic automated framework for analyzing rail incident reports to support a wide range of applications and functions, depending on the constraints of the available data. The objectives are twofold: a) extract information that is required in the standard report forms (automation), and b) extract other useful content and insights from the unstructured text in the original report that would have otherwise been lost/ignored (knowledge discovery). The approach is demonstrated through a case study involving analysis of 23,728 records of general incidents in the London Underground (LU). The results show that it is possible to automatically extract delays, impacts on trains, mitigating strategies, underlying incident causes, and insights related to the potential actions and causes, as well as accurate classification of incidents into predefined categories.

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    Discovering Latent Activity Patterns from Transit Smart Card Data: A Spatiotemporal Topic Model

    Transportation Research Part C
    (
    2020
    )

    Although automatically collected human travel records can accurately capture the time and location of human
    movements, they do not directly explain the hidden semantic structures behind the data, e.g., activity types. This work
    proposes a probabilistic topic model, adapted from Latent Dirichlet Allocation (LDA), to discover representative and
    interpretable activity categorization from individual-level spatiotemporal data in an unsupervised manner. Specifically,
    the activity-travel episodes of an individual user are treated as words in a document, and each topic is a distribution
    over space and time that corresponds to certain type of activity. The model accounts for a mixture of discrete and
    continuous attributes—the location, start time of day, start day of week, and duration of each activity episode. The
    proposed methodology is demonstrated using pseudonymized transit smart card data from London, U.K. The results
    show that the model can successfully distinguish the three most basic types of activities—home, work, and other. As
    the specified number of activity categories increases, more specific subpatterns for home and work emerge, and both
    the goodness of fit and predictive performance for travel behavior improve. This work makes it possible to enrich
    human mobility data with representative and interpretable activity patterns without relying on predefined activity categories or heuristic rules. 

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    Value of Demand Information in Autonomous Mobility-on-Demand Systems

    Transportation Research Part A
    (
    2019
    )

    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 discuss the compatibility of the AMoD dispatching algorithms with different types of information. Second, the paper assesses the value of demand information through agent-based simulation experiments with the actual road network and travel demand in a major European city, where we assume a single operator monopolizes the AMoD service in the case study area but competes with other transportation modes. The performance of the AMoD system is evaluated from the perspectives of travelers, AMoD operators, and transportation authority in terms of the overall system performance. The paper tests multiple scenarios, combining different information levels, information dynamism, and information granularity, as well as various fleet sizes. Results show that aggregate demand information leads to more served requests, shorter wait time and higher profit through effective rebalancing, especially when supply is high and demand information is spatially granular. Individual demand information from in-advance requests also improves the system performance, the degree of which depends on the spatial disparity of requests and their coupled service priority. By designing hailing policies accordingly, the operator is able to maximize the potential benefits. The paper concludes that the strategic trade-offs of demand information need to be made regarding the information level, information dynamism, and information granularity. It also offers a broader discussion on the benefits and costs of demand information for key stakeholders including the users, the operator, and the society.

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    An Urban Agenda for Autonomous Vehicles: Embedding Planning Principles into Technological Deployment

    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 local government to adjust local mobility policies for AVs towards the goal of achieving key planning principles by disrupting the current transportation system. Local governments must leverage the ephemeral moment in advance of full-scale AV rollout to achieve the principles of equitable, environmentally sustainable, efficient, and livable cities. It is necessary to establish a new regulatory relationship with automobiles and design mobility policies to cultivate AV benefits, while responding to their potentially deleterious impacts. Local governments are capable of doing so through their already-existing regulatory mechanisms managing much of the transportation infrastructure, public transit, taxi, parking, land uses, and public data. Based on such local government power, we identify eight policy instruments that are feasible: centralized data collection and distribution; distance- and congestion-based road pricing; integration of AV and transit networks; income-based subsidies; minimum levels of service provision; zero-emission vehicles; lowered parking provision; and a rethinking of the use of street space. We explore how such instruments could be implemented in the context of existing regulatory mechanisms through the diverging lenses of indicative cases of Chicago and London.

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    Transit-Oriented Autonomous Vehicle Operation with Integrated Demand-Supply Interaction

    Transportation Research Part C
    (
    2018
    )

    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 with the purpose of supporting existing PT modes; the second is the explicit modeling of the interaction between demand and supply. We highlight the transit-orientation by identifying the synergistic opportunities between AV and PT, which makes AVs more acceptable to all the stakeholders and respects the social-purpose considerations such as maintaining service availability and ensuring equity. Specifically, AV is designed to serve first-mile connections to rail stations and provide efficient shared mobility in low-density suburban areas. The interaction between demand and supply is modeled using a set of system dynamics equations and solved as a fixed-point problem through an iterative simulation procedure. We develop an agent-based simulation platform of service and a discrete choice model of demand as two subproblems. Using a feedback loop between supply and demand, we capture the interaction between the decisions of the service operator and those of the travelers and model the choices of both parties. Considering uncertainties in demand prediction and stochasticity in simulation, we also evaluate the robustness of our fixed-point solution and demonstrate the convergence of the proposed method empirically. We test our approach in a major European city, simulating scenarios with various fleet sizes, vehicle capacities, fare schemes, and hailing strategies such as in-advance requests. Scenarios are evaluated from the perspectives of passengers, AV operators, PT operators, and urban mobility system. Results show the trade off between the level of service and the operational cost, providing insight for fleet sizing to reach the optimal balance. Our simulated experiments show that encouraging ride-sharing, allowing in-advance requests, and combining fare with transit help enable service integration and encourage sustainable travel. Both the transit-oriented AV operation and the demand-supply interaction are essential components for defining and assessing the roles of the AV technology in our future transportation systems, especially those with ample and robust transit networks.

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    Rebalancing Shared Mobility-on-Demand Systems: A Reinforcement Learning Approach

    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 and adaptively moves idle vehicles to regain balance. This innovative model-free approach takes a very different perspective from the state-of-the-art network-based methods and is able to cope with large-scale shared systems in real time with partial or full data availability. The authors apply this approach to an agent based simulator and test it on a London case study. Results show that, the proposed method outperforms the local anticipatory method by reducing the fleet size by 14% while inducing little extra vehicle distance traveled. The performance is close to the optimal solution yet the computational speed is 2.5 times faster. Collectively, the paper concludes that the proposed rebalancing approach is effective under various demand scenarios and will benefit both travelers and operators if implemented in a shared mobility-on-demand system.

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    Detecting Changes in Individual Travel Behavior Patterns

    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 pattern of travel behavior.” To detect such changes, a distribution of travel choices is specified for three distinct dimensions of travel behavior—the frequency of travel, time of travel, and locations to visit. The authors assume that the parameters of the distribution change whenever there exits a pattern change. A Bayesian method is developed to estimate the probability that a pattern change occurs at any given time for each behavior dimension. The proposed methodology is demonstrated using pseudonymised transit smart card records of more than 3,000 individuals over two years, and the results are promising. Based on the likelihood ratio test, the behavior patterns that are partitioned by the detected changepoints are proven to improve the goodness-of-fit of the behavior model. In addition, positive correlation is found between the change probability in the temporal and spatial dimensions. The methodology presented in this paper is generalizable and can be applied to detect changes in other aspect of travel behavior and human behavior in general.

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    Detecting Pattern Changes in Individual Travel Behavior: A Bayesian Approach

    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 (the frequency of travel, time of travel, and origins/destinations), and interpret the change of the parameters of the distributions as indicating the occurrence of the pattern change. A Bayesian method is developed to estimate the probability that a pattern change occurs at any given time for each behavior dimension. The proposed methodology is tested using pseudonymized smart card records of 3,210 users from London, U.K. over two years. The results show that the method can successfully identify significant changepoints in travel patterns. Compared to the traditional generalized likelihood ratio (GLR) approach, the Bayesian method requires less predefined parameters and is more robust. The methodology presented in this paper is generalizable and can be applied to detect changes in other aspects of travel behavior and human behavior in general.

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    Individual mobility prediction using transit smart card data

    Transportation Research Part C
    89
    ,
    (
    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 combination of the trip start time t, origin o, and destination d. To predict individual mobility, we first predict whether the user will travel (trip making prediction), and then, if so, predict the attributes of the next trip (t, o, d) (trip attribute prediction). Each of the two problems can be further decomposed into two subproblems based on the triggering event. For trip attribute prediction, we propose a new model, based on the Bayesian n-gram model used in language modeling, to estimate the probability distribution of the next trip conditional on the previous one. The proposed methodology is tested using the pseudonymized transit smart card records from more than 10,000 users in London, U.K. over two years. Based on regularized logistic regression, our trip making prediction models achieve median accuracy levels of over 80%. The prediction accuracy for trip attributes varies by the attribute considered—around 40% for t, 70-80% for o and 60-70% for d. Relatively, the first trip of the day is more difficult to predict. Significant variations are found across individuals in terms of the model performance, implying diverse travel behavior patterns.

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    Real time transit demand prediction capturing station interactions and impact of special events

    Transportation Research Part C
    (
    2018
    )

    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 univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.

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    Integrating Shared Autonomous Vehicle in Public Transportation System: A Supply-Side Simulation of the First-Mile Service in Singapore

    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 bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently.

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    Mapping transit accessibility: Possibilities for public participation

    Anson Stewart
    Transportation Research Part A: Policy and Practice
    (
    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 and how such interactions reinforce structures of learning such as alignment and imagination. This paper details iterative testing of these mechanisms with a tool called CoAXs (short for Collaborative ACCESSibility-based stakeholder engagement system), through focus groups and exploratory workshops. A mixed-methods analysis of the workshops supports the expectation that alignment and imagination correlate positively with social learning, as measured by reported learning and dialog quality. Specific interactions with the accessibility-based features of CoAXs in turn correlate positively with alignment and imagination, at individual and group levels of analysis. These findings, while not robustly generalizable, suggest that effective targeted stakeholder engagement for public transport can be structured around collaborative accessibility mapping. Adoption for broader public participation requires further development, especially the incorporation of actual day-to-day experiences such as unreliable operations.

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    Mobility as A Language: Predicting Individual Mobility In Public Transportation Using N-Gram Models

    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 to predict individual spatiotemporal behavior of public transportation passengers using smartcard data. In this study, each trip is coded as a combination of trip start time, an entry station and an exit station. A passenger’s daily mobility is represented as a chain of travel decisions. We propose a new modeling framework, inspired by Bayesian n-gram models used in natural language processing, to estimate the probability distribution of the next decision in the sequence. Empirical analysis using Oyster card data from London shows promising results. It is found that the exact time of travel is most challenging to predict, but the difference between the predicted time and the true value is usually small. Model performance varies greatly across individuals for the prediction of entry and particularly exit stations. Overall, our proposed model shows significant improvement over the regular n-gram models, or Markov chain-based models in general. The improvement is even larger for weekend trips when travel behavior is flexible, irregular, and considerably less predictable.

  • Induction_bus_and_car

    Measuring Regularity of Individual Travel Patterns

    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 and the available data. We then present a metric of regularity based on entropy rate, which is sensitive to both the frequency of travel events and the order in which they occur. The methodology is demonstrated using a large sample of transit smart card transaction records from London, UK. The entropy rate is estimated with a procedure based on the Burrows-Wheeler transform. The results confirm that the order of travel events is an essential component of regularity in travel behavior. They also demonstrate that the proposed measure of regularity captures both conventional patterns and atypical routine patterns that are regular but not matched to the 9-to-5 working day or working week. Unlike existing measures of regularity, our approach is agnostic to calendar definitions and makes no assumptions regarding periodicity of travel behavior. The proposed methodology is flexible and can be adapted to study other aspects of individual mobility using different data sources.

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    Rebalancing Shared Mobility-on-Demand Systems: a Reinforcement Learning Approach

    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 and adaptively moves idle vehicles to regain balance. This innovative model-free approach takes a very different perspective from the state-of-the-art network-based methods and is able to cope with large-scale shared systems in real time with partial or full data availability. We apply this approach to an agent based simulator and test it on a London case study. Results show that, the proposed method outperforms the local anticipatory method by reducing the fleet size by 14% while inducing little extra vehicle distance traveled. The performance is close to the optimal solution yet the computational speed is 2.5 times faster. Collectively, the paper concludes that the proposed rebalancing approach is effective under various demand scenarios and will benefit both travelers and operators if implemented in a shared mobility-on-demand system.  

  • Induction_bus_and_car

    Dynamic Pricing in Shared Mobility on Demand Service and its Social Impacts

    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 policy and utilizing Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to search for optimal parameter.

    We evaluate our algorithm with a case study in Langfang, China. We develop a simulation system for both the MoD service operations and the city transportation dynamics, and design scenarios with varying supply size, demand size, congestion level, and fare structure. In this case study, the optimal pricing strategy generates considerably more profit than basic strategies (those without assortment or dynamic pricing) and myopic strategies (dynamic pricing at each request level), but it increases the congestion level and reduces the capacity in the transportation system. We also compare two policy interventions to improve the system efficiency, i.e. congestion based taxation and demand based taxation, and find that the congestion based taxation is more effective. 

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    Simulating the First Mile Service to Access Train Stations by Shared Autonomous Vehicle

    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 is to use on-demand AV sharing service as the alternative to the low-demand buses to improve the first-/last-mile connectivity in the study area. An agent-based simulation model is built to evaluate the performance of the new integrated service. The simulation models the behaviors, movements, and interactions of the agents—passengers, AVs, and traditional buses. A bus-only scenario is firstly simulated for validation purpose based on the real-world statistics. Then a series of scenarios integrating AV sharing in public transit system with various fleet size and passenger’s sharing preference are simulated. The results under the AV sharing scenario show that, by letting everyone in the system share their last-mile rides, with careful selection of the size of AV fleet, the new service is able to (1) reduce the average out-of-vehicle time for the passengers, (2) occupy less road resources than the low-demand buses, and (3) have a higher possibility to be financially viable.

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    Incorporating Mobile Activity Tracking Data In A Transit Agency: Collecting, Comparing, And Trip Mode Inference

    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 of London residents was collected between 2015 and 2016 using an application called Moves. Using this case study, this paper discusses the benefits of this new data and how it compares with other data at TfL and elsewhere and examines the process of collecting the data.

    Using the resulting data, this paper then compares the resulting trip records from the mobile activity tracking data with those form the automatic fare card data collected during the same period and same individuals. By comparing mobile activity tracking with an established, well-researched data source like AFC, we observe that while the trip match rate between the two data sources is high (68%) but not perfect. Next, the paper proposes a probabilistic framework to identify between motorized trip modes using mobile activity tracking data and and the public transit network. Specifically, the model uses both spatial characteristics, such as distance to public transit network, and trip characteristics such as speed in order to identify the trip mode as bus, rail, subway, or non-public transit. Using logistic regression, classification tree, and random forest, this model achieves an accuracy of 90%, 91%, and 92% respectively.

  • Induction_bus_and_car

    Inferring patterns in the multi-week activity sequences of public transport users

    Transportation Research Part C: Emerging Technologies
    64
    ,
    (
    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, passenger heterogeneity is investigated based on a longitudinal representation of each user’s multi-week activity sequence derived from smart card data. We propose a methodology leveraging this representation to identify clusters of users with similar activity sequence structure. The methodology is applied to a large sample (n = 33,026) from London’s public transport network, in which each passenger is represented by a continuous 4-week activity sequence. The application reveals 11 clusters, each characterized by a distinct sequence structure. Socio-demographic information available for a small sample of users (n = 1973) is combined to smart card transactions to analyze associations between the identified patterns and demographic attributes including passenger age, occupation, household composition and income, and vehicle ownership. The analysis reveals that significant connections exist between the demographic attributes of users and activity patterns identified exclusively from fare transactions.

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    Clustering the Multi-week Activity Sequences of Public Transport Users

    95th Transportation Research Board Annual Meeting
    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, passenger heterogeneity is investigated based on a longitudinal representation of each user's multi-week activity sequence derived from smart card data. We propose a methodology leveraging this representation to identify clusters of users with similar activity sequence structure. The methodology is applied to a large sample from London's public transport network, in which each passenger is represented by a continuous 4-week activity sequence. The application reveals 11 clusters, each characterized by a distinct sequence structure. Socio-demographic information available for a small sample of users is combined to smart card transactions to analyze associations between the identied patterns and demographic attributes including passenger age, occupation, household composition and income, and vehicle ownership. The analysis reveals that signicant connections exist between the demographic attributes of users and activity patterns identied exclusively from fare transactions.

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    Automatic Data for Applied Railway Management: A Case Study on the London Overground

    Journal of the Transportation Research Board
    2353
    ,
    (
    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 the new tactical plan. NLL trains were delayed routinely en route, with excessive dwell time a major cause. Near-random passenger incidence behavior suggested that an even headway service for NLL might have been more appropriate. The confluence of these analyses was confirmed by the corresponding excess journey time results. On the basis of longitudinal analysis, an evaluation showed that on-time performance increased substantially and observed journey time decreased with the introduction of the new plan. Overall, the effects of this implementation appeared to have been positive on balance. This case study thus demonstrated the applicability of automatic data generally, and certain measures and techniques in the London Overground specifically, to support the tactical planning of an urban railway.

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    Unified Estimator for Excess Journey Time under Heterogenous Passenger Incidence Behavior using Smartcard Data

    Transportation Research Part C
    34
    ,
    (
    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 about passenger’ journey time standards as implied by varying incidence behavior. It was found that although the wrong assumption about passenger incidence behavior and journey time standards could result in a biased estimate of EJT for individual passenger journeys, the unified estimator of EJT proposed in this paper is unbiased at the aggregate level regardless of the passenger incidence behavior (random incidence, scheduled incidence, or a mixture of both). A case study based on the London Overground network (with a tap-in-and-tap-out smartcard system) was conducted to demonstrate the applicability of the proposed method. EJT was estimated using the smartcard (Oyster) data at various levels of spatial and temporal aggregation in order to measure and evaluate the service quality. Aggregate EJT was found to vary substantially across the different London Overground lines and across time periods of weekday service.

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    Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment

    Journal of the Transportation Research Board
    2274
    ,
    (
    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 incidence chose their data samples from stations with a single service pattern such that the linking of passengers to services was straightforward. This choice of data samples simplifies the analysis but heavily limits the stations that can be studied. In any moderately complex network, many stations may have more than one service pattern. This limitation prevents the method from being systematically applied to the whole network and constrains its use in practice. This paper considers incidence behavior in stations with heterogeneous services and proposes a method for estimating incidence headway and waiting time by integrating disaggregate smartcard data with published timetables using schedule-based assignment. This method is applied to stations in the entire London Overground to demonstrate its practicality; incidence behavior varies across the network and across times of day and reflects headways and reliability. Incidence is much less timetable-dependent on the North London Line than on the other lines because of shorter headways and poorer reliability. Where incidence is timetable-dependent, passengers reduce their mean scheduled waiting time by more than 3 min compared with random incidence.

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    A Subjective Measure of Car Dependence

    Jinhua Zhao
    Journal of the Transportation Research Board
    2231
    ,
    (
    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 between the measures, despite their statistical linkage. The measures also differed significantly in terms of how they were influenced by the determinants. Segmenting the population by both measures revealed 20% of the sample with contrasting subjective and objective measures. After controlling for the determinants, the SEMs examined relations between subjective car dependence (attitude), actual car use (behavior), and the intent to reduce car use (intention). Given the cross-sectional nature of the data, causality could not be proven. Two plausible structural relationships were tested: that actual car use determined subjective car dependence and that no direction of causality was assumed. Subjective car dependence mediates the impact of car use on the intent to reduce it: the direct effect of car use on the intent to reduce it is 0.2; the indirect effect through stated car dependence is -0.6; the total effect is -0.4. Actual car use explains approximately 50% of the variation in subjective car dependence, which, together with actual car use, explains approximately 60% of the variation in people's intent to reduce car use.

  • Induction_bus_and_car

    The Potential Impact of Automated Data Collection Systems on Urban Public Transport Planning

    Schedule-Based Modeling of Transportation Networks: Theory and Applications
    (
    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 transport, well beyond the design applications. Of particular interest in the planning of public transport is the opportunity to make use of these increasingly ubiquitous databases to develop a better picture of how public transport systems are performing and being used. In some cases, better estimates of certain performance measures and usage attributes may be made at lower cost than by using conventional data collection me thods, even though there are important limitations on the detailed attributes typically available from these systems. In other cases it is possible for the first time to estimate important performance attributes, such as those related to reliability and its impacts, which have hitherto been virtually impossible to quantify because of paucity of data. This paper describes two applications, focusing on system usage and passenger behavior, which have been developed jointly between MIT and the Chicago Transit Authority (CTA), taking advant age of CTA’s AFC and AVL systems. The specific applications are the estimation of passenger origin-destination matrices for the CTA rail system and the estimation of path choice models for CTA rail passengers. Next steps in the development of further applications for urban public transport systems are also discussed.

PEOPLE