Public Transportation Management

A robust public transit network is an integral part of an urban transportation system. Together with the MIT Transit Lab, we merge behavioral science and systems engineering to determine how to improve the flow of passengers on mass transit, better understand demand, and offer policy solutions to transit agencies to help them respond to emerging challenges in this space.

Please click on the following links to learn more about our specific transit agency partnerships: Transport for London, Chicago Transit Authority, Hong Kong MTA

  • Depth‐damage curves for rail rapid transit infrastructure

    Journal of Flood Risk Management
    1637
    ,
    (
    2023
    )

    Estimates of flood-related damages and costs often rely on asset-specific depth-damage curves that characterize the fragility of a given asset. To date, there are very few depth-damage curves that are potentially applicable to rail rapid transit infrastructure, and no studies attempt to construct these relationships specifically these asset classes. Given the lack of empirical performance data or asset-specific reliability tests, we solicited expert engineering judgment to characterize the fragility of transit assets to saltwater flood exposure. We validate the resulting synthetic depth-damage relationships via a benchmarking approach and demonstrate consistency with previously published depth-damage curves for similar asset classes. The solicitation framework presented can easily be extended to other infrastructure assets and systems, potentially serving as a key step toward a more rigorous quantification of the potential risks posed to infrastructure by natural hazards and climate change.

  • Estimating coastal flood damage costs to transit infrastructure under future sea level rise

    Communications Earth & Environment
    4
    ,
    (
    2023
    )

    Climate change, sea-level rise, and associated increases in climate-related risks pose significant threats to transportation infrastructure in coastal cities. To improve resilience of the transportation infrastructure it is necessary to understand projected future climate extremes, inherent system characteristics, and relationships to local and regional socio-economic and socio-political systems. We provide an overview of the theoretical and practical dimensions of the design of climate-resilient transportation systems and relevant dimensions for infrastructure adaptation and planning, including valuation and assessment of equity. Highlighting existing gaps in literature, we note further research is needed to better relate natural hazard exposure to physical and operational consequences (e.g., disruption durations, asset-level damages, interdependencies) and improved methods for assessing the adaptive capacity of organizations managing transportation infrastructure systems. Climate-resilient transportation infrastructure systems will require paradigms shifts in infrastructure engineering, planning, and design. We also highlight the need for new frameworks for evaluating benefits in the financing of adaptation projects to improve resilience.

  • Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty

    Transportation Research Part B: Methodological
    (
    2023
    )

    When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate congestion during public transit disruptions. Passengers with different origins, destinations, and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation problem as an optimal flow problem with uncertain demand information. To tackle the lack of analytical formulation of travel times due to capacity constraints, we propose a simulation-based first-order approximation to transform the original problem into a linear program. Uncertainties in demand are modeled using robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (-20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce system travel times. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. The improvement of robust models is more prominent when the actual demand pattern is close to the worst-case demand.

  • Evaluation of climate change resilience for Boston’s rail rapid transit network

    Transportation Research Part D: Transport and Environment
    97
    ,
    (
    2021
    )

    Sea level rise (SLR) poses increasing flood risks to coastal cities and infrastructure. We propose a general framework of engineering resilience for infrastructure systems in the context of climate change and illustrate its application for the assessment of SLR impacts on the rail rapid transit network in Boston. Within this framework, projected coastal flood events are treated as exogenous exposure events, which interact with both physical and topological endogenous network characteristics. We consider contextual aspects of resilience by assigning relative importance to links based on passenger flows. Resilience is computed assuming a linear recovery model, neglecting recovery strategies. Using a reference 1–100 year coastal flood event we show decreasing resilience of the rail transit network as projected SLR increases. The proposed framework can be readily extended to consider more sophisticated performance models, recovery strategies, other perturbation events, and additional contextual factors, such as equity considerations.

  • Predictive decision support platform and its application in crowding prediction and passenger information generation

    Transportation Research Part C
    (
    2021
    )

    Demand for public transport has witnessed a steady growth over the last decade in many densely populated cities around the world. However, capacity has not always matched this increased demand. As such, passengers experience long waiting times and are denied boarding during the peak hours. Crowded platforms and the subsequent customer dissatisfaction and safety issues have become a serious concern. The COVID-19 pandemic has dramatically reduced passengers’ willingness to board crowded trains, causing a surge in demand for real-time crowding information. In this paper, we propose a real-time predictive decision support platform which addresses both, operations control and customer information needs. The system provides crowding predictions on trains and platforms, communicates this information to passengers, and takes into account their response to it. It is demonstrated through a case study that providing predictive information to passengers can potentially reduce denied boarding and lead to better utilization of train capacity.

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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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    Capacity-Constrained Network Performance Model for Urban Rail Systems

    Transportation Research Record
    (
    2020
    )

    This paper proposes a general Network Performance Model (NPM) for urban rail systems performance monitoring using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination (OD) demand, operations, route choice, and effective train capacity. A Bayesian simulation-based optimization method for calibrating the effective train capacity is introduced, which explicitly recognizes that capacity may be different at different stations depending on congestion levels. Case studies with data from the Mass Transit Railway (MTR) network in Hong Kong are used to validate the model and illustrate its applicability. NPM is validated using left behind survey data and exit passenger flow extracted from smart card data. The use of NPM for performance monitoring is demonstrated by analyzing the spatial-temporal crowding patterns in the system and evaluating dispatching strategies.

  • Dynamic Origin-Destination Prediction in Urban Rail Systems: A Multi-resolution Spatio-Temporal Deep Learning Approach

    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
    (
    2020
    )

    Short-term demand predictions, typically defined as less than an hour into the future, are essential for implementing dynamic control strategies and providing useful customer infor- mation in transit applications. Knowing the expected demand enables transit operators to deploy real-time control strategies in advance of the demand surge, and minimize the impact of abnormalities on the service quality and passenger experience. One of the most useful applications of demand prediction models in transit is in predicting the congestion on station platforms and crowding on vehicles. These require information about the origin- destination (OD) demand, providing a detailed profile of how and when passengers enter and exit the service. However, existing work in the literature is limited and overwhelmingly focuses on forecasting passenger arrivals at stations. This information, while useful, is incomplete for many practical applications. We address this gap by developing a scalable methodology for real-time, short-term OD demand prediction in transit systems. Our proposed model consists of three modules: multi-resolution spatial feature extraction module for capturing the local spatial dependencies with a channel-wise attention block, auxiliary information encoding module (AIE) for encoding the exogenous information, and a module for capturing the temporal evolution of demand. The OD demand at time t, represented as a N × N matrix, is processed in two separate branches. In one branch we use the discrete wavelet transform (DWT) to decompose the demand into its different time and frequency variations, detecting patterns that are not visible in the raw data. In the other, three convolutional neural network (CNN) layers are utilized to learn the spatial dependencies from the OD demand directly. Instead of treating each channel of the resultant transformation equally, we use a squeeze-and-excitation layer to weight feature maps based on their contribution to the final prediction. A Convolutional Long Short-term Memory network (ConvLSTM) is then used to capture the temporal evolution of demand. The approach is demonstrated through a case study using 2 months of Automated Fare Collection (AFC) data from the Hong Kong Mass Transit Railway (MTR) system. The extensive evaluation of the model shows the superiority of our proposed model compared to the other compared methods.

  • Modeling Epidemic Spreading through Public Transit using Time-Varying Encounter Network

    Transportation Research Part C
    (
    2020
    )

    Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people’s preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying ”influential passengers” using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.

  • Unexpected Bus Operator Absence and Extraboard Scheduling – MBTA Case Study

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

    Improving service reliability and reducing cost have always been prioritized by transit agencies and workforce planning is related to both performance metrics. An important workforce planning function is the management of the extraboard operators who cover for absent drivers. Despite its importance, extraboard planning is an understudied area, in part due to the lack of detailed and reliable data. In this paper, using data from HASTUS Daily at the MBTA, we investigate open work caused by operator absence and how it affects extraboard scheduling. Using k-means clustering, the representative time-of-day absence profiles are identified, and a logistic regression model is estimated to classify each day into the identified clusters and predict the time-of-day absence distribution by combining clustered profiles and classification results. The daily total absent hours are modelled by negative binomial regression. An integer optimization program is formulated to analyze the impact of wrong predictions on scheduling. Key findings are: 1) Time-of-day absence patterns follow regular service schedules well. 2) There is a large variation in the number of extraboard operators needed from week to week, resulting in inherent inefficiencies. 3) Time-of-day profile alignment error is around 26% on average. 4) The average error in predicting daily total absent hours using negative binomial regression is around 22% (19h) for weekdays 32% (21h) for weekends. 5) Optimal extraboard assignment is much more sensitive to the total number of hours than the time-of-day distribution of absences.

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    How does Ridesourcing Substitute for Public Transit? A geospatial perspective in Chengdu, China

    Journal of Transport Geography
    (
    2020
    )

    The explosive growth of ridesourcing services has stimulated a debate on whether they represent a net substitute for or a complement to public transit. Among the empirical evidence that supports discussion of the net effect at the city level, analysis at the disaggregated level from a geospatial perspective is lacking. It remains unexplored the spatiotemporal pattern of ridesourcing’s effect on public transit, and the factors that impact the effect. Using DiDi Chuxing data in Chengdu, China, this paper develops a three-level structure to recognize the potential substitution or complementary effects of ridesourcing on public transit. Furthermore, this paper investigates the effects through exploratory spatiotemporal data analysis and examines the factors influencing the degree of substitution via linear, spatial autoregressive, and zero-inflated beta regression models. The results show that 33.1% of DiDi trips have the potential to substitute for public transit. The substitution rate is higher during the day (8:00–18:00), and the trend follows changes in public transit coverage. The substitution effect is more exhibited in the city center and the areas covered by the subway, while the complementary effect is more exhibited in suburban areas as public transit has poor coverage. Further examination of the factors impacting the relationship indicates that housing price is positively associated with the substitution rate, and distance to the nearest subway station has a negative association with it, while the effects of most built environment factors become insignificant in zero-inflated beta regression. Based on these findings, policy implications are drawn regarding the partnership between transit agencies and ridesourcing companies, the spatially differentiated policies in the central and suburban areas, and the potential problems in providing ridesourcing service to the economically disadvantaged population. 

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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.

  • Tangible Tools for Public Transportation Planning: Public Involvement and Learning for Bus Rapid Transit Corridor Design

    Transportation Research Record: Journal of the Transportation Research Board
    2672
    ,
    (
    2018
    )

    Open governance and open data have given rise to new collaborative tools for public involvement in transit planning. The research presented in this paper extends such tools, adding tangible and interactive features in an attempt to foster interaction, dialog, and social learning. Three tools, representing the impacts of bus rapid transit (BRT) projects at the street, neighborhood, and regional scales, were deployed at a series of public workshops in Boston. A pre-/post- survey design reveals substantive learning about BRT, supported by participants’ general agreement with statements about social learning. The quality of dialog in the workshops may point to the potential for more in-depth, double-loop learning. Of the tools used in the workshop, participants judged the one representing the street scale to be the easiest to use, whereas they judged the touchscreen regional map to be the most relevant and credible. Further research could test such tools with more representative participants and in other settings.

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    Bayesian Inference of Passenger Boarding Strategies at Express Stops with Real-Time Bus Arrival Information

    Transportation Research Board 97th Annual Meeting
    Washington, DC
    ,
    (
    2018
    )

    Efficient design of express and local bus services in urban corridors requires accurate understanding of the travel demand and heterogeneities in passengers’ preferences and needs. Public transit Automated Fare Collection (AFC) systems provide a high-coverage source of data that facilitates an unprecedented opportunity for understanding the demand patterns and passenger preferences for more efficient service designs.In this paper, a Bayesian inference method is proposed to analyze the AFC repeated boarding records of passengers in the presence of real-time bus arrival information. A continuous representation of boarding strategies is introduced that can capture the behavior of passengers if they extend their waiting times to board a preferred route that is due shortly. The proposed method is tested in a case study on the Western Avenue corridor in Chicago, Illinois. The case study demonstrates the possibility of making confident inferences (95%) for thousands of the corridor passengers. The case study also confirms intuitive correlation of the inferred strategies with variables such as travel distance, egress distance, time of day, and availability of countdown timers at the stop. Potential biases of the inference sample and possible applications in service planning are discussed.

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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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    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.

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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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    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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    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.

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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.

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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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    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.

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    Worse than Baumol’s disease: The implications of labor productivity, contracting out, and unionization on transit operation costs

    Transport Policy
    61
    ,
    (
    2017
    )

    Unit costs measured as bus operating costs per vehicle mile have increased considerably above the inflation rate in recent decades in most transit agencies in the United States. This paper examines the impact of (lack of) productivity growth, union bargaining power, and contracting out on cost escalation. We draw from a 17-year (1997–2014) and a 415-bus transit agency panel with 5780 observations by type of operation (directly operated by the agency or contracted out). We have three main findings: first, the unit cost increase in the transit sector is far worse than what economic theory predicts for industries with low productivity growth. Second, contracting out tends to reduce unit costs, and the results suggest that the costs savings from private operations can be only partly explained by lower wages in the private sector. Interestingly, we find that the cost savings from contracting out are larger when the transit agency also directly operates part of the total transit service. However, while overall unit costs are lower in contracted services, cost growth in large private bus operators is no different than cost growth in large public transit operators. Third, unique transit labor laws that lead to union bargaining power are a likely driver of the unit cost growth above inflation. Overall, these factors reflect inherent characteristics of the bus transit sector, such as the nature of low productivity growth and union legislative power related to the need for public subsidy. They drive increases in both transit fares and public subsidy at rates higher than inflation, and play an important role in the deterioration of transit agencies’ financial sustainability.

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    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.

  • 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.

  • Induction_bus_and_car

    Uncertainty in Bus Arrival Time Predictions: Treating Heteroscedasticity With a Metamodel Approach

    IEEE Transactions on Intelligent Transportation Systems
    (
    2016
    )

    Arrival time predictions for the next available bus or train are a key component of modern Traveller Information Systems (TIS). A great deal of research has been conducted within the ITS community developing an assortment of different algorithms that seek to increase the accuracy of these predictions. However, the inherent stochastic and non-linear nature of these systems, particularly in the case of bus transport, means that these predictions suffer from variable sources of error, stemming from variations in weather conditions, bus bunching and numerous other sources. In this paper we tackle the issue of uncertainty in bus arrival time predictions using an alternative approach. Rather than endeavour to develop a superior method for prediction we take existing predictions from a TIS and treat the algorithm generating them as a black box. The presence of heteroscedasticity in the predictions is demonstrated and then a meta-model approach deployed that augments existing predictive systems using quantile regression to place bounds on the associated error. As a case study this approach is applied to data from a real-world TIS in Boston. This method allows bounds on the predicted arrival time to be estimated, which give a measure of the uncertainty associated with the individual predictions. This represents to the best of our knowledge the first application of methods to handle the uncertainty in bus arrival times that explicitly takes into account the inherent heteroscedasticity. The meta-model approach is agnostic to the process generating the predictions which ensures the methodology is implementable in any system.

  • Induction_bus_and_car

    A Ride to Remember: Experienced vs. Remembered Emotion on Public Transit

    95th Transportation Research Board Annual Meeting
    Washington, D.C.
    ,
    (
    2016
    )

    Prior research has shown disconnects between the subjective well-being a person experiences during an event and the subjective well-being the same individual remembers once the event has passed. Despite the differences that exist between experience and memory, memory is often used as a basis for making decisions about the future. Measures of utility in transportation decision models have begun to incorporate concepts of subjective well-being. A better understanding of the differences between experience and memory will allow researchers to understand the human decision making process more accurately. This paper describes a survey used to examine differences between experience and memory for riders of public transit. The survey was given to people riding the Boston subway system and respondents were asked to rate the emotions they felt during their trip on several scales. Later, a follow-up survey was given where respondents rated the emotions they remembered feeling on the previously surveyed trip using the same scales. The results of this survey show that there is a statistically significant difference between the emotional net affect reported during the trip and in the follow-up survey. Respondents indicated significantly more emotional satisfaction while onboard than they did when recalling the trip. Significant differences were also found specifically in feelings of comfort and boredom. This research indicates that the subjective well-being which people experience during a trip is not the same as they remember from it, which has possible impacts on the understanding and modeling of transportation decision-making.

  • Induction_bus_and_car

    Measuring Explicit and Implicit Social Status Bias in Car vs. Bus Mode Choice

    95th Transportation Research Board Annual Meeting
    Washington, D.C.
    ,
    (
    2016
    )

    With results from an Implicit Association Test (IAT) and sociodemographic, travel behavior, and Likert-scale survey questions, we investigate implicit and explicit social status biases in the context of mode choice between car and bus. Using a novel two-part experimental design, the differences between implicit and explicit measures of bias are examined to understand how the IAT may complement or improve upon traditional survey methods to capture attitudinal biases. We find that explicit agreement with positive and negative statements about social status may fail to capture subconscious biases that play a role in individual travel behavior (i.e. mode choice). We corroborate previous research into the idea of pride as a factor in explaining car mode choice as well as propose a new way to quantify these inherent or implicit social status biases that are controversial or difficult to consciously identify and articulate. With this as our case study, we introduce the IAT as a method in transportation literature and propose further application of the IAT in transportation research to better understand the subconscious biases that influence travel behavior and policy preference.

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    FMS-TQ: Combining Smartphone and iBeacon 4 Technologies in A Transit Quality Survey

    95th Transportation Research Board Annual Meeting
    Washington, D.C.
    ,
    (
    2016
    )

    The Internet of Things (IoT) will offer transit agencies an opportunity to transform ways to measure, monitor, and manage performance. We demonstrate the potential value of two combined technologies, smartphones and iBeacons, for actively engaging customers in measuring satisfaction and co-monitoring bus service quality. Specifically, we adapt our smartphone-based survey system, Future Mobility Sensing (FMS), to connect with iBeacons for an event-driven approach to measure user-reported satisfaction before (i.e. at the stop), during (i.e., while traveling), and after (reflectively) transit trips. The system collects a combination of sensor (GPS, WiFi, GSM and accelerometer) data to track transit trips, while soliciting users’ feedback on trip experience with in-app pop-up surveys. Both bus trip data and passenger feedback are collected and uploaded onto the server at the end of each day. These data are not intended to replace traditional monitoring channels and processes, but, rather, they complement official performance monitoring through a more customer-centric perspective in relative real time. The paper presents the theoretical foundations, describes a pilot implementation of the platform in Singapore, and discusses preliminary results that demonstrate technical feasibility.

  • 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.

  • Method for Assessing Bus Delay in Mixed Traffic to Identify Transit Priority Improvement Locations in Cambridge, Massachusetts

    Transportation Research Record: Journal of the Transportation Research Board
    2533
    ,
    (
    2015
    )

    Urban transit services face a number of challenges from space constraints, congestion, and delays, among other issues. Implementing bus priority at traffic signals or providing exclusive operating space for buses can increase the attractiveness of taking the bus and thereby encourage ridership. The City of Cambridge, Massachusetts, was looking to pilot such interventions to demonstrate benefits of bus ridership, but needed a prioritized list of route segments with the largest levels of excess travel time to do so. Three metrics were used to evaluate delay: vehicle delay, overall passenger delay, and system reliability. These three metrics were combined into a single composite rating system for each segment and used to identify route segments along which buses experienced the most delay. The City of Cambridge analyzed high-ridership bus routes with automatic passenger counter data to identify segments along the route where buses experienced substantial delay. The next step for this project is to conduct an on-site field visit of targeted segments to develop potential bus prioritization proposals for each. This paper outlines the method developed to calculate bus delay by segment and presents results for one route analyzed.

  • Induction_bus_and_car

    Capacity Constrained Accessibility of High-Speed Rail

    Transportation
    (
    2015
    )

    This paper proposes an enhanced measure of accessibility that explicitly considers circumstances in which the capacity of the transport infrastructure is limited. Under these circumstances, passengers may suffer longer waiting times, resulting in the delay or cancellation of trips. Without considering capacity constraints, the standard measure overestimates the accessibility contribution of transport infrastructure. We estimate the expected waiting time and the probability of forgoing trips based on the M/GB/1 type of queuing and discrete-event simulation, and formally incorporate the impacts of capacity constraints into a new measure: Capacity Constrained Accessibility (CCA). To illustrate the differences between CCA and standard measures of accessibility, this paper estimates the accessibility change in the Beijing–Tianjin corridor due to the Beijing–Tianjin Intercity High-Speed Railway (BTIHSR). We simulate and compare the CCA and standard measures in five queuing scenarios with varying demand patterns and service headway assumptions. The results show that 1) under low system loads condition, CCA is compatible with and absorbs the standard measure as a special case; 2) when demand increases and approaches capacity, CCA declines significantly; in two quasi-real scenarios, the standard measure overestimates the accessibility improvement by 14~30% relative to the CCA; and 3) under the scenario with very high demand and an unreliable timetable, the CCA is almost reduced to the pre-BTIHSR level. Because the new CCA measure effectively incorporates the impact of capacity constraints, it is responsive to different arrival rules, service distributions, and system loads, and therefore provides a more realistic representation of accessibility change than the standard measure.

  • Induction_bus_and_car

    Smart Devices and Travel Time Use by Bus Passengers in Vancouver, Canada

    International Journal of Sustainable Transportation
    9
    ,
    (
    2015
    )

    This research investigates the usage of smart devices and time at bus stops and on buses in Vancouver, Canada. Using passive observations and self-reported surveys mainly from college students, the majority of passengers were found to use their travel time actively. Most of the observed active activities are associated with the usage of smart devices. However, while the possession of smart devices is prevalent, less than one third of passengers used them during travel. A variety of environmental and trip factors, personal attributes, and past experiences influence the usage of smart devices. Research also found that smart devices encourage multitasking.

  • Induction_bus_and_car

    Hysteresis and Urban Rail: The Effects of Past Urban Rail on Current Residential and Travel Choices

    European Journal of Transport and Infrastructure Research
    15
    ,
    (
    2015
    )

    Cities are endowed with and accumulate natural and constructed assets based on their unique histories, which in turn define the choice set of the present. But, common practice is that current behaviour can be described without reference to past circumstances. This work departs from that practice by examining the effects of historical urban rail on current residential location and travel behaviour, from the era of horsecars (1865) and streetcars (1925) to the present in Boston. It uses tract level data to explore the hysteretical effects of past access to rail—the extent to which the urban system retains the impacts of rail even when it no longer exists.
    Current density and travel behaviour are measurably influenced by past access to rail. These findings are robust to a series of alternate causal, functional, and spatial specifications. The built environment and demographic patterns are found to be the strongest mechanisms for these persistent effects. Past access to rail has shaped the city, and that shape has, in turn, affected travel behaviour. For density and auto ownership there is also a residual measurable effect of past access unexplained by the built environment or demographic patterns.
    This research shows that past rail access continues to reverberate in current residential location and travel behaviour. These findings of hysteresis add to an understanding of the long-term impacts of rail infrastructure, and suggest that if higher density and lower levels of auto ownership are desirable, policymakers should focus on reuse of areas that were built around rail.

  • Induction_bus_and_car

    Agglomeration and Diversification: Bi-Level Analysis of 15-Years’ Impacts of Madrid-Seville High-Speed Rail

    Transportation Research Board 94th Annual Meeting
    Washington, D.C.
    ,
    (
    2015
    )

    This paper studies the impacts of Madrid-Seville High-Speed Rail (HSR) on population growth and land cover change in the five HSR connected cities – Madrid, Ciudad Real, Puertollano, Cordoba, and Seville – at both regional and local level. The analysis period ranges from 1991 to 2006. The study finds that, at regional level, the population growth and land development process concentrate mostly towards the two largest cities, Madrid and Seville, while other smaller HSR served cities are also benefited by HSR. At local level, the impacts of HSR are more diverse. The process of population redistribution and land development in each city varies largely. Among all evidences, HSR contributes the most to Ciudad Real, with booming population increases and urban development. In addition, younger people are also attracted to reside in this area. To study the accessibility impacts of HSR on the development of new urban areas in each HSR city, binary probit models incorporating the change of accessibility, population, and neighborhood situations are adopted. The results suggest that, the land development in smaller cities can be majorly ascribed to the improvement of regional accessibility and population growth. However, to explain the urban development process in Madrid and Seville, the inputs with only accessibility and population are not sufficient.

  • Induction_bus_and_car

    Customer Loyalty Differences Between Captive and Choice Transit Riders

    Journal of the Transportation Research Board
    2415
    ,
    (
    2014
    )

    Traditionally, efforts to increase the customer base of public transportation agencies have focused primarily on attracting first-time users. Customer retention, however, has many benefits not often realized. Loyal customers provide recommendations to others, increase and diversify their use of the service, and do not require acquisition costs associated with new customers. An earlier study identified key drivers of customer loyalty, with the Chicago Transit Authority (CTA) in Illinois as a case study. A customer loyalty model was created with service value, service quality, customer satisfaction, problem experience, and perception of CTA as constructs. The present study examined customer loyalty differences of captive and choice riders. Captive riders had no viable travel alternatives and might have continued to use transit even if unhappy with service. Choice riders chose to use transit after they compared travel options and might have switched to an alternative if service degraded. Captive riders reported experiencing more problems and were more sensitive to problems; each additional problem brought significant drops in service quality ratings. Captive riders tolerated problems and continued to use transit but showed discontent through their ratings of service quality. Service value was insignificant in captive riders’ loyalty decisions because cost–benefit analysis defined service value as irrelevant to them. The relationship between perceptions of CTA and of service quality was stronger for choice riders. If they began the service with high opinions of the transit agency, they were much more likely to have high ratings of service quality than were captive riders.

  • Induction_bus_and_car

    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.

  • Induction_bus_and_car

    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.

  • Induction_bus_and_car

    Quantity and Quality of Productive Use of Transit Commuting Time: A Heckman Model

    Transportation Research Board 92nd Annual Meeting
    Washington, D.C.
    ,
    (
    2013
    )

    In North America, the average individual taking public transportation spends about 45 minute commuting one way each day. This equates to about 398 hours per year and thus ways to reduce travel time are imperative. Rather than attempting to reduce travel time directly, changing the perspective of how commuting time is spent by improving the productive use of time provides a more cost effective solution. This paper explored and measured the extent that bus commuters are currently using their time actively during in-vehicle travel time. Heckman’s selection method was used to incorporate passengers who do not use their time actively to correct for sample selection bias and model the decision to use time actively as a two stage process. Average quantity and quality of primary activity time was found to be 20 minutes and 66%, respectively, where 66% is a relative measure compared to the same activity conducted at a home or office environment where the quality would be 100%. The impact of ICT is predominant in almost all models tested, with degree of crowding and gender being a major factor in one’s choice to use time actively. Given that individuals are able to make use of their transit commute time productively, commuters will in hope be more attracted to use transit by having this advantage over driving. If being productive does have an influence on an individual’s travel mode choice, there could be huge implications on traditional transportation modelling and demand and quality management.

  • Induction_bus_and_car

    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.

  • Induction_bus_and_car

    Potential and Reality of Using High Speed Rail for Commuting in Yangtze River Delta

    IEEE/ASCE/ASME Joint Rail Conference
    Philadelphia
    ,
    (
    2012
    )

    This paper focuses the current and potential impact of Huning High Speed Railway (H-N HSR) on commuting patterns in the Yangtze River Delta region. We will examine 1) to what extent commuting via HSR is occurring, since the HSR system is very new and is a substantial and long-term decision for people to move for a job or home across cities; 2) the future potential of commuting via the HSR perceived by current HSR passengers to relocate for a job or relocate their home and their preferences in terms of target cities of jobs and housing relocations; and 3) the areas in the HSR system required for improvement in order to enable a smooth commuting experience across cities, including affordability of the HSR tickets, the comparison between the maximum tolerable travel time with the actual door-to-door travel time, and various aspects of integration between HSR and intra-urban transportation systems.

  • 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.

  • Induction_bus_and_car

    Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems

    Computer-Aided Civil and Infrastructure Engineering
    22
    ,
    (
    2007
    )

    Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide-ranging application, well beyond their design purpose. This article illustrates the potential that ADC systems can provide transit agencies with new rich data sources at low marginal cost, as well as the critical gap between what ADC systems directly offer and what is needed in practice in transit agencies. To close this gap requires data processing and analysis methods with support of technologies such as database management systems (DBMS) and geographic information systems (GIS). This research presents a case study of the automatic fare collection (AFC) system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin-destination (OD) matrices from an origin-only AFC system to replace expensive passenger OD surveys. A software tool is developed to facilitate the method implementation and the results of the application in CTA are reported.

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