Using automated fare collection data, GIS, and dynamic schedule queries to improve transit data and transit assignment model.

Slavin, Howard; Rabinowicz, Andres; Brandon, Jonathan; Flammia, Giovanni; Freimer, Robert

doi:10.1007/978-0-387-84812-9_6

Cited by 5 publications

(4 citation statements)

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“…Slavin et al . investigated the usefulness of AFC data from the transit system of New York. They pointed out that little is known about travelers' behavior in large transit systems even though there are a variety of mathematical models for transit route choice because many route alternatives exist for the same trip and no data, prior to AFC, is available to study real‐world route choices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Slavin et al . developed a schedule‐driven algorithm for searching a shortest path over the large rail transit network. This is an important consideration especially when the lines within a rail transit network have different schedules, consequently, different headways and capacities.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

“…In the case that lines within a metro transit network have different schedules, the headway and capacity information can be used in the way proposed by Slavin et al . to construct more accurate shortest paths. Nevertheless, the following assignment procedure will be exactly the same as proposed in this study.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

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Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data

Zhong

Liu

et al. 2012

J of Advced Transportation

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Literature review indicates that little is known about traveler behavior, such as transfer and route choices, in large transit systems because of the number of alternative routes involved and lack of empirical data. Even though many transit route assignment models have been developed and ample automated fare collection data have been collected, nearly no study has quantified how accurate resulting flow assignments are, especially for transfer flows. However, as a multi-stakeholder system, it is essential to estimate passenger flows over the Beijing rail transit network for revenue sharing and daily management/operation purpose. In this paper, major factors (including total travel time and transfer cost) that influence passenger flow pattern in the Beijing rail transit network are considered in a logit-based network flow assignment model. Specifically, a full transfer cost function, including transfer walking time, vehicle waiting time, and a penalty to additional transfers, is proposed to better simulate passengers' transfer behaviors. A generalized cost function for urban rail transit network is presented, and the corresponding route choice behavior of travelers is analyzed. An improved logit-based model is then presented for solving this network flow assignment problem. The depth-first method is used to search for "effective paths" among all O-D pairs. The average errors of estimated transfer flows from the proposed assignment model, which is proven to be more realistic in searching a set of effective paths, are below 20%. The results indicate that the models being developed in this study are capable of reasonably reproducing passengers' transfer and route choices and thus helpful for understanding the transfer behaviors of passengers of large rail transit networks. Copyright the fare of operational lines, except for the airport line, was adjusted to ¥2 (about $US0.3) only, and transferring between these lines does not require any additional charge.As BRTN is expanding quickly, the challenge is to predict the growth of travel demand and understand passengers' behaviors, such as line, route, and transfer location choice, and adjust the operation and management of the system accordingly. In addition, the construction of BRTN has been funded by multi-stakeholders besides the government, and there is a need to share the revenue on the basis of certain statistics, such as the travel demand served by a particular line. For example, the operator of metro line #4 is the Beijing MTR Corporation, which is financed by the Beijing Infrastructure Investment Corporation, the Beijing Capital Group, and the MTR Corporation (Hong Kong), whereas the operator of the other lines is the state-owned Beijing Subway Co., Ltd. This multi-stakeholder status makes it a necessary and critical task to scientifically predict passenger flow distribution within the BRTN for effective operation, management, and revenue allocation.An automatic fare collection (AFC) system was in effect for BRTN since June 2008. Through the AFC system, the O-D i...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Conclusion and Recommendationsmentioning

confidence: 99%

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Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data

Zhong

Liu

et al. 2012

J of Advced Transportation

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“…Several studies have investigated the feasibility of applying data from automated fare collection system for extracting passenger behavior and flows over a transit network or railway transit network . Most of these efforts have used AFC data for inferring passenger's destination choice in case of entry‐only system, or deriving the demand for each segment of a transit network.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network

Liu

et al. 2015

J of Advced Transportation

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This paper describes a case study comparing a multi-class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network-one of the largest railway transit networks in the world. Multi-class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi-class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi-class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Figure 3. Comparison of observed and estimated transfer flows by different methods.Figure 4. The mean absolute relative percentage error (MAPE) of estimated transfer flows at different stations.

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Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network

2019

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Using automated fare collection data, GIS, and dynamic schedule queries to improve transit data and transit assignment model.

Cited by 5 publications

References 1 publication

Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data

Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data

A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network

Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network

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