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

Si, Bingfeng; Zhong, Ming; Liu, Jianfeng; Gao, Ziyou; Wu, Jianjun

doi:10.1002/atr.1203

Cited by 32 publications

(35 citation statements)

References 26 publications

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“…Guo and Wilson's transfer penalty was defined as a psychological perception of the transfer environment (not a specific component of the transfer time). Si et al (2013) proposed a full transfer cost function to simulate passengers' transfer behaviors. The cost also includes walking time, vehicle waiting time, and a transfer penalty.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Isolating high-priority metro and feeder bus transfers using smart card data

et al. 2016

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Fixed-rail metro (or 'subway') infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service's operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers' transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.

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Section: Background and Literature Reviewmentioning

confidence: 99%

“…Inspired by the studies of Si et al (2013) and Guo and Wilson (2011), this research divided total transfer time into three parts: the expected walking time E(wl), the expected waiting time E(w), and the delay time DT. Figure 3 illustrates these three components of transfer time.…”

Section: Task 3: Estimate Transfer Time Componentsmentioning

confidence: 99%

Isolating high-priority metro and feeder bus transfers using smart card data

et al. 2016

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“…Compared with all-or-nothing assignment method, this model was more practical and reasonable, according to the Beijing AFC data. Si [7] made a survey about the passenger travel behavior in Beijing URT system. Some parameters were modified and the general framework of passenger flow assignment was presented in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Si [6] proposed a modified logit-based passenger flow assignment model, using automatic fare collection (AFC) data as basic data. This paper also adopted an amplification factor for transfer time.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of a Behavior-Based Passenger Flow Assignment Model for Urban Rail Transit in Section Interruption Circumstance

Teng

Liu

2015

Urban Rail Transit

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At present, the urban rail transit (URT) system has achieved network operation in many major cities of China. But, little attention has been given to the vulnerability of the URT system. The purpose of this study is to assign the passenger flow under the condition of section interruption in URT system. Two surveys (a passenger behavior survey and a stated preference survey) were conducted and a multinomial logit model was developed. The results show that although the first choice of passengers in emergency situation is to stay in URT system by a circuitous way, more than half of the respondents express interest in the temporary shuttle bus. For the temporary shuttle bus, the sensitivity analysis show that the relative speed is more important than crowding degree for passengers. The significant variables mostly fall in the personal attributes such as income, gender, age, etc. The impacts of trip feature factors are similar to the previous research in normal situations. These results provide basic support for passenger flow assignment at the shuttle bus level and reducing the risk of crowding at some special stations. Moreover, it is also good for reducing passenger delay and recovering the trip.

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

Cited by 32 publications

References 26 publications

Isolating high-priority metro and feeder bus transfers using smart card data

Isolating high-priority metro and feeder bus transfers using smart card data

Development of a Behavior-Based Passenger Flow Assignment Model for Urban Rail Transit in Section Interruption Circumstance

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

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