A Passenger Flow Analysis Method Through Ride Behaviors on Massive Smart Card Data

Weilong Ding; Zhuofeng Zhao; Han Li; Yaqi Cao; Yang Xu

Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings

Research Article

A Passenger Flow Analysis Method Through Ride Behaviors on Massive Smart Card Data

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@INPROCEEDINGS{10.1007/978-3-030-00916-8_35,
    author={Weilong Ding and Zhuofeng Zhao and Han Li and Yaqi Cao and Yang Xu},
    title={A Passenger Flow Analysis Method Through Ride Behaviors on Massive Smart Card Data},
    proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings},
    proceedings_a={COLLABORATECOM},
    year={2018},
    month={10},
    keywords={Smart card data Passenger flow Ride behavior Urban computing},
    doi={10.1007/978-3-030-00916-8_35}
}

Weilong Ding
Zhuofeng Zhao
Han Li
Yaqi Cao
Yang Xu
Year: 2018
A Passenger Flow Analysis Method Through Ride Behaviors on Massive Smart Card Data
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-00916-8_35

Weilong Ding^,*, Zhuofeng Zhao, Han Li, Yaqi Cao¹, Yang Xu²

1: Chinese Academy of Sciences
2: Beijing E-Hualu Information Technology Co., Ltd.

*Contact email: dingweilong@ncut.edu.cn

Abstract

In transportation business, the analysis counts the ridership of given bus stops on given time duration. On the smart card data from the card readers of buses, the calculation of passenger flow faces challenges: the accuracy or the latency is blamed, and the scalability is poor on large volume data. In this paper, we propose an effective method on massive smart card data, in which ride behaviors are modeled and the passenger flow can be achieved and efficiently. Our method is implemented by Hadoop MapReduce, and proves minute-level latencies on weekly historical data with nearly linear scalability.

Keywords: Smart card data Passenger flow Ride behavior Urban computing

Published: 2018-10-17
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-00916-8_35