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Author:

Liang, Quan (Liang, Quan.) | Weng, Jian-Cheng (Weng, Jian-Cheng.) | Lin, Peng-Fei (Lin, Peng-Fei.) | Zhou, Wei (Zhou, Wei.) | Rong, Jian (Rong, Jian.) (Scholars:荣建)

Indexed by:

EI CSCD

Abstract:

To obtain elaborate travel characteristics and better meet travel demands for different public transport passengers, it is necessary to find ways identifying public transport commuter accurately. Based on public transport smart card transaction and network data, travel chain is obtained by data processing and matching. Taking travel behavior data of April, 2017 in Beijing, China, individual travel graph is constructed by adopting multi-layer planning theory. Seven feature indexes are extracted from individual travel graph and set as input for passenger classification model. Revealed Preference survey is conducted to collect travel behavior category attributes, which is the output of classification model. A back propagation neuron networks based public transport passenger classification model is constructed. Validation results indicate that the average classification accuracy and Kappa coefficient are 94.5% and 0.879, respectively. The study results contribute to identify public transport passengers of different types accurately and further support to optimize public transport operating and improve service level precisely. Copyright © 2018 by Science Press.

Keyword:

Smart cards Systems engineering Backpropagation Neural networks Data handling Knowledge representation

Author Community:

  • [ 1 ] [Liang, Quan]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Weng, Jian-Cheng]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Lin, Peng-Fei]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Zhou, Wei]Ministry of Transport of the People's Republic of China, Beijing; 100736, China
  • [ 5 ] [Rong, Jian]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address:

  • [weng, jian-cheng]beijing key laboratory of traffic engineering, beijing university of technology, beijing; 100124, china

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Source :

Journal of Transportation Systems Engineering and Information Technology

ISSN: 1009-6744

Year: 2018

Issue: 2

Volume: 18

Page: 100-107

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 12

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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