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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.
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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