Abstract
We have constructed a framework for analyzing passenger behaviors in public transportation systems as understanding these variables is a key to improving the efficiency of public transportation. It uses a large-scale dataset of trip records created from smart card data to estimate passenger flows in a complex metro network. Its interactive flow visualization function enables various unusual phenomena to be observed. We propose a predictive model of passenger behavior after a train accident. Evaluation showed that it can accurately predict passenger flows after a major train accident. The proposed framework is the first step towards real-time observation and prediction for public transportation systems.
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Yokoyama, D., Itoh, M., Toyoda, M., Tomita, Y., Kawamura, S., Kitsuregawa, M. (2014). A Framework for Large-Scale Train Trip Record Analysis and Its Application to Passengers’ Flow Prediction after Train Accidents. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_44
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DOI: https://doi.org/10.1007/978-3-319-06608-0_44
Publisher Name: Springer, Cham
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