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Smart city article details

Title Transfer Inference Of Bus Passengers Based On Multi-Source Data Fusion
ID_Doc 58753
Authors Gao F.; Guan D.; Wei Y.
Year 2022
Published ACM International Conference Proceeding Series
DOI http://dx.doi.org/10.1145/3565291.3565307
Abstract Due to the continuous expansion of the city, residents' bus trips cannot avoid the transfer. However, with the development of public transport big data, multi-source bus data provides convenience for transfer inference. The existing advanced transfer inference is based on the principle of "to go", which only considers dynamic factors. Unfortunately, this method has the disadvantages of low inference accuracy and low efficiency. Therefore, we propose an algorithm combining dynamic and static spatio-temporal constraints by analyzing the travel behavior of passengers. What is more, we take into account the behavior of passengers tapping a smartcard for others. The feasibility and effectiveness of the proposed algorithm are verified by applying the multi-source public transportation data in Huangdao District, Qingdao, and the results show that the algorithm has good performance. © 2022 ACM.
Author Keywords dynamic and static constraints; Multi-source data; Public transport big data; spatio-temporal; Transfer


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