Abstract
The passenger-flow data of urban rail transit is the basis of transport capacity arrangement, operation evaluation, and passenger density control in station area. Meanwhile it can guide strategies to prevent or reduce safety accident risks of all kinds caused by the huge passenger-flow of rail transit. Urban rail transit in china generally uses passenger's card-swiping data from automatic fare collection (AFC) system and passenger counting data from intelligent video for passenger-flow detection and statistics. But the passenger-flow detection method by AFC system has some problems like poor real-time performance, insufficient information of passengers' travelling process and so on. Whereas passenger-flow detection by intelligent video can reflect passenger-flow situation in the micro area better, but being greatly affected by camera installation angle, passenger-flow density and illumination change etc. To solve the above problems, this paper proposes a passenger-flow detection method based on Wi-Fi sniffing data, which focuses on indoor positioning principle, mechanism of positioning data's cleaning and preprocessing, method of passenger's trip-chain restoration, and application effect of passenger-flow identification of train and station etc. It provides guidance for early warning of huge passenger-flow coming in rail transit, networking operation organization and train arrangement of the whole city lines.
Published Version
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