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A Systematic Literature Review of Metro’s Passenger Flow Prediction

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Document pages: 13 pages

Abstract: Passenger flow prediction is an essential and functional part of urban rail transit or Metro operation. An accurate passenger flow prediction helps to tackle the problem such as, reduce crowdedness and passenger waiting time, alleviate traffic congestion, or decrease incidence with traffic control and route guidance. This systematic literature review presents state-of-the-art for Metro’s passenger flow prediction algorithm over a few years. Previously, several studies have been made to proposed an algorithm to predict Metro’s passenger flow, from the traditional classical algorithm, regressive based model, machine learning-based model, and a hybrid model. We explore how often the algorithms are used in the studies over the years, the variables used in developing the algorithm, and also how each algorithm performance is measured.

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