Intelligent Route Choice Model for Passengers’ Movement in Subway Stations

Abstract

Current practice of designing subway stations usually based on relevant design guidebooks and experiences of the designers. Improper station design may lead to bottleneck areas which may reduce the efficiency of the passenger flow. In Hong Kong, microscopic pedestrian movement models have been adopted to predict the pedestrian flow patterns inside subway stations. However, the route choice decisions are required to be pre-defined by the designers. In reality, a passenger should make the decision based on the visual information he/she received. This study collected the actual pedestrian behaviors from subway stations and adopted support vector machine to simulate the decision making on route choice. The results showed that, with 95 % confidence level, the percentage of correct prediction achieved almost 80 %.