Estimation and simulation for route choice proportion in multi-period urban rail transit control systems

As we all known, estimating the proportion of passenger route choice is of great significance in almost every aspect of urban rail transit control system, including passenger allocation, fare clearing and flow control strategies. Existing researches only pay attention to the route choice through travel time, but usually ignore the influence in different periods of the day. Therefore, this paper proposes a novel estimation method for the proportion of passenger route choice in different periods. Firstly, by introducing the normalized value of passenger flow and the standard coefficient of peak passenger flow, the train operation time is divided into peak and flat periods. Secondly, the travel time distribution of each route can be obtained by estimating the expected value and standard deviation of passenger travel time in each different period. The Naïve Bayes algorithm is further employed to realize the identification of the proportion of passenger route choices. Finally, this proposed algorithm is applied to Hangzhou Metro. The result shows that by using the segmented estimation, the error can be reduced by more than 60% compared with the whole-day experiment, which indicates the superiority of the method.