Detection and analysis of transfer time in urban rail transit system using WIFI data
Yan Li, Si-rui Nan, Yue Guo, Caihua Zhu, Duo Li
Abstract
This study presents a comprehensive framework for estimating passengers' transfer times and extracting their distribution and related transfer routes using WIFI probe data. The departure time of preceding station, arrival time of subsequent station, and train running time are selected to obtain transfer times. Then, the collected data is analyzed using kernel density estimation to obtain candidate distribution. Gaussian mixture models are adopted to extract the distribution of each possible transfer route at both peak hours and off-peak hours. This method is tested at two transfer stations of Xi’an metro system with the comparison of results from automatic fare collection data and manual sampling survey data. The results indicate that the proposed approach can collect the transfer time with a sampling ratio greater than 30% and a deviation less than 5%. The route choice behaviors and distribution of transfer time under various conditions can be identified using the proposed methods.