CrowdAtlas: Estimating Crowd Distribution within the Urban Rail Transit System

Jinlong E, Mo Li, Jianqiang Huang

Abstract: While the urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, the precise awareness of how the human crowd is distributed within the urban rail transit system is highly necessary, which serves to a range of important applications including emergency response, transit recommendation, commercial valuation, etc. Most urban rail transit systems are closed systems where once entered the travelers are free to move around all stations that are connected into the system and are difficult to track. In this paper, we attempt to estimate the crowd distribution within the urban rail transit system based only on the entrance and exit records of all the rail riders. Specifically, we study Singapore MRT (Mass Rapid Transit) as a vehicle and leverage the tap-in and tap-out records of the EZ-Link transit cards to estimate the crowd distribution. Guided by a key observation that the passenger inflows and arrival flows at various MRT stations are spatio-temporally correlated due to behavioral consistence of MRT riders, we design and implement a machine learning based solution, CrowdAtlas, that accurately estimates the crowd distribution within the MRT system. Our trace-driven performance evaluation demonstrates the effectiveness of CrowdAtlas.