Abstract: Urban rail transit systems often face issues of passenger congestion during periods of insufficient capacity. To address this problem, it is crucial to forecast future network congestion and provide passengers with optimal travel routes based on these predictions, thereby achieving balanced passenger flow in the rail transit network. Therefore, this paper proposes a method for recommending urban rail transit travel routes based on Origin-Destination (OD) prediction. This method utilizes graph convolutional networks and gated recurrent units to predict OD passenger flows in the urban rail transit network. Subsequently, it calculates the future traffic volume of rail sections as an indicator of congestion level. Furthermore, a personalized travel route recommendation method based on route cost is designed to suggest travel routes for passengers. By computing the cost of K shortest-time routes, the method prioritizes future travel routes for passengers. Case studies are conducted using the Shanghai Metro as a research subject to validate the proposed model. The results demonstrate the model's accurate prediction of OD passenger flows and the reasonableness of the recommended personalized travel routes for passengers.
邢 雪, 王 兵. 基于OD预测的城市轨道交通出行路线推荐方法[J]. 吉林化工学院学报, 2026, 43(1): 23-28.
XING Xue, WANG Bing. Recommended Urban Rail Transit Travel Route Method based on OD Prediction. Journal of Jilin Institute of Chemical Technology, 2026, 43(1): 23-28.
