Smarttransfer: Modeling the Spatiotemporal Dynamics of Passenger Transfers for Crowdedness-Aware Route Recommendations


In urban transportation systems, transfer stations refer to hubs connecting a variety of bus and subway lines and, thus, are the most important nodes in transportation networks. The pervasive availability of large-scale travel traces of passengers, collected from automated fare collection (AFC) systems, has provided unprecedented opportunities for understanding citywide transfer patterns, which can benefit smart transportation, such as smart route recommendation to avoid crowded lines, and dynamic bus scheduling to enhance transportation efficiency. To this end, in this article, we provide a systematic study of the measurement, patterns, and modeling of spatiotemporal dynamics of passenger transfers. Along this line, we develop a data-driven analytical system for modeling the transfer volumes of each transfer station. More specifically, we first identify and quantify the discriminative patterns of spatiotemporal dynamics of passenger transfers by utilizing heterogeneous sources of transfer related data for each station. Also, we develop a multi-task spatiotemporal learning model for predicting the transfer volumes of a specific station at a specific time period. Moreover, we further leverage the predictive model of passenger transfers to provide crowdedness-aware route recommendations. Finally, we conduct the extensive evaluations with a variety of real-world data. Experimental results demonstrate the effectiveness of our proposed modeling method and its applications for smart transportation.


Computer Science

Research Center/Lab(s)

Intelligent Systems Center


This research was supported in part by National Natural Science Foundation of China via Grant no. 51778033. This research was supported in part by Beijing Municipal Science and Technology Project no. Z171100000917016.

Keywords and Phrases

Bus transportation; Dynamics; Transfer stations; Transportation routes; Automated fare collection; Route recommendation; Spatio-temporal dynamics; Spatio-temporal learning; Spatiotemporal; Transit behavior; Transportation efficiency; Urban transportation systems; Urban transportation; Crowdedness detection

International Standard Serial Number (ISSN)

2157-6904; 2157-6912

Document Type

Article - Journal

Document Version


File Type





© 2018 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Nov 2018