Dynamic Spatio-Temporal Multi-Scale Representation for Bus Ridership Prediction

• Main Authors: Peng, Lilan, Wang, Xiu, Lu, Hongchun, Guo, Xiangyu, Li, Tianrui, Ji, Shenggong
• Format: Conference Proceeding
• Language: English
• Subjects: Convolution; Feature extraction; Multi-Scale Representation Learning; Neural networks; Representation learning; Schedules; Spatio-Temporal Data Mining; Traffic Flow prediction; Transportation; Urban areas
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN54540.2023.10191107

Accurate bus ridership forecasts can help city managers develop more effective transportation plans, such as bus schedules. With the continuous development of intelligent transportation systems, many methods have been proposed to predict bus ridership. However, there are two limitations to these existing efforts. First, the existing methods mainly consider temporal properties such as closeness, period, trends, holidays, and weekends, respectively. However, the temporal characteristics can be correlated and affect each other among different time scales. Besides, the spatial interactions are dynamic and complex. Thus, how to extract and represent the high-level temporal features and the dynamic spatial dependency among multiple time scales is significant and profound for bus ridership prediction. In this paper, we propose a novel dynamic spatio-temporal multi-scale representation method DSTMR to predict bus ridership. Specifically, DSTMR consists of several dynamic spatio-temporal representation blocks (DSTs). In DST, the graph generator is used to learn the adjacency of bus stations, the graph convolution module is employed to represent the dynamic spatial relationship, and the temporal convolution module is designed to extract the high-level temporal features. To demonstrate the effectiveness of our proposed method, we first construct a dataset (ASBus) based on real-world data, which is available for further study. Extensive experiments on two real-world datasets validate the effectiveness of our method.