期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 45, 期 3, 页码 3574-3589出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2022.3178184
关键词
Time series analysis; Sparse matrices; Predictive models; Task analysis; Transformers; Public transportation; Forecasting; Causality and correlation; heterogeneous information; online metro system; origin-destination ridership
In this work, a novel neural network module called Heterogeneous Information Aggregation Machine (HIAM) is proposed to jointly learn the evolutionary patterns of OD and DO ridership by fully exploiting heterogeneous information of historical data. Based on the proposed HIAM, a unified Seq2Seq network is developed to forecast the future OD and DO ridership simultaneously.
Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD matrices of previous time intervals can not be obtained immediately in online metro systems, and conventional methods only used limited information to forecast the future OD and DO ridership separately. In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership. Specifically, an OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Moreover, a Dual Information Transformer is introduced to propagate the mutual information among OD features and DO features for modeling the OD-DO causality and correlation. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously. Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction. Our code is resealed at https://github.com/HCPLab-SYSU/HIAM.
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