Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

• URL: http://arxiv.org/abs/2107.00946v2
• Date: Mon, 5 Jul 2021 01:28:16 GMT
• Title: Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
• Authors: Lingbo Liu, Yuying Zhu, Guanbin Li, Ziyi Wu, Lei Bai, Mingzhi Mao, Liang Lin
• Abstract summary: We propose a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM) to jointly learn the evolutionary patterns of OD and DO ridership. An OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices. A DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously.
• Score: 99.54200992904721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metro origin-destination prediction is a crucial yet challenging task for intelligent transportation management, 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.

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