Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting

TL;DR

This paper introduces a multi-spatial graph convolution Seq2Seq model for traffic forecasting that incorporates reachability knowledge and multi-view features to improve long-term prediction accuracy under noisy or limited data conditions.

Contribution

The paper proposes a novel approach combining reachability-based attention, multi-view feature fusion, and multiple spatial correlations to enhance multi-step traffic prediction performance.

Findings

Outperforms existing models on real-world datasets

Effectively handles noisy and insufficient data scenarios

Improves long-term traffic forecasting accuracy

Abstract

Accurate traffic state prediction is the foundation of transportation control and guidance. It is very challenging due to the complex spatiotemporal dependencies in traffic data. Existing works cannot perform well for multi-step traffic prediction that involves long future time period. The spatiotemporal information dilution becomes serve when the time gap between input step and predicted step is large, especially when traffic data is not sufficient or noisy. To address this issue, we propose a multi-spatial graph convolution based Seq2Seq model. Our main novelties are three aspects: (1) We enrich the spatiotemporal information of model inputs by fusing multi-view features (time, location and traffic states) (2) We build multiple kinds of spatial correlations based on both prior knowledge and data-driven knowledge to improve model performance especially in insufficient or noisy data cases. (3) A spatiotemporal attention mechanism based on reachability knowledge is novelly designed to produce high-level features fed into decoder of Seq2Seq directly to ease information dilution. Our model is evaluated on two real world traffic datasets and achieves better performance than other competitors.