Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge

TL;DR

This paper introduces a novel spatio-temporal prediction model that integrates real-time regional knowledge like POIs, satellite images, and LTE traces to improve mid-term road traffic forecasting accuracy.

Contribution

It proposes a new method combining regional and road-level spatio-temporal correlations using dynamic convolution, temporal attention, and bipartite spatial transform attention.

Findings

Model outperforms baseline methods on real-world data

Effective integration of regional knowledge improves prediction accuracy

Demonstrates the importance of multi-modal regional data in traffic forecasting

Abstract

For traffic prediction in transportation services such as car-sharing and ride-hailing, mid-term road traffic prediction (within a few hours) is considered essential. However, the existing road-level traffic prediction has mainly studied how significantly micro traffic events propagate to the adjacent roads in terms of short-term prediction. On the other hand, recent attempts have been made to incorporate regional knowledge such as POIs, road characteristics, and real-time social events to help traffic prediction. However, these studies lack in understandings of different modalities of road-level and region-level spatio-temporal correlations and how to combine such knowledge. This paper proposes a novel method that embeds real-time region-level knowledge using POIs, satellite images, and real-time LTE access traces via a regional spatio-temporal module that consists of dynamic convolution and temporal attention, and conducts bipartite spatial transform attention to convert into road-level knowledge. Then the model ingests this embedded knowledge into a road-level attention-based prediction model. Experimental results on real-world road traffic prediction show that our model outperforms the baselines.