Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge
This addresses traffic prediction for transportation services like car-sharing and ride-hailing, but it is incremental as it builds on existing regional knowledge incorporation methods.
The paper tackles the problem of mid-term road traffic prediction by proposing a novel method that embeds real-time region-level knowledge (e.g., POIs, satellite images, LTE traces) and converts it to road-level predictions, outperforming baselines in experiments on real-world data.
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.