On Traffic Prediction With Knowledge-Driven Spatial–Temporal Graph Convolutional Network Aided by Selected Attention Mechanism

Abstract

Intelligent transportation systems grapple with the formidable task of precisely forecasting real-time traffic conditions, where the traffic dynamics exhibit intricacies arising from spatial and temporal dependencies. The urban road network presents a complex web of interconnected roads, where the state of traffic on one road can influence the conditions of others. Moreover, the prediction of traffic conditions necessitates the consideration of diverse temporal factors. Notably, the proximity of a time point to the present moment wields a more substantial impact on subsequent states. In this paper, we propose the knowledge-driven graph convolutional network (KGCN) aided by the gated recurrent unit with a selected attention mechanism (GSAM) to predict traffic flow. In particular, KGCN is employed to capture the correlation of the external knowledge factors for the road and the spatial dependencies, and the gated recurrent unit (GRU) is used to cope with temporal dependence. Furthermore, to improve traffic prediction accuracy, we propose the GRU combined with a selected attention mechanism with Gumble-Max to predict traffic at the temporal dimension, where a selector is chosen to dynamically assign the feature in various time intervals with different weights. Experimental results with real-life data show the proposed KGCN with GSAM can achieve high accuracy in traffic prediction. Compared to the traditional traffic prediction method, the proposed KGCN with GSAM can achieve higher efficacy and robustness when capturing global dynamic temporal dependencies, external knowledge factor correlations, and spatial correlations.