Short-term traffic state reasoning and precise prediction in urban networks

Abstract

The structure of urban traffic network has a significant impact on the formation and spatio-temporal pattern propagation of traffic congestions.However, in studies based on traditional traffic models or deep learning models, the generation of traffic mode can only be described indirectly by traffic indicators, without considering the traffic network feature.This makes it very difficult to accurately describe the propagation dynamics both in temporal and spatial dimensions and lacks specificity.To tackle the above-mentioned problems, a novel traffic state prediction approach based on traffic pattern reasoning (TP2) framework was proposed.The framework modeled congestion propagation as a dynamically evolving temporal knowledge graph (TKG), and applied an inferencing framework (TPP-TKG) that was based on a novel aggregator called RGraAN.TPP-TKG captured the spatial-temporal propagation pattern of traffic congestion, and combined related road links to a given link, and constructed correlated sub region of the traffic network.Then a traffic state predicting based on graph neural network was employed to predict short-term speed evolution of road links in this sub region.Comparing to the state-of-the-art benchmark models, TP2 achieves 1% ~ 2% higher accuracy.

Keywords

• traffic network structure;propagation mode;traffic mode reasoning and prediction;temporal knowledge graph;graph neural network.