Road Network Bottleneck Prediction Based on Inductive Congestion Propagation: A Causal Anonymous Walk Approach

As the popularity of automobiles increases, major cities around the world are facing serious problems of urban traffic congestion, and the increasingly severe traffic congestion will limit the development of cities. For urban road networks, relieving congestion at traffic bottlenecks can effectively improve overall traffic congestion, so identifying traffic bottlenecks in the road network is essential. Some researchers currently use spatial features based on section congestion propagation to predict bottlenecks, but their temporal features are also an important factor. A new bottleneck definition that considers both the temporal and spatial characteristics of congestion propagation is proposed. A congestion propagation dynamic model is built based on this definition, and the congestion propagation probability between different time periods is estimated using causal anonymous walks. The total congestion cost of road segments is then calculated, and traffic bottlenecks in the road network at different time periods are predicted. Finally, we used SUMO to simulate the Sioux city road network and collected 30 days of traffic flow information to construct a congestion propagation dataset. We then trained a causal anonymized walking network to estimate the congestion propagation probability for different time periods and predict traffic bottlenecks. By alleviating congestion on bottleneck sections, we demonstrated the effectiveness of this method in predicting bottlenecks and improving traffic flow for the road network as a whole.