Study on Traffic Accident Forecast of Urban Excess Tunnel Considering Missing Data Filling

Abstract

Urban highway tunnels are frequent accident locations, and predicting and analyzing road conditions after accidents to avoid traffic congestion is a key measure for tunnel traffic operation management. In this paper, 200 traffic accident data from the YingTian Street Tunnel in Nanjing city were analyzed and encoded to extract the main factors affecting tunnel traffic conditions from three aspects: time, traffic flow, and tunnel environment. Next, graph convolution long short-term memory networks were used to predict and fill in missing traffic flow data. Finally, seven independent variables selected by Pearson correlation analysis were input into the constructed BP neural network and random forest model to predict tunnel traffic conditions during accidents and accident duration. Experimental results show that the accuracy of random forest and BP neural networks in predicting traffic flow is 83.39% and 82.94%, respectively, and that the absolute error of the two models in predicting accident duration is 75% and 60% within 25 min, respectively. Both models perform well in predicting traffic conditions, and the random forest models perform better in terms of robustness and generalization in predicting crash duration. The experimental results have important implications for tunnel operation management during accidents.

Keywords

• urban highway tunnels
• graph convolution long short-term memory networks
• BP neural network
• random forest model
• road conditions during accidents