The “Visual-Behavior” Chain and Risk Prediction Model for Sedan Drivers Under the Influence of Container Trucks: A Case Study of Yangshan Port Freight Corridor

Abstract

With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers’ visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a “Visual-behavior” chain model was established. The relationship between drivers’ visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time-series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and ARIMA-LSTM. The results indicate that the ARIMA-LSTM model shows the most effective prediction performance. This research provides a field-data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large-scale container ports.