Abstract:

In order to study the characteristics of lane changing conflicts involving trucks on the expressway， reduce the risk， and explore the significant factors that cause traffic conflicts among lane changing behaviors of trucks. Firstly， this paper considered the traffic conflict measurement indicators of time and space dimensions， proposed judgment indexes， and used the extracted traffic conflict indexes as a category of samples in the conflict prediction model. Secondly， the XGBoost algorithm was adopted to build the prediction model， and its performance was compared with the Random Forest （RF） and Gradient Boosting Decision Tree （GBDT） algorithms. Finally， the SHAP algorithm was used to study the impact of traffic conflicts on truck lane changes， including the lane changing characteristics of trucks， the relative motion state between different vehicles， and traffic flow. The results show that the XGBoost model has a better prediction effect， with an accuracy and F1-score of 83.87% and 84.85%， respectively. The lane changing safety of trucks is greatly affected by the motion status of the preceding vehicle in the original and target lanes， and the occurrence of collision is negatively correlated with the vehicle length and traffic volume of the road section per minute. Under the interaction of multiple factors， when the traffic volume of a section is more than 70 pcu per minute， the probability of collision when trucks change lanes increases with the increase of the proportion of trucks in the traffic flow. When the vehicle length is less than 8 m， the possibility of truck collision is positively correlated with the speed along the lane line.

Key words: traffic engineering, expressway, trucks lane changing behavior, conflict prediction, XGBoost