Wang Xing, Su Yikun, Zheng Zhizhe, Xu Liang
School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, 150040, China.
School of Civil Engineering, Changchun Institute of Technology, Changchun, 130012, China.
Heliyon. 2024 Aug 8;10(16):e35595. doi: 10.1016/j.heliyon.2024.e35595. eCollection 2024 Aug 30.
Providing accurate prediction of the severity of traffic collisions is vital to improve the efficiency of emergencies and reduce casualties, accordingly improving traffic safety and reducing traffic congestion. However, the issue of both the predictive accuracy of the model and the interpretability of predicted outcomes has remained a persistent challenge. We propose a Random Forest optimized by a Meta-heuristic algorithm prediction framework that integrates the spatiotemporal characteristics of crashes. Through predictive analysis of motor vehicle traffic crash data on interstate highways within the United States in 2020, we compared the accuracy of various ensemble models and single-classification prediction models. The results show that the Random Forest (RF) model optimized by the Crown Porcupine Optimizer (CPO) has the best prediction results, and the accuracy, recall, f1 score, and precision can reach more than 90 %. We found that factors such as Temperature and Weather are closely related to vehicle traffic crashes. Closely related indicators were analyzed interpretatively using a geographic information system (GIS) based on the characteristic importance ranking of the results. The framework enables more accurate prediction of motor vehicle traffic crashes and discovers the important factors leading to motor vehicle traffic crashes with an explanation. The study proposes that in some areas consideration should be given to adding measures such as nighttime lighting devices and nighttime fatigue driving alert devices to ensure safe driving. It offers references for policymakers to address traffic management and urban development issues.
提供准确的交通碰撞严重程度预测对于提高应急效率和减少伤亡至关重要，从而提高交通安全并减少交通拥堵。然而，模型的预测准确性和预测结果的可解释性问题一直是一个持续的挑战。我们提出了一种由元启发式算法预测框架优化的随机森林，该框架整合了碰撞的时空特征。通过对2020年美国州际公路上机动车交通事故数据的预测分析，我们比较了各种集成模型和单分类预测模型的准确性。结果表明，由皇冠豪猪优化器（CPO）优化的随机森林（RF）模型具有最佳的预测结果，准确率、召回率、F1分数和精确率均可达到90%以上。我们发现温度和天气等因素与车辆交通事故密切相关。基于结果的特征重要性排名，使用地理信息系统（GIS）对密切相关指标进行了解释性分析。该框架能够更准确地预测机动车交通事故，并通过解释发现导致机动车交通事故的重要因素。研究建议在一些地区应考虑增加夜间照明设备和夜间疲劳驾驶警报设备等措施，以确保安全驾驶。它为政策制定者解决交通管理和城市发展问题提供了参考。
