Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, Jiangsu 210000, PR China.
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, 12800 Pegasus Dr #211, Orlando, FL 32816, USA.
Accid Anal Prev. 2024 Sep;205:107681. doi: 10.1016/j.aap.2024.107681. Epub 2024 Jun 18.
Lane change behavior disrupts traffic flow and increases the potential for traffic conflicts, especially on expressway weaving segments. Focusing on the diversion process, this study incorporating individual driving patterns into conflict prediction and causation analysis can help develop individualized intervention measures to avoid risky diversion behaviors. First, to minimize measurement errors, this study introduces a lane line reconstruction method. Second, several unsupervised clustering methods, including k-means, agglomerative clustering, gaussian mixture, and spectral clustering, are applied to explore diversion patterns. Moreover, machine learning methods, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Attention-based LSTM, eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), are employed for real-time traffic conflict prediction. Finally, mixed logit models are developed using pre-conflict condition data to investigate the causal mechanisms of traffic conflicts. The results indicate that the K-means algorithm with four clusters exhibits the highest Calinski-Harabasz and Silhouette scores and the lowest Davies-Bouldin scores. With superior classification accuracy and generalization ability, the LSTM is used to develop the personalized traffic conflict prediction model. Sensitivity analysis indicates that incorporating the diversion patterns into the LSTM model results in an improvement of 3.64% in Accuracy, 7.15% in Precision, and 1.34% in Recall. Results from the four mixed logit models indicate significant differences in factors contributing to traffic conflicts within each diversion pattern. For instance, increasing the speed difference between the target vehicle and the right preceding vehicle benefits traffic conflict during acceleration diversions but decreases the likelihood of traffic conflicts during deceleration diversions. These results can help traffic engineers propose individualized solutions to reduce unsafe diversion behavior.
变道行为会扰乱交通流并增加交通冲突的可能性，尤其是在高速公路交织段。本研究聚焦于变道过程，将个体驾驶模式纳入冲突预测和成因分析中，可以帮助制定个性化的干预措施，避免危险的变道行为。首先，为了最小化测量误差，本研究引入了车道线重构方法。其次，应用了几种无监督聚类方法，包括 k-均值、凝聚聚类、高斯混合和谱聚类，以探索变道模式。此外，还采用了卷积神经网络 (CNN)、长短期记忆 (LSTM)、基于注意力的 LSTM、极端梯度提升 (XGB)、支持向量机 (SVM) 和多层感知机 (MLP) 等机器学习方法进行实时交通冲突预测。最后，利用预冲突条件数据开发混合 logit 模型，以研究交通冲突的因果机制。结果表明，具有四个聚类的 K-均值算法表现出最高的 Calinski-Harabasz 和 Silhouette 得分以及最低的 Davies-Bouldin 得分。LSTM 具有较高的分类准确性和泛化能力，用于开发个性化的交通冲突预测模型。敏感性分析表明，将变道模式纳入 LSTM 模型可将准确性提高 3.64%，将精度提高 7.15%，将召回率提高 1.34%。四个混合 logit 模型的结果表明，在每个变道模式中，导致交通冲突的因素存在显著差异。例如，目标车辆与右侧前车之间的速度差增大有利于加速变道时的交通冲突，但会降低减速变道时的交通冲突可能性。这些结果有助于交通工程师提出个性化的解决方案，以减少不安全的变道行为。
