Toll plazas are commonly recognized as bottlenecks on toll roads, where vehicles are prone to crashes. However, there has been a lack of research analyzing and predicting dynamic short-term crash risk specifically at toll plazas. This study utilizes traffic, geometric, and weather data to analyze and predict dynamic short-term collision occurrence probability at mainline toll plazas. A random-effects logit regression model is employed to identify crash precursors and assess their impacts on the probability of crash occurrence at toll plazas. Meanwhile, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) network is applied for crash prediction. The results of random-effects logit regression model indicate that the flow standard deviation of downstream, upstream occupancy, speed difference and occupancy difference between upstream and downstream positively influence the probability of crash occurrence. Conversely, an increase in the proportion of ETC lanes negatively impacts the probability of crash occurrence. Additionally, there appears a higher likelihood of crashes occurring during summer at toll plaza area. Furthermore, to address the issue of data imbalance, Synthetic Minority Oversampling Techniques (SMOTE) and class weight methods were employed. Stacked Sparse AutoEncoder-Long Short-Term Memory (SSAE-LSTM) and CatBoost were developed and their performance was compared with the proposed model. The results demonstrated that the LSTM-CNN model outperformed the other models in terms of the Area Under the Curve (AUC) values and the true positive rate. The findings of this study can assist engineers in selecting suitable traffic control strategies to improve traffic safety in toll plaza areas. Moreover, the developed collision prediction model can be incorporated into a real-time safety management system to proactively prevent traffic crash.

收费站通常被认为是收费公路的瓶颈，车辆很容易发生碰撞。然而，缺乏对收费站动态短期碰撞风险进行分析和预测的研究。本研究利用交通、几何和天气数据来分析和预测干线收费站的动态短期碰撞发生概率。采用随机效应 Logit 回归模型来识别碰撞前兆并评估其对收费站碰撞发生概率的影响。同时，采用长短期记忆卷积神经网络（LSTM-CNN）网络进行碰撞预测。随机效应logit回归模型结果表明，下游流量标准差、上游占用率、上下游速度差和占用率差对事故发生概率有正向影响。相反，ETC车道比例的增加会对碰撞发生的概率产生负面影响。此外，夏季收费广场地区发生车祸的可能性更高。此外，为了解决数据不平衡的问题，采用了合成少数过采样技术（SMOTE）和类别权重方法。开发了堆叠稀疏自动编码器-长短期记忆 (SSAE-LSTM) 和 CatBoost，并将它们的性能与所提出的模型进行了比较。结果表明，LSTM-CNN 模型在曲线下面积 (AUC) 值和真阳性率方面优于其他模型。这项研究的结果可以帮助工程师选择合适的交通控制策略，以改善收费广场地区的交通安全。此外，所开发的碰撞预测模型可以纳入实时安全管理系统，以主动预防交通事故。