Compared with traditional traffic studies, real-time safety analyses can be better incorporated into proactive traffic management strategies to improve traffic safety. However, few studies have investigated the real-time pedestrian safety model. Intersections usually have mixed traffic conditions with more pedestrian-vehicle interactions. This paper uses conflict indicators, PET (Post Encroachment Time) and TTC (Time to Collision) to identify pedestrians’ conflicts from CCTV (closed-circuit television) videos. The high-resolution traffic data from the Automated Traffic Signal Performance Measures (ATSPM) system are used to derive traffic flow-related variables. The pedestrian exposure is also estimated. Pedestrians’ conflicts are predicted using multiple machine learning models and Logistic Regression. The resampling methods, random over-sampling, and random under-sampling are compared. The best model, Extreme Gradient Boosting (XGBT) with random over-sampling method can achieve AUC (area under the ROC curve) value of 0.841 and recall value of 0.739 on the test data set. The proposed model can predict pedestrians’ conflicts one cycle ahead, which can be 2–3 min. The proposed model has the potential to be implemented in the Connected and Automated Vehicles (CAV) environment to adjust signal timing accordingly and enhance traffic safety.

中文翻译：

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使用机器学习模型的信号周期级别的实时行人冲突预测模型


与传统的交通研究相比，实时安全分析可以更好地融入主动交通管理策略中，以提高交通安全。然而，很少有研究调查实时行人安全模型。十字路口通常交通状况复杂，行人与车辆的互动较多。本文使用冲突指标 PET（入侵后时间）和 TTC（碰撞时间）来识别闭路电视视频中的行人冲突。来自自动交通信号性能测量 (ATSPM) 系统的高分辨率交通数据用于导出与交通流相关的变量。行人的暴露量也被估计。使用多种机器学习模型和逻辑回归来预测行人冲突。比较了重采样方法、随机过采样和随机欠采样。最好的模型，采用随机过采样方法的极限梯度提升（XGBT）可以在测试数据集上实现0.841的AUC（ROC曲线下面积）值和0.739的召回值。所提出的模型可以提前一个周期（可能是 2-3 分钟）预测行人冲突。所提出的模型有可能在联网和自动驾驶车辆（CAV）环境中实施，以相应地调整信号定时并提高交通安全。