Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, 32816, United States.
Accid Anal Prev. 2021 Feb;150:105924. doi: 10.1016/j.aap.2020.105924. Epub 2020 Dec 17.
Pedestrian and bicycle safety is a key component in traffic safety studies. Various studies were conducted to address pedestrian and bicycle safety issues for intersections, road segments, etc. However, only a few studies investigated pedestrian and bicycle safety for bus stops, which usually have a relatively larger volume of pedestrians and bicyclists. Moreover, traditional reactive safety approaches require a significant number of historical crashes, while pedestrian and bicycle crashes are usually rare events. Alternatively, surrogate safety measures could proactively evaluate traffic safety status when crash data are rare or unavailable. This paper utilized critical bus driving events extracted from GPS trajectory data as pedestrian and bicycle surrogate safety measures for bus stops. A city-wide trajectory data from Orlando, Florida was used, which contains around 300 buses, 6,700,000 GPS records, and 1300 bus stops. Three critical driving events were identified based on the buses' acceleration rates and stop time; hard acceleration, hard deceleration, and long stop. The relationships between critical driving events and crashes were examined using Spearman's rank correlation coefficient. All three events were positively correlated with pedestrian and bicycle crashes. Long stop event has the highest correlation coefficient, followed by hard acceleration and hard deceleration. A Bayesian negative binomial model incorporating spatial correlation (Bayesian NB-CAR) was built to estimate the pedestrian and bicycle crash frequency using the generated events. The results were consistent with the correlation estimation. For example, hard acceleration and long stop events were both positively related to pedestrian and bicycle crashes. Moreover, model evaluation results indicated that the proposed Bayesian NB-CAR outperformed the standard Bayesian negative binomial model with lower Watanabe-Akaike Information Criterion (WAIC) and Deviance Information Criteria (DIC) values. In conclusion, this paper suggests the use of critical bus driving events as surrogate safety measures for pedestrian and bicycle crashes, which could be implemented in a proactive traffic safety management system.
行人和自行车安全是交通安全研究的一个关键组成部分。已经进行了各种研究来解决交叉口、路段等的行人和自行车安全问题。然而，只有少数研究调查了公共汽车站的行人和自行车安全问题，这些地方通常有相对较多的行人和骑自行车的人。此外，传统的反应性安全方法需要大量的历史碰撞数据，而行人和自行车碰撞通常是罕见事件。相反，替代安全措施可以在碰撞数据稀少或不可用时主动评估交通安全状况。本文利用从 GPS 轨迹数据中提取的关键公共汽车驾驶事件作为公共汽车站的行人和自行车替代安全措施。使用了来自佛罗里达州奥兰多市的全市范围的轨迹数据，其中包含大约 300 辆公共汽车、670 万条 GPS 记录和 1300 个公共汽车站。基于公共汽车的加速度率和停车时间，确定了三个关键驾驶事件；急加速、急刹车和长时间停车。使用 Spearman 秩相关系数检查了关键驾驶事件与碰撞之间的关系。所有三个事件都与行人和自行车碰撞呈正相关。长时间停车事件的相关系数最高，其次是急加速和急刹车。建立了一个包含空间相关性的贝叶斯负二项式模型（贝叶斯 NB-CAR），以使用生成的事件来估计行人和自行车的碰撞频率。结果与相关性估计一致。例如，急加速和长时间停车事件都与行人和自行车碰撞呈正相关。此外，模型评估结果表明，所提出的贝叶斯 NB-CAR 优于标准贝叶斯负二项式模型，具有较低的 Watanabe-Akaike 信息准则（WAIC）和偏差信息准则（DIC）值。总之，本文建议使用关键公共汽车驾驶事件作为行人和自行车碰撞的替代安全措施，可用于主动交通安全管理系统。
