School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China.
School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China; Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, Hunan, China.
Accid Anal Prev. 2023 Oct;191:107203. doi: 10.1016/j.aap.2023.107203. Epub 2023 Jul 3.
Analyzing risk dynamic change mechanism under spatio-temporal effects can provide a better understanding of traffic risk, which helps reinforce the safety improvement. Traditionally, spatio-temporal studies based on crash data were mostly conducted to explore crash risk evolution mechanism from a macroscopic perspective. Dynamic change mechanism of short-term risk within a small-scale area deserves exploration, which cannot be captured in macroscopic crash-based studies. It is practical to analyze traffic conflict risk as a surrogate safety measure, which can preferably overcome the limitations of crash-based studies. This study aims to explore the spatio-temporal dynamic change mechanism of conflict risk based on trajectory data. Both conflict frequency and severity are integrated and assessed by applying fuzzy logic theory to develop the whole risk indicator. Trajectories on U.S. Highway101 from NGSIM dataset are utilized and aggregated. A two-step framework is proposed to analyze the risk dynamic change mechanism. The spatial Markov model is firstly applied to explore the transition probability of risk level, and then the panel regression approach is employed to quantify the relationship between spatio-temporal risk and traffic characteristics. Modeling results show that (1) the dynamic change trend of safety states differs under different spatial lag conditions, and it can be well depicted by the spatial Markov model; (2) dynamic spatial panel data modeling method performs better than the model that only considers temporal or spatial dependency. The novel proposed framework promotes a systematic exploration of conflict risk from a mesoscopic perspective, which contributes to assess the real-time road safety more comprehensively.
分析时空效应下的风险动态变化机制可以更好地理解交通风险，有助于加强安全改进。传统上，基于事故数据的时空研究主要是从宏观角度探索事故风险演变机制。需要探索小范围内短期风险的动态变化机制，这在宏观的基于事故的研究中是无法捕捉到的。将交通冲突风险作为替代安全措施进行分析是很实际的，它可以更好地克服基于事故的研究的局限性。本研究旨在基于轨迹数据探索冲突风险的时空动态变化机制。通过应用模糊逻辑理论来综合和评估冲突频率和严重程度，开发出整体风险指标。利用 NGSIM 数据集上的美国 101 号公路轨迹进行了汇总。提出了一个两阶段框架来分析风险动态变化机制。首先应用空间马尔可夫模型来探索风险水平的转移概率，然后采用面板回归方法来量化时空风险与交通特征之间的关系。建模结果表明：(1)在不同的空间滞后条件下，安全状态的动态变化趋势不同，空间马尔可夫模型可以很好地描述这种变化趋势；(2)动态空间面板数据建模方法比仅考虑时间或空间依赖性的模型表现更好。新提出的框架从介观角度促进了对冲突风险的系统探索，有助于更全面地评估实时道路安全。
