Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Department of Civil Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.
Int J Environ Res Public Health. 2022 May 8;19(9):5726. doi: 10.3390/ijerph19095726.
Road traffic crashes cause social, economic, physical and emotional losses. They also reduce operating speed and road capacity and increase delays, unreliability, and productivity losses. Previous crash duration research has concentrated on individual crashes, with the contributing elements extracted directly from the incident description and records. As a result, the explanatory variables were more regional, and the effects of broader macro-level factors were not investigated. This is in contrast to crash frequency studies, which normally collect explanatory factors at a macro-level. This study explores the impact of various factors and the consistency of their effects on vehicle crash duration and frequency at a macro-level. Along with the demographic, vehicle utilisation, environmental, and responder variables, street network features such as connectedness, density, and hierarchy were added as covariates. The dataset contains over 95,000 vehicle crash records over 4.5 years in Greater Sydney, Australia. Following a dimension reduction of independent variables, a hazard-based model was estimated for crash duration, and a Negative Binomial model was estimated for frequency. Unobserved heterogeneity was accounted for by latent class models for both duration and frequency. Income, driver experience and exposure are considered to have both positive and negative impacts on duration. Crash duration is shorter in regions with a dense road network, but crash frequency is higher. Highly connected networks, on the other hand, are associated with longer length but lower frequency.
道路交通事故造成社会、经济、身体和情感损失。它们还降低了运行速度和道路容量，并增加了延误、不可靠性和生产力损失。以前的碰撞持续时间研究集中在单个碰撞上，从事故描述和记录中直接提取了促成因素。因此，解释变量更具区域性，没有研究更广泛的宏观层面因素的影响。这与碰撞频率研究形成对比，后者通常在宏观层面收集解释因素。本研究探讨了各种因素的影响及其对宏观层面车辆碰撞持续时间和频率的影响的一致性。除了人口统计学、车辆利用、环境和响应者变量外，网络特征，如连通性、密度和层次结构，也被添加为协变量。该数据集包含了澳大利亚大悉尼地区超过 4.5 年的超过 95,000 起车辆碰撞记录。在对自变量进行降维后，对碰撞持续时间进行了基于风险的模型估计，对频率进行了负二项式模型估计。对持续时间和频率都采用潜在类别模型来解释未观察到的异质性。收入、驾驶经验和暴露被认为对持续时间有积极和消极的影响。在道路网络密集的地区，碰撞持续时间更短，但碰撞频率更高。高度连通的网络则与更长的长度但更低的频率相关。
