Chen Feng, Chen Suren, Ma Xiaoxiang
Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.
Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA.
Int J Environ Res Public Health. 2016 Jun 18;13(6):609. doi: 10.3390/ijerph13060609.
Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling.
经常随时间变化的交通和环境条件（如天气状况）对碰撞事故的发生有重大影响。传统的具有大时间尺度和综合变量的碰撞频率模型不足以捕捉驾驶环境因素的时变特性，导致在碰撞频率建模方面关键信息大量丢失。本文旨在为复杂驾驶环境开发具有精细时间尺度的碰撞频率模型，通过这样的努力提供更详细和准确的碰撞风险信息，从而实现更有效和主动的交通管理及执法干预。利用不平衡面板数据开发了具有特定地点随机效应的零膨胀负二项式（ZINB）模型，以分析高速公路路段每小时的碰撞频率。主要来源于道路气象信息系统的实时驾驶环境信息，包括交通、天气和路面状况数据，与特定地点的道路特征一起被纳入模型。不平衡面板数据ZINB模型的估计结果表明，有许多因素影响碰撞频率，包括时变因素（如能见度和每小时交通流量）和地点变化因素（如限速）。该研究证实了实时天气、路面状况和交通数据对碰撞频率建模具有独特的重要性。
