Mukherjee Dipanjan, Mitra Sudeshna
Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Transport Specialist, Global Road Safety Facility, The World Bank, Washington, DC, USA.
Int J Inj Contr Saf Promot. 2022 Jun;29(2):160-181. doi: 10.1080/17457300.2021.1973509. Epub 2021 Sep 6.
In developing nations, road traffic crashes involving pedestrians have become a foremost worry. Presently, most of the road safety assessment projects and selection of interventions are still restricted to traditional methods that depend on historical crash data. However, in low and middle-income countries such as India, the availability, reliability, and accuracy of crash data are uncertain. Alternatively, Post Encroachment Time (PET) has added attention as a proximal indicator to examine pedestrian-vehicular potential crashes and address pedestrian risk under mixed traffic conditions. Hence, it will be meaningful to examine if the PET is a good substitute for pedestrian-vehicular crashes and if so, what built environment and pedestrian-level factors influence PET. In this background, the present study establishes a mathematical association between the average PET value of the urban road network level and actual crashes. Afterward, multiple linear regression models are developed to study the impact of the built environment, traffic parameters, and pedestrian-level attributes on PET. The outcomes indicate that vehicle speed, lack of enforcement, absence of traffic signal (for traffic as well as pedestrians), land use type, slum population, inadequate sight distance, pedestrian's state of crossing, and pedestrian's risky crossing behaviour substantially affect the average PET at road network-level.
在发展中国家,涉及行人的道路交通事故已成为一个首要担忧问题。目前,大多数道路安全评估项目和干预措施的选择仍局限于依赖历史事故数据的传统方法。然而,在印度等中低收入国家,事故数据的可获取性、可靠性和准确性尚不确定。另外,侵入后时间(PET)作为一种近端指标,在研究行人与车辆潜在碰撞以及解决混合交通条件下的行人风险方面受到了更多关注。因此,研究PET是否是行人与车辆碰撞事故的良好替代指标,如果是,哪些建成环境和行人层面的因素会影响PET,将具有重要意义。在此背景下,本研究建立了城市道路网络层面的平均PET值与实际事故之间的数学关联。随后,开发了多元线性回归模型,以研究建成环境、交通参数和行人层面属性对PET的影响。结果表明,车速、执法不力、交通信号(包括交通信号灯和行人信号灯)缺失、土地利用类型、贫民窟人口、视距不足、行人的过街状态以及行人的危险过街行为,对道路网络层面的平均PET有显著影响。