Stephan Karen L, Newstead Stuart V
a Monash University Accident Research Centre, Monash University , Melbourne , Victoria , Australia.
Traffic Inj Prev. 2014;15 Suppl 1:S74-80. doi: 10.1080/15389588.2014.930450.
Modeling crash risk in urban areas is more complicated than in rural areas due to the complexity of the environment and the difficulty obtaining data to fully characterize the road and surrounding environment. Knowledge of factors that impact crash risk and severity in urban areas can be used for countermeasure development and the design of risk assessment tools for practitioners. This research aimed to identify the characteristics of the road and roadside, surrounding environment, and sociodemographic factors associated with single-vehicle crash (SVC) frequency and severity in complex urban environments, namely, strip shopping center road segments.
A comprehensive evidence-based list of data required for measuring the influence of the road, roadside, and other factors on crash risk was developed. The data included a broader range of factors than those traditionally considered in accident prediction models. One hundred and forty-two strip shopping segments located on arterial roads in metropolitan Melbourne, Australia, were identified. Police-reported casualty data were used to determine how many SVC occurred on the segments between 2005 and 2009. Data describing segment characteristics were collected from a diverse range of sources; for example, administrative government databases (traffic volume, speed limit, pavement condition, sociodemographic data, liquor licensing), detailed maps, on-line image sources, and digital images of arterial roads collected for the Victorian state road authority. Regression models for count data were used to identify factors associated with SVC frequency. Logistic regression was used to determine factors associated with serious and fatal outcomes.
One hundred and seventy SVC occurred on the 142 selected road segments during the 5-year study period. A range of factors including traffic exposure, road cross section (curves, presence of median), road type, requirement for sharing the road with other vehicle types (trams and bicycles), roadside poles, and local amenities were associated with SVC frequency. A different set of risk factors was associated with the odds of a crash leading to a severe outcome: segment length, road cross section (curves, carriageway width), pavement condition, local amenities and vehicle, and driver factors. The presence of curves was the only factor associated with both SVC frequency and severity.
A range of risk factors were associated with SVC frequency and severity in complex urban areas (metropolitan shopping strips), including traditionally studied characteristics such as traffic density and road design but also less commonly studied characteristics such as local amenities. Future behavioral research is needed to further investigate how and why these factors change the risk and severity of crashes before effective countermeasures can be developed.
由于城市环境的复杂性以及获取能全面描述道路和周边环境的数据存在困难，在城市地区对碰撞风险进行建模比在农村地区更为复杂。了解影响城市地区碰撞风险和严重程度的因素，可用于制定对策以及为从业者设计风险评估工具。本研究旨在确定在复杂的城市环境中，即带状购物中心路段，与单车碰撞（SVC）频率和严重程度相关的道路、路边、周边环境以及社会人口因素的特征。
制定了一份基于证据的综合数据清单，用于衡量道路、路边及其他因素对碰撞风险的影响。这些数据所包含的因素范围比事故预测模型中传统考虑的因素更广泛。在澳大利亚墨尔本大都市的主干道上确定了142个带状购物中心路段。利用警方报告的伤亡数据来确定2005年至2009年期间这些路段发生了多少起单车碰撞事故。从各种不同来源收集描述路段特征的数据；例如，政府行政数据库（交通流量、限速、路面状况、社会人口数据、酒类许可证发放情况）、详细地图、在线图像来源以及为维多利亚州道路管理局收集的主干道数字图像。使用计数数据回归模型来确定与单车碰撞频率相关的因素。逻辑回归用于确定与严重和致命后果相关的因素。
在为期5年的研究期间，142个选定的道路路段发生了170起单车碰撞事故。一系列因素与单车碰撞频率相关，包括交通暴露程度、道路横截面（弯道、中央分隔带的存在情况）、道路类型、与其他车辆类型（电车和自行车）共用道路的要求、路边电线杆以及当地便利设施。另一组风险因素与碰撞导致严重后果的几率相关：路段长度、道路横截面（弯道、车道宽度）、路面状况、当地便利设施以及车辆和驾驶员因素。弯道的存在是唯一与单车碰撞频率和严重程度都相关的因素。
在复杂的城市地区（大都市购物街），一系列风险因素与单车碰撞频率和严重程度相关，包括传统研究的特征如交通密度和道路设计，也包括较少研究的特征如当地便利设施。在制定有效的对策之前，需要未来的行为研究进一步调查这些因素如何以及为何会改变碰撞的风险和严重程度。
