Exposure to risk is a theoretically important correlate of crash risk, but many safety performance functions (SPFs) for pedestrian and bicycle traffic have yet to include the mode-specific measures of exposure. When SPFs are used in the systematic approach to assess network-wide crash risk, the omission of the exposure potentially could affect the identification of high-risk locations. Using crash data from Minneapolis, this study constructs and compares two sets of SPFs, one with pedestrian and bicycle exposure variables and the other without, for network-wide intersection and mid-block crash models. Inclusion of mode-specific exposure variables improves model validity and measures of goodness-of-fit and increases accuracy of predictions of pedestrian and bicycle crash risk. Including these exposure variables in the SPFs changes the distribution of high-risk locations, including the proportion of high-risk locations in low-income and racially concentrated areas. These results confirm the importance of incorporating exposure measures within SPFs and the need for pedestrian and bicycle monitoring programs to generate exposure data.

中文翻译：

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行人和自行车暴露对明尼阿波利斯碰撞风险的影响

风险暴露在理论上是碰撞风险的重要关联因素，但行人和自行车交通的许多安全性能函数 (SPF) 尚未包括特定模式的暴露测量。当在系统方法中使用 SPF 来评估网络范围内的崩溃风险时，暴露的遗漏可能会影响高风险位置的识别。使用来自明尼阿波利斯的碰撞数据，本研究构建并比较了两组 SPF，一组包含行人和自行车暴露变量，另一组没有，用于网络范围的交叉路口和中间街区碰撞模型。包含特定于模式的暴露变量提高了模型的有效性和拟合优度的度量，并提高了行人和自行车碰撞风险预测的准确性。在 SPF 中包含这些暴露变量会改变高风险地点的分布，包括低收入和种族集中地区的高风险地点的比例。这些结果证实了将暴露措施纳入 SPF 的重要性，以及行人和自行车监测计划生成暴露数据的必要性。