In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve ‘unintended pedestrians’, drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not ‘typical pedestrians’, a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014–2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian’s FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35–44 years, and spring season. The interaction of ‘pedestrian impairment’ and ‘weekend’ was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the ‘unintended pedestrians’ about the crash scenarios on the interstate and reduce these unexpected incidents.

中文翻译：

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调查州际高速公路上的行人车辆碰撞事故：应用均值异质性的随机参数二元 Logit 模型

在美国，州际高速公路系统被归类为受控通道或限制通道路线，行人进入或穿越此类高速公路是非法的。然而，州际公路系统上的行人与车辆碰撞事故构成了一个独特的安全问题。大多数事故涉及“无意的行人”、从故障车辆中走出来的司机，或者由于参与了之前州际公路上的事故。由于这些人不是“典型的行人”，因此需要进行单独的调查，以更好地了解州际高速公路上的行人碰撞问题并识别高风险场景。本研究探讨了 2014 年至 2018 年期间路易斯安那州州际高速公路上发生的 531 起 KABC（K = 致命、A = 严重、B = 中度、C = 投诉）行人伤害事故。行人伤害严重程度分为两个级别：FS（致命/严重）和IN（中度/投诉）。具有均值异质性的随机参数二元对数 (RPBL-HM) 模型用于解决碰撞数据中未观察到的异质性（即，整个样本群体中碰撞影响因素的影响的变化）。研究发现，一些因素会增加州际高速公路事故中行人 FS 受伤的可能性，包括行人损伤、行人行为、周末、35-44 岁的驾驶员以及春季。研究发现“行人损伤”和“周末”之间存在显着的交互作用，这表明酗酒的行人更有可能在周末的州际公路上卷入 FS 事故。获得的结果可以帮助“无意识的行人”了解州际公路上的碰撞场景，并减少这些意外事件的发生。