基于部分优势比的公路隧道交通事故严重程度分析模型

为了明晰公路隧道交通事故严重程度的影响因素，在分析了16条公路隧道3年内发生的296起交通事故的空间特性、事故形态及其发生原因的基础上，以交通事故严重程度为因变量，将其分为仅财产损失、轻伤、重伤或死亡事故3个等级，从人、车和隧道行车环境3个方面选择了14个交通事故严重程度的潜在影响因素，分别采用有序Logit模型和部分优势比模型建立交通事故严重程度分析模型，并采用Brant检验判断比例优势假设。研究结果表明：与公路隧道交通事故严重程度显著相关的有4个自变量，分别为是否涉及大货车、事故涉及车辆数、事故发生时间和天气因素，其中是否涉及大货车、事故发生时间和天气因素3个自变量满足比例优势假设，而事故涉及车辆数不满足比例优势假设；对于部分优势比模型来说，涉及大货车的事故发生轻伤事故、重伤或死亡事故的概率比不涉及大货车的事故分别增加10.2%和3.4%，多车事故发生轻伤事故、重伤或死亡事故的概率比单车事故分别增加1.9%和5.9%，夜间发生轻伤事故、重伤或死亡事故的概率比白天分别增加5.6%和1.7%，非雨天发生轻伤事故、重伤或死亡事故的概率比雨天分别增加4.5%和1.5%。

Analysis of Traffic Accident Severity on Highway Tunnels Using the Partial Proportion Odds Model

To clarify the influence factors of traffic accident severity on highway tunnels, 14 potential factors were selected according to 296 cases of traffic accidents that occurred in 16 highway tunnels during a 3-year interval. An ordered logit model and a partial proportional odds model were respectively employed to define a traffic accident severity prediction model. The Brant test was used to evaluate the proportional odds assumption. The results show that four independents, including truck, number of vehicles, time, and weather, are significantly related to the accident severity. Truck, time, and weather fulfill the proportional odds assumption, but the number of vehicles violates it. In the partial proportional odds model, the probability of minor and severe/fatal injuries increases by 10.2% and 3.4%, respectively, for truck drivers compared with other drivers; the probability of minor and severe/fatal injuries increases by 1.9% and 5.9%, respectively, for multi-vehicle crashes compared with single-vehicle crashes; the probability of minor and severe/fatal injuries increases by 5.6% and 1.7%, respectively, for night crashes compared with daylight crashes; and the probability of minor and severe/fatal injuries increases by 4.5% and 1.5%, respectively, for clear days compared with rainy days.