Spatiotemporal characteristics and severity modelling of electric vehicle-pedestrian collision accidents
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摘要: 为了探索电动汽车与行人碰撞事故的时空分布模式，在传统核密度估计法中引入聚类强度和事故严重度指标，建立了交通事故黑点时空核密度估计方法；考虑到事故变量中可能存在的随机变量和异质性，基于均值异质性的随机参数Logit模型，建立了电动汽车与行人碰撞事故严重程度模型，识别影响电动汽车与行人碰撞事故严重程度的关键因素，并通过计算显著变量的边际效用定量确定了关键因素的影响程度。研究结果表明：电动汽车和燃油汽车与行人碰撞事故均主要分布在城市中心区域，电动汽车与行人碰撞事故分布相对更为集中，且工作日和白天时电动汽车的事故数更多，严重程度更高；电动汽车驾驶人年龄在[40，60)岁和事故发生时间段在18:00至次日6:00为随机变量，驾驶人年龄的效用值服从标准差为-0.582、方差为0.9262的正态分布，且在道路功能等级(主干路)和车辆类型(货车)变量中具有均值异质性，事故发生时间段的效用值服从标准差为-0.313、方差为0.5182的正态分布，且在天气(雾霾)变量中具有均值异质性，此外，男性驾驶人、中青年驾驶人和工作日出行等因素会导致电动汽车与行人碰撞事故严重程度的不同增加；考虑均值异质性的随机参数Logit模型的赤池信息准则比随机参数Logit模型减少了26，对数似然值增加了94.272，可见，引入均值异质性后，构建的基于随机参数Logit模型的电动汽车与行人碰撞事故严重程度模型拟合优度有所提升，且能更科学全面地解释电动汽车与行人碰撞事故的致因原理。Abstract: To investigate the spatiotemporal distribution pattern of electric vehicle-pedestrian collision accidents, the indicators of clustering strength and accident severity were incorporated into the traditional kernel density estimation method. A spatiotemporal kernel density estimation approach for traffic accident-prone locations was built. The random parameter Logit model based on mean heterogeneity was developed to model the severity of electric vehicle-pedestrian collision accident by considering the potential random variables and heterogeneity in accident variables. Key factors affecting the severity of electric vehicle-pedestrian collision accidents were identified, and the marginal utility of each significant variable was calculated to quantify its impact on the severity of electric vehicle-pedestrian collision accidents. Research results show that the electric/fuel vehicle-pedestrian collision accidents are predominantly located in urban center areas, whereas the distribution of electric vehicle-pedestrian collision accidents is relatively more concentrated. Moreover, the number and severity of electric vehicle-pedestrian collision accidents are higher on weekdays and daytime. Electric vehicle driver age ranging from 40 to 60 years old and accident occurring time between 18:00 and 6:00 the next day are identified as random variables. The utility value of driver age follows a normal distribution with standard deviation of -0.582 and variance of 0.9262, and has mean heterogeneity in variables of road function level (trunk road) and vehicle type (truck). The utility value of accident occurring time follows a normal distribution with standard deviation of -0.313 and variance of 0.5182, and there is mean heterogeneity in variable of weather (haze). Additionally, the factors such as male drivers, middle-aged and young drivers, and weekday travel are found to contribute to the varying degrees of severity in electric vehicle-pedestrain collision accidents. The Akaike information criterion of the random parameter Logit model considering mean heterogeneity decreases by 26 compared with the random parameter Logit model, and the logarithmic likelihood value increases by 94.272. The introduction of mean heterogeneity improves the fit goodness of severity model for electric vehicle-pedestrian collision accidents based on the random parameter Logit model, providing a more scientific and comprehensive explanation of causal principles behind electric vehicle-pedestrian collision accidents.
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图 1 行人碰撞事故中影响因素分布特征对比
Figure 1. Comparison of distribution characteristics of influencing factors in pedestrian collision accidents
图 2 汽车与行人碰撞的事故核密度
Figure 2. Accident kerhel densities of vehicle-pedestrian collisions
图 3 不同等级道路上汽车与行人碰撞事故的分布
Figure 3. Distribution of vehicle-pedestrian collision accidents on different grade roads
图 4 汽车与行人碰撞事故的时间分布
Figure 4. Time distributions of vehicle-pedestrian collision accidents
图 5 汽车与行人碰撞事故的时空分布
Figure 5. Spatiotemporal distributions of vehicle-pedestrian collision accidents
表 1 样本描述性统计
Table 1. Sample descriptive statistics
特征分类 参数 电动汽车 燃油汽车 非死亡事故 死亡事故 非死亡事故 死亡事故 起数 百分比/% 起数 百分比/% 起数 百分比/% 起数 百分比/% 驾驶人特征 性别 男 43 97.73 1 2.27 28 59.57 19 40.43 女 14 100.00 4 66.67 2 33.33 年龄/岁 [20，40) 39 97.50 1 2.50 23 65.71 12 34.29 [40，60) 16 100.00 9 52.94 8 47.06 [60，80) 2 100.00 1 100.00 车辆特征 类别 公交 2 100.00 2 66.67 1 33.33 小汽车 50 100.00 25 64.10 14 35.90 货车 5 83.33 1 16.67 5 45.45 6 54.55 道路特征 道路等级分类 快速路 5 100.00 主干路 6 85.71 1 14.29 5 45.45 6 54.55 次干路 6 100.00 10 83.33 2 16.67 支路 45 100.00 17 68.00 8 32.00 环境特征 天气 晴 52 98.11 1 1.89 27 64.29 15 35.71 雾霾 3 100.00 3 42.86 4 57.14 雨、雪 1 50.00 1 50.00 2 50.00 2 50.00 周内分布 工作日 43 97.73 1 2.27 25 59.52 17 40.48 周末 14 100.00 7 63.64 4 36.36 时间段分布 6:00~18:00 42 97.68 1 2.32 12 54.55 10 45.45 18:00~6:00 15 100.00 20 64.52 11 35.48 
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表 2 汽车与行人碰撞事故的分布数值
Table 2. Distribution values of vehicle-pedestrian collision accidents
电动汽车 燃油汽车 时间段 位置代码 聚类强度 时间段 位置代码 聚类强度 14:00~15:00 220 0.744 20:00~21:00 136 0.651 19:00~20:00 314 0.676 19:00~20:00 251 0.526 20:00~21:00 220 0.666 18:00~19:00 152 0.511 16:00~17:00 20 0.617 14:00~15:00 37 0.507 18:00~19:00 659 0.613 16:00~17:00 659 0.493 22:00~23:00 96 0.577 7:00~8:00 72 0.482 20:00~21:00 152 0.536 22:00~23:00 125 0.429 9:00~10:00 95 0.521 20:00~21:00 221 0.411
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表 3 变量的VIF
Table 3. VIF of variables
变量 VIF 变量 VIF 女 1.180 主干路 1.103 [40，60)岁 1.249 次干路 1.143 [60，80)岁 1.194 雾霾 1.260 公交 1.163 雨、雪 1.297 货车 1.131 周末 1.176 快速路 1.380 18:00~6:00 1.309
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表 4 模型估算结果
Table 4. Estimation results of models
变量 随机参数Logit模型 均值异质性随机参数Logit模型 平均边际效应 电动汽车 燃油汽车 电动汽车 燃油汽车 电动汽车 燃油汽车 系数估计 Z值 系数估计 Z值 系数估计 Z值 系数估计 Z值 非死亡事故 死亡事故 非死亡事故 死亡事故 常数 1.577 4.240 0.741 2.010 1.283 3.246 0.752 2.790 女 -1.822 1.420 -0.113 4.303 -1.671 2.752 -0.075 3.714 0.002 -0.002 0.004 -0.004 [40，60)岁 0.649 2.258 2.940 2.911 -0.582 1.255 0.560 1.824 0.003 -0.003 -0.023 0.023 [40，60)岁的标准差 0.254 1.618 0.182 1.785 0.926 2.862 1.425 2.663 公交 0.501 1.738 -0.745 1.793 0.575 3.628 -0.824 3.054 -0.001 0.001 0.002 -0.002 货车 -2.416 2.672 0.391 2.036 -2.367 2.285 0.541 2.793 0.004 -0.004 -0.017 0.017 主干路 -9.284 2.074 1.752 2.825 -9.361 2.349 1.893 2.512 0.006 -0.006 -0.053 0.053 次干路 0.562 3.547 2.437 3.281 0.579 3.277 2.547 3.727 -0.007 0.007 0.076 -0.076 雾霾 -0.283 2.249 0.352 2.461 -0.294 3.162 0.352 2.625 0.001 -0.001 -0.005 0.005 周末 -0.517 2.813 -0.627 1.943 -0.546 2.724 -0.641 1.916 0.003 -0.003 0.006 -0.006 18:00~6:00 -0.677 3.594 1.293 3.416 -0.313 3.012 0.960 3.175 0.006 -0.006 -0.032 0.032 18:00~6:00的标准差 0.467 1.537 0.174 2.723 0.518 1.572 1.186 2.682 [40，60)岁，主干路 -0.170 -3.571 0.136 2.158 [40，60)岁，货车 -0.316 -1.284 0.272 1.682 18:00~6:00，雾霾 -0.146 -2.035 0.229 3.186 AIC 4 311 4 241 4 285 4 109 模型收敛时的对数似然值 -2 300.691 -2 300.451 -2 206.419 -2 041.357 伪R2 0.365 0.326 0.463 0.452
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