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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (5): 997-1008    DOI: 10.3785/j.issn.1008-973X.2023.05.016
    
Expressway driving risk identification method based on velocity risk potential field
Bo WANG1,2,3(),Chi ZHANG1,2,*(),Shi-peng REN4,Chang-he LIU1,Zi-long XIE1
1. School of Highway, Chang’an University, Xi’an 710064, China
2. Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Xi’an 710000, China
3. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
4. Guangdong Communication Planning and Design Institute Group Co. Ltd, Guangzhou 510630, China
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Abstract  

The vehicle speed data on road segments and cross-sections were collected to achieve the spatial localization, classification, and quantification of driving risk at the macro scale. The influence of speed change and road alignment on driving risk was comprehensively considered based on the analysis of the spatial distribution characteristics of the vehicle speed. The safety potential field theory was introduced, the velocity risk potential field model was established, and a macro-identification method for expressway driving risk was proposed. Then the case analysis and validity check were conducted. The results showed that the proportions of lateral, longitudinal and spatial high-risk sections identified in the verification sections were 7.74%, 12.88%, and 24.33% respectively. The accidents in these areas accounted for 31.21%, 31.55% and 43.26% of the total accidents respectively. The intensity of the speed risk potential field and the distribution of traffic accidents have strong regularity, which can represent the composition and severity of the traffic risk in the road section to a certain extent. The velocity variation of expressway traffic flow reflects the road safety states to a certain extent. Taking into account the impact of road alignment index on driving risk, the change in vehicle velocity can be used to identify driving risk precisely.

Key words： expressway      driving risk      risk identification      velocity distribution      risk potential field     
Received: 07 May 2022      Published: 09 May 2023
CLC:  U 412  
Fund:  国家重点研发计划资助项目(2020YFC1512005)；四川省科技计划资助项目(2022YFG0048)；四川省交通运输厅科技项目(2019-ZL-12, 2022-ZL-04); 山西省重点研发计划资助项目(202102020101014)
Corresponding Authors: Chi ZHANG     E-mail: wb1010110wb@chd.edu.cn;zhangchi@chd.edu.cn
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Bo WANG
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Zi-long XIE
Cite this article:

Bo WANG,Chi ZHANG,Shi-peng REN,Chang-he LIU,Zi-long XIE. Expressway driving risk identification method based on velocity risk potential field. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 997-1008.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.05.016     OR     https://www.zjujournals.com/eng/Y2023/V57/I5/997


基于速度风险势场的高速公路行车风险甄别方法

为了在宏观尺度下实现行车风险的空间定位、分类和量化，收集了路段和断面的车辆速度数据. 在分析速度空间分布特征的基础上，综合考虑速度变化和道路线形对行车风险的影响.引入安全势场理论，建立速度风险势场模型，提出高速公路行车风险宏观甄别方法，最后进行实例分析与有效性验证. 结果表明：在验证路段中甄别出的侧向、纵向和空间高风险路段占比分别为7.74%、12.88%、24.33%，区域内发生的事故占总事故的比例分别为31.21%、31.55%、43.26%. 速度风险势场强度和交通事故分布具有较强的规律性，能够在一定程度上表征路段行车风险构成和严重程度. 高速公路交通流速度波动在一定程度上反映道路安全状态，考虑到道路线形指标对行车风险的影响，车辆速度的变化可用于准确甄别行车风险.


关键词： 高速公路,  行车风险,  风险甄别,  速度分布,  风险势场 
序号 断面桩号 车牌号 车辆类型 T/s 行驶方向 v/(km·h?1) 车道号
1 K2084 川D602** 大货车 20200223000246 由南向北 58 2
2 K2084 川U676** 大型客车 20200223000311 由南向北 61 2
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
124216 K2114 晋KH16** 大型客车 20200322235935 由北向南 61 2
Tab.1 Cross section monitoring data of expressway in Southwest China
序号 案发日期 公里桩号 车型 伤亡人数 事故类型
轻伤 重伤 死亡
1 2017-9-24 K2094+719 轿车 0 0 0 单车事故
2 2017-9-30 K2094+473 轿车 0 0 0 追尾
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
438 2020-12-20 K2105+472 轿车 0 0 0 单车事故
439 2020-12-24 K2120+200 轿车 0 0 0 单车事故
Tab.2 Accident data of expressway in Southwest China
Fig.1 Comparison of vehicle speed data from Baidu POI and HD bayonet
断面 车道 $\bar v$/ (km·h?1) ${R_{{\text{lane}}}}$ sd
K2084 内侧 66.50 0.847 6.43
外侧 56.36 6.80
K2088 内侧 66.12 0.853 6.77
外侧 56.43 6.75
K2110 内侧 70.93 0.841 8.35
外侧 59.67 7.68
K2114 内侧 69.83 0.838 5.63
外侧 58.53 5.86
Tab.3 Cross section vehicle speed statistics
Fig.2 Time and space distribution characteristics analysis of vehicle speed on road cross-sections
Fig.3 Distribution of traffic velocity gradients and traffic accidents
Fig.4 Distribution of vertical slope and traffic accidents
Fig.5 Distribution of road horizontal curvature parameters and traffic accidents
Fig.6 Conceptual map of highway velocity risk potential field
车道类别 φx
左边线 内侧车道 车道分界线 中间车道 外侧车道 右边线
左 右
双向四车道 0.900 1.000 0.925 0.925 — 0.850 0.800
双向六车道 0.900 1.000 0.950 0.825 0.900 0.750 0.700
Tab.4 Value table of lateral distribution coefficient of velocity potential energy
Fig.7 Temporal distribution statistics of vehicle speed variation coefficient for cross-section K2084
序号 起点桩号 终点桩号 平面线形 R/m α $ \bar i $/% β
1 K2064+140.000 K2065+174.558 圆曲线 800 3.13 ?2.19 1.19
2 K2065+175.558 K2065+200.000 缓和曲线 ? 1.00 ?2.19 1.19
3 K2065+200.000 K2065+295.558 缓和曲线 ? 1.00 2.12 1.00
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
198 K2094+000.000 K2094+140.000 直线 ? 1.00 ?3.20 2.20
Tab.5 Calculation table of lateral and longitudinal risk impact factors for road section K2064+140 to K2094+140
Fig.8 Three velocity risk potential fields and distribution of vehicle traffic accidents on K2064+140~K2094+140 section
Fig.9 Three velocity risk potential fields and distribution of vehicle traffic accidents on K2094+140~K2130+640 section
类别 阈值 高风险路段占比/% 事故比例/% 总体事故比例/% 甄别效益比
侧向风险事故 纵向风险事故 形态不明事故
Ex 75.5 7.74 21.43 ? 34.9 31.21 4.03
Ey 74.6 12.88 ? 34.21 30.2 31.55 2.45
Ev 106.8 24.33 32.14 43.42 47.65 43.26 1.78
Tab.6 Comparison of high-risk road section identification effects based on three velocity risk potential field intensities
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