Estimation method of operational safety risk for mixed traffic flow on expressway
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摘要:
基于收费数据，考虑车流构成和安全风险成因，确定了车流饱和度、重型货车混入率为影响事故率的2个关键参数，提出了每个参数的算法、阈值及生成流程；模型采用Pearson相关系数法分析了参数之间的独立性，引入变异系数分析了参数与事故率之间的离散度；通过模拟昆虫在不同食物密度下的进食特征，结合多项式拟合方法，提出了高速公路混行车流运行安全风险评估模型；采用Taylor级数展开法和Levenberg-Marquardt算法分别完成模型求解和迭代过程；基于四川省域684个实例路段的数据确定了模型的待标定参数，验证了模型的可行性。研究结果表明：安全风险评估模型能够有效反映各路段事故率的特征；模型迭代523次后离散统计量达到1%的误差要求，耗时1.42 s；车流饱和度、重型货车混入率对事故率的致因分别符合Peal-Reed模型、三次多项式的特征；当车流饱和度为33%且重型货车混入率为71%时，混行车流运行安全风险达到最大值；以路段事故率在[0.01，1.03]内按10.20%递增且通过合并分组后发现，路段事故率划分为5个等级且搭配5种配色可清晰地表达路网安全风险状况；以85%、15%分位数划分重型货车混入率、车流饱和度可实现对参数取值范围的全覆盖。安全风险评估方法对动态监测高速公路交通安全、指导应急资源选址配额、配置警力与路政、疏散车流等研究有重要价值。
Abstract:Based on toll collection data, this study identified two key parameters affecting accident rates by considering traffic flow composition and the causes of safety risks: traffic flow saturation and the mixing rate of heavy-duty trucks. The algorithm, threshold values, and generation process for each parameter were proposed. The Pearson correlation coefficient method was employed to analyze the independence between the two parameters, and the coefficient of variation was introduced to examine their dispersion relative to the accident rate. By simulating insect feeding characteristics under varying food densities and incorporating polynomial fitting, a safety risk assessment model for mixed traffic flow on expressways was developed. The model was solved using the Taylor series expansion method and iteratively optimized with the Levenberg-Marquardt algorithm. The model's parameters were calibrated using data from 684 expressway sections in Sichuan Province, and its feasibility was verified. The research results indicate that the proposed safety risk assessment model effectively captures the accident rate characteristics of different sections. After 523 iterations, the model achieves a discrete statistical error of 1%, requiring only 1.42 s. The influence of traffic flow saturation and the mixing rate of heavy-duty trucks on the accident rate aligns with the Peal-Reed model and a cubic polynomial model, respectively. The safety risk of mixed traffic flow peaks when traffic flow saturation reaches 33% and the mixing rate of heavy-duty trucks reaches 71%. By incrementally increasing the accident rate by 10.20% within [0.01, 1.03] and subsequently merging groups, it is found that dividing the accident rate into five levels, each represented by a distinct color scheme, can clearly illustrate the safety risk status of the expressway network. Using the 85th and 15th percentiles to define the mixing rate of heavy-duty trucks and traffic flow saturation, respectively, ensures comprehensive coverage of the parameter ranges. The proposed safety risk assessment method holds significant value for dynamically monitoring expressway traffic safety, guiding the allocation of emergency resources, optimizing the deployment of police and road administration personnel, and facilitating evacuation strategies.
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图 2 行车安全风险参数算法实现效果
Figure 2. Algorithm implementation effect of driving safety risk parameters
图 3 2019年1~12月Mj、Sj和Zj的d的变化情况
Figure 3. d values of Mj, Sj and Zj from Jan. to Dec. in 2019
图 4 Sj与Zj的理论关系与拟合曲线
Figure 4. Theoretical relationship and fitting curves between Sj and Zj
图 8 实际数据拟合的曲面和参数标定后的模型曲面
Figure 8. Fitting surface by actual data and model surface after parameters calibrated
表 1 行车安全风险参数计算方法
Table 1. Calculation method for driving safety risk parameters
关键参数 计算方法 TCD约束条件 符号定义 车流饱和度Sj $ \begin{gathered} S_j=\sum\limits_{i=1}^{10} k_i Q_{i j} /\left(m_{j 1} C_{j \mathrm{~d} 1}+m_{j 2} C_{j \mathrm{~d} 2}\right) \\ C_{j \mathrm{~d} 1}=0.99 C_1 f_{\mathrm{c} j} \\ C_{j \mathrm{~d} 2}=0.99 C_2 f_{\mathrm{c} j} \\ f_{\mathrm{c} j}=1 /\left[1+\sum\limits_{i=1}^{10} P_{i j}\left(k_i-1\right)\right] \end{gathered}$ C1=2 200 pcu·h-1·车道-1
C2=2 100 pcu·h-1·车道-1
mj1=1, 2
mj2=1, 2, 3mj1为j路段120 km·h-1设计时速的车道数；
mj2为j路段100 km·h-1设计时速的车道数；
Cjd1为j路段120 km·h-1设计时速的通行能力；
Cjd2为j路段100 km·h-1设计时速的通行能力；
fcj为j路段的交通组成系数；
Pij为i类车j路段的交通量比例；
Qij为i类车j路段的自然交通量。重型货车混入率Mj $ M_j=\sum\limits_{i=7}^{10} k_i Q_{i j} /\left(\sum\limits_{i=1}^{10} k_i Q_{i j}\right)$ Mj∈(0, 100%]
t∈(0, 1 440] min
Vjk∈[60, 140] km·h-1
Vjh∈[40, 100] km·h-1Vjk为客车j路段的车速；
Vjh为货车j路段的车速；
t为通行时间。
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表 2 安全评估风险标准的等级与配色方案
Table 2. Level and color scheme for estimation standard of driving safety risk
Pj/% Zj/[次·(d·100 km)-1] 颜色标记 路段示例 Pj≤25.00 0.01≤Zj≤0.26 绿色G 
25.00＜Pj≤54.69 0.26＜Zj≤0.57 黄色Y 54.69＜Pj≤80.39 0.57＜Zj≤0.83 橙色O 80.39＜Pj≤90.78 0.83＜Zj≤0.94 鲜红R 90.78＜Pj≤100.00 0.94＜Zj≤1.03 深红C
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表 3 Sj与Mj分区间的Zj安全风险分布
Table 3. Distribution for safety risk of Zj by dividing Sj and Mj
Sj Mj [0, 15%] (15%, 40%] (40%, 60%] (60%, 85%] (85%, 100%] [0, 15%] [0.201, 0.607] G→O (0.405, 0.687] Y→O (0.374, 0.676] Y→O (0.225, 0.579] G→Y→O (0.188, 0.432] G→Y (15%, 40%] (0.195, 0.602] G→O (0.399, 0.682] Y→O (0.368, 0.671] Y→O (0.219, 0.574] G→Y→O (0.182, 0.427] G→Y (40%, 60%] (0.423, 0.894] Y→O (0.627, 0.973] O→C (0.596, 0.963] O→C (0.447, 0.866] Y→O→R (0.410, 0.718] Y→O (60%, 85%] (0.606, 0.953] O→R→C (0.810, 1.030] O→R→C (0.780, 1.023] O→R→C (0.630, 0.925] O→R (0.594, 0.778] O (85%, 100%] (0.028, 0.778] G→Y→O (0.232, 0.857] G→Y→O→R (0.202, 0.847] G→Y→O→R (0.053, 0.750] G→Y→O (0.016, 0.602] G→Y→O
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[1] 公安部交通管理局. 中华人民共和国道路交通事故统计年报(2019年度)[R]. 北京: 公安部交通管理科学研究所, 2020.Traffic Administration Bureau of the Ministry of Public Security of the People's Republic of China. Statistical annual report of road traffic accidents of the People's Republic of China (2019)[R]. Beijing: Traffic Management Research Institute of the Ministry of Public Security, 2020. [2] JIANG C M, YIN S C, YAO Z H, et al. Safety evaluation of mixed traffic flow with truck platoons equipped with (cooperative) adaptive cruise control, stochastic human-driven cars and trucks on port freeways[J]. Physica A: Statistical Mechanics and Its Applications, 2024, 643: 129802. doi: 10.1016/j.physa.2024.129802 [3] 闫晟煜, 王钊龙, 武瑾, 等. 省域高速公路网车辆碳排放量测算方法[J]. 中国环境科学, 2024, 44(12): 7095-7104.YAN Sheng-yu, WANG Zhao-long, WU Jin, et al. Estimation model of vehicle carbon emission for provincial expressway networks[J]. China Environmental Science, 2024, 44(12): 7095-7104. [4] 孙剑, 孙杰. 城市快速路实时交通流运行安全主动风险评估[J]. 同济大学学报(自然科学版), 2014, 42(6): 873-879.SUN Jian, SUN Jie. Proactive assessment of real-time traffic flow accident risk on urban expressway[J]. Journal of Tongji University (Natural Science), 2014, 42(6): 873-879. [5] 田万利, 吴忠广, 李娟, 等. 基于动态风险饱和度的高速公路交通安全分析[J]. 交通信息与安全, 2021, 39(5): 12-18, 42.TIAN Wan-li, WU Zhong-guang, LI Juan, et al. An analysis of highway-traffic safety based on dynamic risk saturation[J]. Journal of Transport Information and Safety, 2021, 39(5): 12-18, 42. [6] 游锦明, 方守恩, 张兰芳, 等. 高速公路实时事故风险研判模型及可移植性[J]. 同济大学学报(自然科学版), 2019, 47(3): 347-352.YOU Jin-ming, FANG Shou-en, ZHANG Lan-fang, et al. Real-time crash risk prediction models and transferability analysis on freeways[J]. Journal of Tongji University (Natural Science), 2019, 47(3): 347-352. [7] WAGNER P, HOFFMANN R, LEICH A. Observations on the relationship between crash frequency and traffic flow[J]. Safety, 2021, 7(1): 3. doi: 10.3390/safety7010003 [8] SHEW C, PANDE A, NUWORSOO C. Transferability and robustness of real-time freeway crash risk assessment[J]. Journal of Safety Research, 2013, 46: 83-90. doi: 10.1016/j.jsr.2013.04.005 [9] BEHBAHANI H, AMIRI A M, IMANINASAB R, et al. Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques[J]. Journal of Forecasting, 2018, 37(7): 767-780. doi: 10.1002/for.2542 [10] PARSA A B, MOVAHEDI A, TAGHIPOUR H, et al. Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis[J]. Accident Analysis and Prevention, 2020, 136: 105405. doi: 10.1016/j.aap.2019.105405 [11] 付存勇, 王俊骅. 基于监控数据的高速公路实时事故风险模型[J]. 交通信息与安全, 2017, 35(5): 11-17, 36.FU Cun-yong, WANG Jun-hua. A real-time accident risk model on freeways based on monitoring data[J]. Journal of Transport Information and Safety, 2017, 35(5): 11-17, 36. [12] LIN L, WANG Q, SADEK A W. A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction[J]. Transportation Research Part C: Emerging Technologies, 2015, 55(2): 444-459. [13] 徐铖铖, 刘攀, 王炜, 等. 基于判别分析的高速公路交通安全实时评价指标[J]. 东南大学学报(自然科学版), 2012, 42(3): 555-559.XU Cheng-cheng, LIU Pan, WANG Wei, et al. Discriminant analysis based method to develop real-time crash indicator for evaluating freeway safety[J]. Journal of Southeast University (Natural Science Edition), 2012, 42(3): 555-559. [14] 吉小进, 方守恩, 黄进. 高速公路基本路段V/C比与事故率的关系[J]. 公路交通科技, 2003, 20(1): 122-124.JI Xiao-jin, FANG Shou-en, HUANG Jin. Relationship between V/C and accident rates at basic expressway section[J]. Journal of Highway and Transportation Research and Development, 2003, 20(1): 122-124. [15] 孟祥海, 李昕, 郑来. 基于事故数据与安全服务水平的高速公路路段安全性评价方法[J]. 交通运输工程学报, 2014, 14(6): 83-91. doi: 10.3969/j.issn.1671-1637.2014.06.011MENG Xiang-hai, LI Xin, ZHENG Lai. Safety evaluation method of freeway segment based on accident data and safety service level[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 83-91. doi: 10.3969/j.issn.1671-1637.2014.06.011 [16] YANG D, QIU X P, YU D, et al. A cellular automata model for car-truck heterogeneous traffic flow considering the car-truck following combination effect[J]. Physica A: Statistical Mechanics and Its Applications, 2015, 424: 62-72. doi: 10.1016/j.physa.2014.12.020 [17] KONG D W, SUN L S, LI J, et al. Modeling cars and trucks in the heterogeneous traffic based on car-truck combination effect using cellular automata[J]. Physica A: Statistical Mechanics and Its Applications, 2021, 562: 125329. doi: 10.1016/j.physa.2020.125329 [18] MEHRARA MOLAN A, REZAPOUR M, KSAIBATI K. Investigating the relationship between crash severity, traffic barrier type, and vehicle type in crashes involving traffic barrier[J]. Journal of Traffic and Transportation Engineering (English Edition), 2020, 7(1): 125-136. doi: 10.1016/j.jtte.2019.03.004 [19] 张兰芳, 赵焜. 考虑货车因素的高速公路短期交通流风险预测[J]. 同济大学学报(自然科学版), 2018, 46(2): 208-214.ZHANG Lan-fang, ZHAO Kun. Short-term traffic flow risk prediction on freeways based on truck factors[J]. Journal of Tongji University (Natural Science), 2018, 46(2): 208-214. [20] SINHA A, CHAND S, WIJAYARATNA K P, et al. Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles[J]. Accident Analysis and Prevention, 2020, 142: 105567. doi: 10.1016/j.aap.2020.105567 [21] SORIGUERA F, ROBUSTÉ F. Estimation of traffic stream space mean speed from time aggregations of double loop detector data[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(1): 115-129. doi: 10.1016/j.trc.2010.04.004 [22] 谢恩怡. 混合交通环境下车速离散时空演化机理研究[D]. 南京: 东南大学, 2015.XIE En-yi. Temporal-spatial evolution of speed dispersion under mixed traffic[D]. Nanjing: Southeast University, 2015. [23] 郭延永, 刘佩, 袁泉, 等. 网联自动驾驶车辆道路交通安全研究综述[J]. 交通运输工程学报, 2023, 23(5): 19-38. doi: 10.19818/j.cnki.1671-1637.2023.05.002GUO Yan-yong, LIU Pei, YUAN Quan, et al. Review on research of road traffic safety of connected and automated vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 19-38. doi: 10.19818/j.cnki.1671-1637.2023.05.002 [24] SOHAIL A, CHEEMA M A, ALI M E, et al. Data-driven approaches for road safety: A comprehensive systematic literature review[J]. Safety Science, 2023, 158: 105949. doi: 10.1016/j.ssci.2022.105949 [25] 闫晟煜, 赵佳琪, 尤文博, 等. 高速公路货车差异化通行费折扣的双层规划模型[J]. 清华大学学报(自然科学版), 2025, 65(7): 1347-1358.YAN Sheng-yu, ZHAO Jia-qi, YOU Wen-bo, et al. Bi-level programming model for differentiated toll discounts for expressway trucks[J]. Journal of Tsinghua University (Science and Technology), 2025, 65(7): 1347-1358. [26] JT/T 489—2019, 收费公路车辆通行费车型分类[S].JT/T 489—2019, Vehicle classification of the toll for highway[S]. [27] SHAHZAD U, AHMAD I, GARCÍA-LUENGO A V, et al. Estimation of coefficient of variation using calibrated estimators in double stratified random sampling[J]. Mathematics, 2023, 11(1): 1-17. [28] 钟连德, 孙小端. 高速公路安全与交通流特征参数关系[J]. 北京工业大学学报, 2013, 39(2): 251-256.ZHONG Lian-de, SUN Xiao-duan. Relationship between traffic flow and safety on freeway[J]. Journal of Beijing University of Technology, 2013, 39(2): 251-256. [29] 梁国华, 程国柱, 王春艳, 等. 高速公路大型车混入率与交通流稳定性关系[J]. 长安大学学报(自然科学版), 2014, 34(4): 120-126.LIANG Guo-hua, CHENG Guo-zhu, WANG Chun-yan, et al. Relationship between mixing rate of freeway large vehicles and traffic flow stability[J]. Journal of Chang'an University (Natural Science Edition), 2014, 34(4): 120-126. [30] 金开正, 何利良, 刘茂泉. Peal-Reed模型在捕食性昆虫功能反应研究中的应用[J]. 安徽农业科学, 2005, 33(3): 393-395.JIN Kai-zheng, HE Li-liang, LIU Mao-quan. Application of the peal-reed model in the study on the response of predator to prey[J]. Journal of Anhui Agricultural Sciences, 2005, 33(3): 393-395. [31] WEI Y X, HUANG P Z. Convergence of several iterations for double-diffusive natural convection model[J]. Mathematical Methods in the Applied Sciences, 2023, 46(16): 16899-16936. doi: 10.1002/mma.9480 [32] HAUER E. Engineering judgment and road safety[J]. Accident Analysis & Prevention, 2019, 129: 180-189. [33] ASWIN I C, SANKARAN P G, SUNOJ S M. Reliability aspects of quantile-based residual coefficient of variation[J]. International Journal of Reliability, Quality and Safety Engineering, 2023, 30(2): 2250021. doi: 10.1142/S0218539322500218 [34] 裴华鑫, 杨敬轩, 胡坚明, 等. 大规模路网中分布式车辆群体协同决策方法[J]. 交通运输工程学报, 2022, 22(3): 174-183. doi: 10.19818/j.cnki.1671-1637.2022.03.014PEI Hua-xin, YANG Jing-xuan, HU Jian-ming, et al. Distributed cooperative decision-making method for vehicle swarms in large-scale road networks[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 174-183. doi: 10.19818/j.cnki.1671-1637.2022.03.014 [35] HE Yi-xu, LIN Hong-yi, YANG Lan, et al. Generative models for the evolution of transportation systems[J]. Journal of Traffic and Transportation Engineering (English Edition), 2025, 12(6): 1784-1803. doi: 10.1016/j.jtte.2024.03.010 -
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