智能网联环境下高速道路交通运行风险分析综述

张梦雅; 杨晓光; 马成元; 杨洁

doi:10.3963/j.jssn.1674-4861.2025.02.015

智能网联环境下高速道路交通运行风险分析综述

doi: 10.3963/j.jssn.1674-4861.2025.02.015

1.
同济大学道路与交通工程教育部重点实验室上海 201804
2.
威斯康辛大学麦迪逊分校土木与环境工程系美国麦迪逊 53706

基金项目:

国家自然科学基金项目 52472350

国家自然科学基金项目 52172307

广西科技重大专项子课题项目 2023AA14006-3

详细信息

作者简介:
张梦雅（1990—），博士研究生. 研究方向：交通安全. E-mail：2010787@tongji.edu.cn

通讯作者:
杨晓光（1959—），博士，教授. 研究方向：交通信息工程及控制、交通安全等. E-mail：yangxg@tongji.edu.cn

中图分类号: U491.5+4
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出版历程
- 收稿日期: 2024-05-02
- 网络出版日期: 2025-09-29

A Review of Traffic Operation Risk Analysis on Highways in a Connected and Automated Environment

1.
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
2.
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison 53706, WI, USA

摘要

摘要: 高速道路交通运行风险分析对交通安全性、可靠性及实现主动管理与控制具有重要的理论意义和实用价值。网联环境为交通运行风险分析提供了全新的方法、理论与技术支持。面向高速公路和城市快速路等连续交通流，系统综述了非网联环境与网联环境下交通流运行风险分析的理论基础与关键技术，并展望了未来的研究发展方向。研究表明，非网联环境下交通运行风险分析多依赖有限非结构化数据，主要聚焦风险因素识别、事故机理解析与风险预测等问题，存在场景建模精度有限、对交通动态演变响应能力不足及实时风险感知与预测能力有待提升的问题。网联环境下，随着多源实时数据的融合应用，交通风险分析逐步向事前预测与局部交互挖掘转变，数据支撑与建模精细度有所提高。然而，在混合交通流条件下，不同类型车辆之间的交互演变规律尚未形成系统建模框架。异质驾驶决策、通信延迟与感知偏差等因素的综合作用机制尚待细化，复杂交通环境中动态要素协同演化过程的建模与解释能力亦需加强。未来研究可深化人-车-路协同演化机制建模，完善混合交通流中风险积累与传播规律的表征，强化多源感知数据融合与动态特征提取，提升交通运行风险评估的实时性与鲁棒性，推进智能推演算法与系统级验证方法的开发。通过理论深化、数据赋能与技术协同，逐步构建可解释、可预测、可干预的智能交通运行风险防控体系。
- 交通安全 /
- 交通运行风险 /
- 综述 /
- 智能网联环境 /
- 高速道路
Abstract: The analysis of traffic operation risks on expressways holds significant theoretical and practical value for enhancing traffic safety, reliability, and realizing proactive management. The connected environment introduces new methods, theories, and technologies for traffic risk analysis. Focusing on continuous traffic flows on highways and urban expressways, this study systematically reviews the theoretical foundations and key techniques of traffic operation risk analysis under both non-connected and connected environments, and discusses future research directions. In non-connected environments, risk analysis primarily relies on limited unstructured data, emphasizing risk factor identification, accident mechanism analysis, and risk prediction. Challenges remain in achieving high-precision scene modeling, responsive dynamic evolution analysis, and real-time risk perception and forecasting. In connected environments, the integration of multi-source real-time data enables a shift toward proactive risk prediction and localized interaction analysis, with improvements in data support and modeling accuracy. However, in mixed traffic conditions, systematic models for the interaction dynamics of heterogeneous vehicle types have not yet been fully established. The combined effects of diverse driving behaviors, communication delays, and perception deviations require further investigation, and the modeling and interpretation of dynamic factor evolution in complex environments remain incomplete. Future research should advance the modeling of human-vehicle-road collaborative evolution, refine the characterization of risk accumulation and propagation in mixed traffic, enhance multi-source data fusion and dynamic feature extraction, and strengthen the real-time performance and robustness of risk evaluation. Through theoretical innovation, data-driven methods, and integrated technological development, it is expected that an interpretable, predictable, and actionable intelligent traffic risk prevention and control system will be progressively established.
- traffic safety /
- traffic operational risks /
- review /
- connected and automated driving environment /
- highways

HTML全文

图 1 交通运行风险的基本内涵

Figure 1. Basic connotations of traffic operation risks

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图 2 高速道路交通运行风险领域文献出版数量和引用量变化情况（1975—2023年）

Figure 2. Publications and citations in the field of traffic driving risk over time(1975—2023)

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图 3 文献关键词共现分析

Figure 3. Co-occurrence analysis of key words in literature

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图 4 智能网联车辆自动化水平及技术阶段

Figure 4. Level of automation and technological stages of CAVs

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图 5 智能网联环境下的交通流重构

Figure 5. Traffic flow reconstruction in connected environment

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图 6 非网联环境下运行风险分析基本问题及方法

Figure 6. Basic issues and methods for operational risk analysis in a non-connected environment

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图 7 交通运行风险分析理论模型脉络图

Figure 7. Theoretical model framework of traffic operation risk analysis

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图 8 网联环境下交通运行风险分析研究框架及愿景

Figure 8. Research framework and vision for traffic operation risk analysis in connected environments

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图 9 替代安全措施的分类及适用场景

Figure 9. Classification and applicable scenarios of surrogate safety measures

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表 1 CAV和HDV驾驶行为比较

Table 1. Comparison of driving behaviors between CAV and HDV

分类	CAV	HDV
环境感知	多传感器全面感知（雷达、激光雷达、摄像头）实时生成高精度环境模型，适应环境变化	依靠人类感官（视觉、听觉等）感知能力有限，易受环境条件（如恶劣天气）影响
反应决策	毫秒级快速反应基于算法的理性决策行为高度一致	反应速度较慢且不稳定受个人经验和情绪影响，决策不可预测
规则遵守	严格遵守交通法规持续监控并调整行为	有时违反交通规则（如闯红灯、超速等）
行为预测	驾驶行为高度可预测遵循预定驾驶模式	行为存在较大变数驾驶模式不可预测
协同沟通	实时车联网通信高效协同驾驶（车队行驶）	依赖视觉和听觉信号沟通协同程度有限，效率较低
能源效率	优化加速、刹车和路径选择提高能源使用效率	驾驶行为受个人习惯影响能源使用效率不稳定，可能浪费

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表 2 智能网联车辆技术模块及其带来的优势和运行风险

Table 2. Modules of CAV technology and their advantages and risks

模块	功能	优势	风险
感知模块	通过传感器收集环境信息，识别道路、车辆、行人、交通标志等元素	“超视距”感知实时获知周围车辆行驶状态	感知误差：传感器盲区、分辨率低、受环境影响大、信息交互延时长交互预测失败：自动驾驶预测人类行为错误复杂的道路环境：道路环境影响自动驾驶的感知与决策
规划模块	基于感知模块提供的信息进行路径规划和决策制定	确定且可预测的驾驶行为形成小车间距/ 时距的队列行驶，提高车道容量3倍以上^[6] 协同完成转弯、倾斜、避障、紧急制动等驾驶行为	决策与规划复杂性：算法与人类直觉差异、环境预测不准确复杂驾驶行为：自动驾驶与人类驾驶行为差异可能增加速度波动
控制模块	依据规划结果执行具体动作，如调整车速、转向等	充当控制器，消除拥堵区域的走走停停行为协作自适应巡航控制的平滑效应	控制系统的执行错误：硬件故障、软件漏洞反应时间差异：自动驾驶与人类驾驶反应时间不一致，影响交通流平滑性通信依赖性：V2V或V2I通信不稳定可能导致局部交通流中断

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表 3 非网联环境下数据的类别

Table 3. Categories of data in non- connected environments

类型	数据内容	应用	局限
历史事故数据	事故数事故类型事故频率严重程度	交通事故发生情况的详细统计分析	受信息收集主观性影响，导致数据质量差；收集周期长，时间不稳定，无法及时获取
		交通安全评估和预测	未观察到的异质性、时空相关性弱，数据分析受限
		提出预防措施，改善交通安全	伦理问题，需要发生多次事故才能总结出对应的预防措施^[11]
交通流数据	速度流量地理位置	交通流量和特征的快速获取和分析	数据精度不高（5~ 30 min间隔的集计数据），可能存在滞后性
		评估交通拥堵情况和交通状况的变化	数据处理和分析复杂，可能受到设备故障和覆盖范围限制的影 _响^[12]
		交通流量预测和交通管理措施，提供实时的交通情况和改进措施

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表 4 非网联环境与网联环境交通流数据对比

Table 4. A comparison of traffic flow data between non-connected and connected environments

类别	特点	采集方式	数据特点	优点	缺点
非网联环境	断面集计静态	线圈、微波、地磁、超声波、交通调查	固定断面的流量、速度和占有率，部分要素可计算	不受环境影响	精度粗、布点有限、建设运维成本高、数据易丢失
网联环境	个体连续动态	ETC、高精度地图、车辆轨迹、视频监控、无人机、互联网数据	广域连续的个体车辆轨迹、路径，全要素可计算	粒度细、范围广、成本低、可靠性高、更精准、不易丢失	更新频率受影响

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表 5 网联环境下高速道路交通运行风险分析研究理论及关键技术

Table 5. Research theories and key technologies for traffic operation risk analysis on highways in a connected environment

理论维度		内涵	研究重点	关键技术
维度一：感	风险感知	全息：融合卡口、视频、微波等多源感知信息，实现高速道路精准状态感知	互联网车辆轨迹电警卡口高清视频数据库架构	多源数据融合大数据分析研判视频图像处理可计算路网
维度二：辩	风险辨识	自主：基于交通拥堵、事故前兆（冲突），结合高精度交通流数据，实现交通拥堵、事故风险动态辨识	路网路径状态人-车-路关联关系交通事件检测	路径重构计算以往模型+智能算法异常数据识别视频图像处理风险实时预测
维度三：评	风险评估	精准：统计分析模型和机器学习模型进行风险概率和等级评估	评估指标体系问题诊断方法智能优化算法	传统理论模型+ 多源数据融合在线交通仿真数据驱动控制
维度四：管	风险防控	协同：针对复杂场景，实现动态限速、流量控制的动态车道管理等主动防控	全场景管控能力多系统协同能力全业务打通能力	PaaS平台即服务模块化开发 AI+预案生成在线交通仿真

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表 6 基于仿真模拟的风险评估研究摘要

Table 6. Summary of studies based on simulation methods

研究类别	文献	仿真工具	场景	评估内容	参数
交通冲突的风险评估	[40]	VISSIM、API	基本路段	纵向和横向冲突	渗透率25%~100%
	[41]	VISSIM、SSAM	环岛	冲突数量、延误	渗透率50%~100%
	[42]	MATLAB	直线坡道	追尾冲突、缩队行驶	速度差、车型、乘车距离
	[43]	SUMO、Unity3D	基本路段	自由换道行为、车辆交互	网联环境设置、驾驶人特性、加速度、换道间距
驾驶行为的风险评估	[44]	PreScan	多种场景	横、纵向驾驶模式	L0-L5级CAV、渗透率、渗透率、交通密度
	[45]	CARLA	基本路段	安全行为规划	渗透率、L2-L4级CAV、平均延误时间和行程时间
	[46]	VISSIM	合流和分流路段	车队横、纵向驾驶行为	渗透率、L2-L4级CAV、平均延误时间和行程时间
基于交通管理策略的风险评估	[47]	MATLAB	瓶颈路段	可变限速策略	车辆数、渗透率、总行程时间
	[48]	PreScanN、MATLAB	主干道	变速策略	车队组合、加速度、车间距
	[49]	VISSIM、MATLAB、Ca2X	入口匝道	匝道控制方法和渐进限速控制方法	平均速度、延迟和流量
	[50]	SUMO	出入口匝道	深度强化学习算法、拥堵模式、安全性、效率和系统适应度	渗透率、流量、行程时间、碰撞风险、加速度
基于新开发模拟框架的风险评估	[51]	VISSIM	基本路段和匝道	外部驱动模型、效率和安全	渗透率、车型、流量
	[52]	CARSIM/SIMULINK	基本路段	协作并分布式控制，执行器延迟和不利通信条件	车队、加速度、总通信时间延迟
	[53]	PreScanN、MATLAB	基本路段	博弈论轨迹规划框架；安全、舒适和效率	车间距、横纵向加速度、出行成本

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表 7 智能网联环境下风险防控应用场景

Table 7. Risk prevention and control application scenarios in a connected and automated environment

类别	应用场景
交通安全类	车辆失控预警	异常车辆提醒
	恶劣天气预警	道路危险状况提示
	前方拥堵预警	二次事故预警
	车内标牌	逆向超车预警
交通效率类	特殊车辆识别/提醒	超数据信息辅助
	车速引导	编队行驶
	潮汐/动态车道行驶	限速提醒
	交通信息及路径规划	电子不停车收费
信息服务类	充电桩目的指引导	自动停车引导及控制
	车辆远程诊断、维修保养体术	智能移动设备与汽车功能交互

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表 8 非网联环境与网联环境的交通运行风险分析方法对比

Table 8. Comparison of risk analysis methods in non-connected and connected environments

类别	感知能力	风险指标	风险评估	风险防控
非网联环境	小样本数据	事后指标	统计分析模型	传统设施静态告知
	固定式信息采集线圈+地磁为主断面、时段集计流/密/速	排队、延误、行程时间等参数提取交通状态识别	对象模糊化基于路段流量，方案模式化	模型驱动子系统间交互难以量化协同程度低
	多源大数据	事前替代	机器学习模型	新型智能设备和车载终端动态引导
网联环境	移动传感器 ETC数据、互联网数据、路测数据、车载数据为主个体车辆轨迹/路径（抽样）感知条件和要求提高	基于轨迹和路径的交通状态完整刻画交通状态机理认知冲突理论	基于关键路径，对象精准化基于路径流量，方案精细化评价-诊断-优化闭环	数据+模型驱动子系统间交互精确掌握多系统高效协同

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