货车驾驶人驾驶行为与行车安全研究进展

doi:10.16097/j.cnki.1009-6744.2022.05.007

交通运输系统工程与信息 ›› 2022, Vol. 22 ›› Issue (5): 55-74.DOI: 10.16097/j.cnki.1009-6744.2022.05.007

所属专题： 2022年英文专栏

• 综合交通运输体系论坛 • 上一篇下一篇

货车驾驶人驾驶行为与行车安全研究进展

覃文文^{1, 2} ，李欢¹ ，李武³，谷金晶⁴ ，戢晓峰^{* 1, 2}

1. 昆明理工大学，交通工程学院，昆明 650504；2. 云南省现代物流工程研究中心，昆明 650504； 3. 大连理工大学，建设工程学部，辽宁大连 116024；4. 同济大学，电子与信息工程学院，上海 201804

收稿日期:2022-03-21 修回日期:2022-03-31 接受日期:2022-04-06 出版日期:2022-10-25 发布日期:2022-10-20
作者简介:覃文文(1986- )，男，广西柳州人，讲师，博士。
基金资助:
国家自然科学基金

A Review of Truck Driving Behavior and Safety

QIN Wen-wen^{1, 2} , LI Huan¹ , LI Wu³ , GU Jin-jing⁴ , JI Xiao-feng^{* 1, 2}

1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China; 2. Yunnan Modern Logistics Engineering Research Center, Kunming 650504, China; 3. Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China; 4. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

Received:2022-03-21 Revised:2022-03-31 Accepted:2022-04-06 Online:2022-10-25 Published:2022-10-20
Supported by:
National Natural Science Foundation of China (52002161, 52062024, 52102382)

摘要/Abstract

摘要： 驾驶行为是影响交通安全最活跃的因素，在“人-车-路”复杂环境中扮演着关键角色。为了深入理解货车驾驶人驾驶行为规律和行为风险性，本文聚焦货车驾驶人驾驶行为对行车安全的影响，对货车驾驶人的驾驶行为风格、行为风险性及其与行车安全的关系等相关研究成果进行系统地梳理和分析。首先，利用构建的文献检索策略，筛选出38篇相关文献，并结合LDA(Latent Dirichlet Allocation)模型，对生成的4个研究主题，即货车驾驶人驾驶行为辨识，危险驾驶行为与行车安全，货车碰撞事故致因分析及驾驶安全风险评估进行总结；其次，针对数据源、特征工程及建模方法等分析要素，构建了适用于任意研究主题的通用研究路径，并重点归纳了目前研究主题在数据源、变量选择方法、研究地点及建模方法等关键要素的研究进展；最后，分析和探讨了货车驾驶人驾驶行为与行车安全领域面临的主要问题，从描述、解释、关联及应用的角度提炼该领域研究的未来发展趋势。研究认为：有必要将驾驶状态特性、车辆运行状态及道路交通状况等多维特征变量进行多源信息融合，开展基于大数据和人工智能双驱动的驾驶行为研究；需加强研究山区公路环境下货车与其他类型车辆之间的交互作用机制，从“人-车-路”视角分析货车碰撞事故致因；需进一步完善智能网联和自动驾驶等高新智能自动化环境下的货车驾驶人驾驶行为与行车安全关系研究；拓展面向驾驶安全的货车驾驶人驾驶风险评估的理论方法和应用框架。研究成果将为货车事故治理、公路货运平台监控及道路线形设计等应用场景提供重要依据，并有助于相对全面地理解货车驾驶人驾驶行为与道路行车安全的交互作用机理。

关键词: 交通工程, 驾驶行为, LDA模型, 货车驾驶人, 行车安全, 风险评估

Abstract: Driving behavior plays the most critical role in the complex environment of human-vehicle-road and it is a core factor in road traffic system. To deeply understand the driving behavior pattern and riskiness of truck drivers, this paper examines the influence of truck driving behavior on traffic safety, and systematically analyzes the research results related to truck driving behavior characteristics, riskiness and its relationship with traffic safety. 38 relevant literatures were screened out by using a proposed literature search strategy, and then a systematic summary by applying a LDA (Latent Dirichlet Allocation) model was given based on four research topics, including truck driving behavior identification, relationship between dangerous driving behavior and driving safety, risk factors associated with truckinvolved analysis, and driving safety assessment. Further, a general research pathway available for any topic was constructed based on the analysis elements such as data sources, feature engineering, and modelling methods, and thenfour topics were summarized with emphasis on data sources, variable selection methods, study site, and modelling methods. At last, several potential challenges on these research topics were refined, and four promising developing trends were proposed from the perspectives of description, explanation, correlation, and application. The analysis of the research indicates that it is necessary to adopt the multi-source information fusion from driving status, vehicle motion status, and road traffic conditions for research on driving behavior based on big data and artificial intelligence. Besides, further research is recommended to enhance the study of the interaction mechanism to crashes between trucks and other types of vehicles in the mountain road environment for exploring risk factors associated with truck- involved crash severity from an overall spatial-temporal view. Furthermore, it will be necessary to further improve the research on the relationship between truck driving behavior and safety under the high-tech intelligent automation environment such as intelligent connected and automated vehicles. The theoretical methodology and application framework for truck driving risk assessment should be developed. This paper provides valuable insights for truck accident management, highway freight platform monitoring, road alignment design and other application scenarios, so as to have a relatively comprehensive understanding of the interaction mechanism between truck driving behavior and traffic safety.

Key words: traffic engineering, driving behavior, LDA (Latent Dirichlet Allocation) model, truck drivers, driving safety, risk assessment

中图分类号:

U491.2

覃文文, 李欢, 李武, 谷金晶, 戢晓峰. 货车驾驶人驾驶行为与行车安全研究进展[J]. 交通运输系统工程与信息, 2022, 22(5): 55-74.

QIN Wen-wen, LI Huan, LI Wu, GU Jin-jing, JI Xiao-feng. A Review of Truck Driving Behavior and Safety[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 55-74.

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货车驾驶人驾驶行为与行车安全研究进展

A Review of Truck Driving Behavior and Safety

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