A Review of Truck Driving Behavior and Safety

doi:10.16097/j.cnki.1009-6744.2022.05.007

Abstract

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

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

CLC Number:

U491.2

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.

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.

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References

[1] GSUL M, 胡予红, 周旋, 等. 道路交通运输安全发展报告(2017)[J]. 中国应急管理, 2018, 12(2): 48-58. [GSUL M, HU Y H, ZHOU X, et al. Road traffic safety development report(2017) [J]. China Emergency Management, 2018, 12(2): 48-58.]

[2] ARBABZADEH N, JAFARI M. A data-driven approach for driving safety risk prediction using driver behavior and roadway information data[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(2): 446- 460.

[3] FORMOSA N, QUDDUS M, ISON S, et al. Predicting real-time traffic conflicts using deep learning[J]. Accident Analysis and Prevention, 2020, 136: 1-14.

[4] YUKSEL A S, ATMACA S. Driver's black box: A system for driver risk assessment using machine learning and fuzzy Logic[J]. Journal of Intelligent Transportation Systems, 2021, 25(5): 482-500.

[5] LI G F, LI S B, CHENG B, et al. Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities[J]. Transportation Research PartC: Emerging Technologies, 2017, 74: 113-125.

[6] CHEN G X, FANG Y, FENG G, et al. The influence of daily sleep patterns of commercial truck drivers on driving performance[J]. Accident Analysis and Prevention, 2016, 91: 55-63.

[7] ZHOU T, ZHANG J. Analysis of commercial truck drivers' potentially dangerous driving behaviors based on 11-month digital tachograph data and multilevel modeling approach[J]. Accident Analysis and Prevention, 2019, 132: 1-11.

[8] 徐婷, 张香, 张亚坤, 等. 基于 AdaBoost 算法的货车驾驶人安全倾向性分类[J]. 安全与环境学报, 2019, 19 (4): 1273-1281. [XU T, ZHANG X, ZHANG Y K, et al. AdaBoost-based algorithm for truck driver safety propensity classification[J]. Journal of Safety and Environment, 2019, 19(4): 1273-1281.]

[9] 王海星, 王翔宇, 王招贤, 等. 基于数据挖掘的危险货物运输风险驾驶行为聚类分析[J]. 交通运输系统工程与信息, 2020, 20(1): 183-189. [WANG H X, WANG X Y, WANG Z X, et al. Dangerous driving behavior clustering analysis for hazardous materials transportation based on data mining[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 183-189.]

[10] CAI M, YAZDI M A A, MEHDIZADEH A, et al. The association between crashes and safety-critical events: Synthesized evidence from crash reports and naturalistic driving data among commercial truck drivers[J]. Transportation Research Part C: Emerging Technologies, 2021, 126: 1-19.

[11] CORI J M, DOWNEY L A, SLETTEN T L, et al. The impact of 7-hour and 11-hour rest breaks between shifts on heavy vehicle truck drivers' sleep, alertness and naturalistic driving performance[J]. Accident Analysis and Prevention, 2021, 159: 1-13.

[12] YANG C, CHEN M, YUAN Q. The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis[J]. Accident Analysis and Prevention, 2021, 158: 1-18.

[13] YUAN Y, YANG M, GUO Y, et al. Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers[J]. Journal of Safety Research, 2021, 76: 154- 165.

[14] HAQ M T, ZLATKOVIC M, KSAIBATI K. Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical bayesian random intercept approach[J]. Accident Analysis and Prevention, 2021, 162: 1-15.

[15] MEHDIZADEH A, YAZDI M A A, CAI M, et al. Predicting unsafe driving risk among commercial truck drivers using machine learning: Lessons learned from the surveillance of 20 million driving miles[J]. Accident Analysis and Prevention, 2021, 159: 1-12.

[16] WEI C H, LEE Y, LUO Y W, et al. Incorporating personality traits to assess the risk level of aberrant driving behaviors for truck drivers[J]. International Journal of Environmental Research and Public Health, 2021, 18(9): 1-18.

[17] 胡立伟, 李耀平, 林玉珍. 不同等级公路环境下驾驶人动态视觉特性规律研究[J]. 交通运输系统工程与信息, 2014, 14(3): 58- 63. [HU L W, LI Y P, LIN Y Z. Driver's dynamic visual characteristics and laws under different highway environments[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(3): 58-63.]

[18] 周悦, 江欣国, 付川云, 等. 基于混合Logit模型的出租车超速者运营因素分析[J]. 交通运输系统工程与信息, 2021, 21(3): 229-236. [ZHOU Y, JIANG X G, FU C Y, et al. Operational factors analysis for taxi speeders using mixed Logit model[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 229-236.]

[19] 周扬, 付锐, 刘卓凡. 考虑驾驶人不同分心类型的分心识别研究[J/OL]. 交通运输系统工程与信息, (2022- 03-01)[2022-03-21]. https://kns.cnki.net/kcms/detail/ 11.4520.U.20220228.1612.006.html. [ZHOU Y, FU R, LIU Z F. Research on distracted driving recognition considering different types of distraction[J/OL]. Journal of Transportation Systems Engineering and Information Technology, (2022-03-01) [2022-03-21]. https: //kns. cnki.net/kcms/detail/11.4520.U.20220228.1612.006. html.]

[20] 吴超仲, 孙一帆, 张晖, 等. 疲劳驾驶转向特征指标的个体差异敏感度分析[J]. 交通运输系统工程与信息, 2019, 19(5): 120-127. [WU C Z, SUN Y F, ZHANG H, et al. Sensitivities of fatigue driving steering features to individual difference[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(5): 120-127.]

[21] HOSSAIN M, ABDEL-ATY M, QUDDUS M A, et al. Real-time crash prediction models: State- of-the- art, design pathways and ubiquitous requirements[J]. Accident Analysis and Prevention, 2019, 124: 66-84.

[22] CANTOR D E, CORSI T M, GRIMM C M, et al. A driver focused truck crash prediction model[J]. Transportation Research Part E: Logistics and Transportation Review, 2010, 46(5): 683-692.

[23] HICKMAN J S, HANOWSKI R J. Use of a video monitoring approach to reduce at-risk driving behaviors in commercial vehicle operations[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2011, 14(3): 189-198.

[24] SIORDIA O S, DIEGO I M D, CONDE C, et al.Combining traffic safety knowledge for driving risk detection[C]. Washington DC: 14th IEEE Intelligent Transportation Systems (ITSC), 2011.

[25] ZHU X, SRINIVASAN S. A comprehensive analysis of factors influencing the injury severity of large-truck crashes[J]. Accident Analysis and Prevention, 2011, 43 (1): 49-57.

[26] 任园园, 李显生, 席建锋. 驾驶行为决策风险的多维结构模型[J]. 吉林大学学报(工学版), 2011, 41(2): 56-61. [REN Y Y, LI X S, XI J F. Multi-dimensional structural model of risk on driving behavior decision-making[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(2): 56-61.]

[27] CHEN C, XIE Y. The impacts of multiple rest-break periods on commercial truck driver's crash risk[J]. Journal of Safety Research, 2014, 48: 87-93.

[28] CHOI S, OH C, KIM M. Risk factors related to fatal truck crashes on Korean freeways[J]. Traffic Injury Prevention, 2014, 15(1): 73-80.

[29] PAHUKULA J, HERNANDEZ S, UNNIKRISHNAN A. A time of day analysis of crashes involving large trucks in urban areas[J]. Accident Analysis and Prevention, 2015, 75: 155-163.

[30] 孙川, 吴超仲, 褚端峰, 等. 基于车联网数据挖掘的营运车辆驾驶速度行为聚类研究[J]. 交通运输系统工程与信息, 2015, 15(6): 86-91. [SUN C, WU C Z, CHU D F, et al. Driving speed behavior clustering for commercial vehicle based on connected vehicle data mining[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 86-91.]

[31] GIROTTO E, ANDRADE S M, GONZÁLEZ A D, et al. Professional experience and traffic accidents/near-miss accidents among truck drivers[J]. Accident Analysis and Prevention, 2016, 95: 299-304.

[32] WU C, SUN C, CHU D, et al. Clustering of several typical behavioral characteristics of commercial vehicle drivers based on GPS data mining[J]. Transportation Research Record Journal of the Transportation Research Board, 2016, 2581(2581): 154-163.

[33] DONG C, DONG Q, HUANG B, et al. Estimating factors contributing to frequency and severity of large truckinvolved crashes[J]. Journal of Transportation Engineering Part A: Systems, 2017, 143(8): 1-7.

[34] ISELAND T, JOHANSSON E, SKOOG S, et al. An exploratory study of long-haul truck Drivers' secondary tasks and reasons for performing them[J]. Accident Analysis and Prevention, 2018, 117: 154-163.

[35] NADERI H, NASSIRI H, SAHEBI S. Assessing the relationship between heavy vehicle driver sleep problem and confirmed driver behavior measurement tools in Iran [J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 59: 57-66.

[36] AGRAWAL U, MASE J M, FIGUEREDO G P, et al. Towards real-time heavy goods vehicle driving behaviour classification in the United Kingdom[C]. Auckland: 22th IEEE Intelligent Transportation Systems (ITSC), 2019.

[37] CLAVERIA J B, HERNANDEZ S, ANDERSON J C, et al. Understanding truck driver behavior with respect to cell phone use and vehicle operation[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 65: 389-401.

[38] FIGUEREDO G P, AGRAWAL U, MASE J M M, et al. Identifying heavy goods vehicle driving styles in the United Kingdom[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(9): 3324-3336.

[39] HAMMOND R L, SOCCOLICH S A, HANOWSKI R J. The impact of driver distraction in tractor-trailers and motorcoach buses[J]. Accident Analysis and Prevention, 2019, 126: 10-16.

[40] HONG J, PARK J, LEE G, et al. Endogenous commercial driver's traffic violations and freight truckinvolved crashes on mainlines of expressway[J]. Accident Analysis and Prevention, 2019, 131: 327-335.

[41] JIN Y, JIA Z, WANG P, et al. Quantitative assessment on truck-related road risk for the safety control via truck flow estimation of various types[J]. IEEE Access, 2019, 7: 88799-88810.

[42] MEHDIZADEH M, SHARIAT-MOHAYMANY A, NORDFJAERN T. Driver behaviour and crash involvement among professional taxi and truck drivers: Light passenger cars versus heavy goods vehicles[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 62: 86-98.

[43] WANG Y, PRATO C G. Determinants of injury severity for truck crashes on mountain expressways in China: A case-study with a partial proportional odds model[J]. Safety Science, 2019, 117: 100-107.

[44] FILTNESS A J, HICKMAN J S, MABRY J E, et al. Associations between high caffeine consumption, driving safety indicators, sleep and health behaviours in truck drivers[J]. Safety Science, 2020, 126: 1-9.

[45] LANDAY K, WOOD D, HARMS P D, et al. Relationships between personality facets and accident involvement among truck drivers[J]. Journal of Research in Personality, 2020, 84: 1-10.

[46] MASE J M, MAJID S, FIGUEREDO G P, et al. Evaluating the impact of heavy goods vehicle driver monitoring and coaching to reduce risky behaviour[J]. Accident Analysis and Prevention, 2020, 146: 105754.

[47] NIU S, UKKUSURI S V. Risk assessment of commercial dangerous-goods truck drivers using geo-location data: A case study in China[J]. Accident Analysis and Prevention, 2020, 137: 105427.

[48] WANG Q, ZHANG R, WANG Y, et al. Machine learning-based driving style identification of truck drivers in openpit mines[J]. Electronics, 2020, 9(1): 1-23.

[49] BLEI D M, NG A Y, JORDAN M I, et al. Latent dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.

[50] FOULDS J, BOYLES L, DUBOIS C, et al. Stochastic collapsed variational bayesian inference for latent dirichlet allocation[C]. Chicago: 19th ACM SIGKDD Knowledge Discovery and Data Mining , 2013.

[51] HOFFMAN M D, BLEI D M, WANG C, et al. Stochastic variational inference[J]. Journal of Machine Learning Research, 2013, 14(5): 1303-1347.

[52] 李国法, 陈耀昱, 吕辰, 等. 智能汽车决策中的驾驶行为语义解析关键技术[J]. 汽车安全与节能学报, 2019, 10(4): 391-412. [LI G F, CHEN Y Y, LV C, et al. Key techniques of semantic analysis of driving behavior in decision making of autonomous vehicles[J]. Journal of Automotive Safety and Energy, 2019, 10(4): 391-412.]

[53] 郝景贤, 余卓平, 赵治国, 等. 混合动力汽车驾驶风格识别的研究[J]. 汽车工程, 2017, 39(12): 1444-1450. [HAO J X, YU Z P, ZHAO Z G, et al. A study on the driving style recognition of hybrid electric vehicle[J]. Automotive Engineering, 2017, 39(12): 1444-1450.]

[54] QI G Q, WU J P, ZHOU Y, et al. Recognizing driving styles based on topic models[J]. Transportation Research Part D: Transport and Environment, 2019, 66: 13-22.

[55] MARKO M, BORIS A, LIPOVAC K, et al. Behaviours of drivers in Serbia: Non-professional versus professional drivers[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 52: 101-111.

[56] 王建强, 吴剑, 李洋. 基于人-车-路协同的行车风险场概念原理及建模[J]. 中国公路学报, 2016, 29(1): 105- 114. [WANG J Q, WU J, LI Y. Concept, principle and modeling of driving risk field based on driver-vehicleroad Interaction[J]. China Journal of Highway and Transport, 2016, 29(1): 105-141.]

[57] FORMOSA N, QUDDUS M, ISON S, et al. Predicting real-time traffic conflicts using deep learning[J]. Accident Analysis and Prevention, 2020, 136: 105429.

[58] SONG L, FAN W. Combined latent class and partial proportional odds model approach to exploring the heterogeneities in truck-involved severities at cross and T- intersections[J]. Accident Analysis and Prevention, 2020, 144: 105638.

[59] BELL J L, TAYLOR M A, CHEN G X, et al. Evaluation of an in-vehicle monitoring system (IVMS) to reduce risky driving behaviors in commercial drivers: Comparison of in- cab warning lights and supervisory coaching with videos of driving behavior[J]. Journal of Safety Research, 2017, 60: 125-136.

[60] FARMER C M, KIRLEY B B, MCCARTT T T. Effects of in-vehicle monitoring on the driving behavior of teenagers [J]. Journal of Safety Research, 2010, 41(1): 39-45.

[61] 李国法. 基于模式转移和操控特性的驾驶风格评测研究[D]. 北京: 清华大学, 2016. [LI G F. Driving style assessment using maneuver transition probabilities and driver operation aggressiveness[D]. Beijing: Tsinghua University, 2016.]

[62] LEMP J D, KOCKELMAN K, UNNIKRISHNAN A. Analysis of large truck crash severity using heteroskedastic ordered probit models[J]. Accident Analysis and Prevention, 2011, 43(1): 370-380.

[63] HICKMAN J S, HANOWSKI R J, BOCANEGRA J. A synthetic approach to compare the large truck crash causation study and naturalistic driving data[J]. Accident Analysis and Prevention, 2018, 112: 11-14.

[64] Federal Motor Carrier Safety Administration. Report to congress on the large truck crash causation study[EB/ OL]. Washington DC: Federal Motor Carrier Safety Administration, 2006[2022- 03- 21]. http: //www. fmcsa. dot. gov/ facts-research/ research-technology/ report/ltccs2006.htm.

[65] HICKMAN J S, KNIPLING R R, OLSON R L, et al. Light vehicle-heavy vehicle interaction data collection and countermeasure research project[R]. Washington DC: FMCSA, 2016.

[66] 薛清文, 蒋愚明, 陆键. 基于轨迹数据的危险驾驶行为识别方法[J]. 中国公路学报, 2020, 33(6): 84-94. [XUE Q W, JIANG Y M, LU J. Risky driving behavior recognition based on trajectory data[J]. China Journal of Highway and Transport, 2020, 33(6): 84-94.]

[67] 戢晓峰, 吴亚欣, 郝京京, 等. 平纵组合路段事故严重程度致因辨识模型[J]. 交通运输系统工程与信息, 2020, 20(6): 197-204. [JI X F, W Y X, HAO J J, et al. Severity factors analysis model of traffic accident on combined horizontal and vertical alignments[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(6): 197-204.]

[68] YANG K, WANG X X, QUDDUS M, et al. Deep learning for real-time crash prediction on urban expressways[C]. Washington DC: 97th Transportation Research Board Annual Meeting, 2018.

[69] LIN L, WANG Q, SADEK A W. A novel variable selection method based on frequent pattern tree for realtime traffic accident risk prediction[J]. Transportation Research Part C: Emerging Technologies, 2015, 55: 444- 459.

[70] AHMED M M, ABDEL-ATY M A. The viability of using automatic vehicle identification data for real-time crash prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 459-468.

[71] HARB R, YAN X, RADWAN E, et al. Exploring precrash maneuvers using classification trees and random forests[J]. Accident Analysis and Prevention,2009, 41(1): 98-107.

[72] HAGHIGHI N, LIU X C, ZHANG G, et al. Impact of roadway geometric features on crash severity on rural two-lane highways[J]. Accident Analysis and Prevention, 2018, 111: 34-42.

[73] BERMÚDEZ L, KARLIS D. A finite mixture of bivariate poisson regression models with an application to insurance ratemaking[J]. Computational Stats and Data Analysis, 2012, 56(12): 3988-3999.

[74] ZOU Y, ZHANG Y, LORD D. Application of finite mixture of negative binomial regression models with varying weight parameters for vehicle crash data analysis [J]. Accident Analysis and Prevention, 2013, 50: 1042- 1051.

[75] CHEN C, ZHANG G H, TIAN Z, et al. Hierarchical bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations[J]. Accident Analysis and Prevention, 2015, 85(12): 186-198.

[76] MENENDEZ M, SASIDHARAN L. Partial proportional odds model-an alternate choice for analyzing pedestrian crash injury severities[J]. Accident Analysis and Prevention, 2014, 72: 330-340.

[77] YAO Y, ZHAO X H, WU Y P, et al. Clustering driver behavior using dynamic time warping and hidden Markov model[J]. Journal of Intelligent Transportation Systems, 2021, 25(3): 249-262.

[78] CHANG X, WU J, LIU H, et al. Travel mode choice: A data fusion model using machine learning methods and evidence from travel diary survey data[J]. Transportmetrica A: Transport Science, 2019, 15(2): 1587-1612.

[79] HYLAND M, HONG Z, DE FARIAS PINTO H K R, et al. Hybrid cluster-regression approach to model bikeshare station usage[J]. Transportation Research Part A: Policy and Practice, 2018, 115: 71-89.

[80] 朱顺应, 蒋若曦, 王红, 等. 机动车交通冲突技术研究综述[J]. 中国公路学报, 2020, 33(2): 15-33. [ZHU S Y, JIANG R X, WANG H, et al. Review of research on traffic conflict technique[J]. China Journal of Highway and Transport, 2020, 33(2): 15-33.]

[81] 李航. 统计学习方法(第2版)[M]. 北京: 清华大学出版社, 2019. [LI H. Statistical learning methods(2th ed.)[M]. Beijing: Tsinghua University Press, 2019.]

[82] 戢晓峰, 卢梦媛, 覃文文. 货车移动遮断影响下的小客车驾驶行为识别[J]. 交通运输系统工程与信息, 2021, 21(5): 174-182. [JI X F, LU M Y, QIN W W, et al. Passenger cars driving recognition under truck movement interruption[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 174-182.]

[83] CHEN Y K, LUO R J, KING M, et al. Spatiotemporal analysis of crash severity on rural highway: A case study in Anhui, China[J]. Accident Analysis and Prevention, 2022, 165: 106538.

[84] ELASSAD Z, MOUSANNIF H, MOATASSIME H A. A real-time crash prediction fusion framework: An imbalance-aware strategy for collision avoidance systems [J]. Transportation Research Part C: Emerging Technologies, 2020, 118: 1-24.

[85] CAI Q, ABDEL-ATY M, YUAN J, et al. Real-time crash prediction on expressways using deep generative models [J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102697.

[86] 甘蜜, 卿三东, 刘晓波, 等. 货车轨迹数据在公路货运系统中应用研究综述[J]. 交通运输系统工程与信息, 2021, 21(5): 91-101, 113. [GAN M, QING S D, LIU X B, et al. Review on application of truck trajectory data in highway freight system[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 91-101, 113.]