基于驾驶模拟器的高速公路工作区预警策略优化

Optimization of Warning Strategies for Highway Work Zone Based on Driving Simulator

摘要

随着高速公路使用年限的增加，日常养护已成为当前高速公路运营的重点。养护活动往往会占用部分道路资源，导致潜在的交通拥堵和安全风险。为提高高速公路工作区交通安全，在现有工作区安全设施设置的基础上，文章提出了4种优化方案，包括工作区安全设施标志加红旗、路面铺设红色频闪灯、全程提供语音导航及预警策略综合优化方案。通过驾驶模拟实验，采集驾驶人驾驶数据比较安全设施优化方案对驾驶人驾驶安全的影响，利用秩和比法综合评价方法对每种优化方案进行评估。结果显示，工作区安全设施标志加红旗、路面铺设红色频闪灯和全程提供语音导航的综合优化方案对驾驶人驾驶安全提升最高。未来在高速公路工作区可考虑加入语音导航提高驾驶人驾驶安全。

Abstract

As expressways age, routine maintenance has become a critical aspect of expressway operations. However, such maintenance often necessitates partial lane closures, which can lead to traffic congestion and heightened safety risks. To address these challenges, this study proposes four optimized strategies for the deployment of safety facilities in work zones, drawing on prior research in this area. These strategies include the work area safety facilities logo plus red flag, road laying red strobe lights, the whole process to provide voice navigation and warning strategies comprehensive optimization scheme. To assess the effectiveness of each strategy, a driving simulation experiment was conducted. Driver behavior data were collected and analyzed to evaluate the impact of the different safety configurations on driving performance. The rank sum ratio (RSR) comprehensive evaluation method was used to assess and compare the performance of each scheme. The results show that the comprehensive optimization scheme of adding red flags to the safety facilities signs in the work area, laying red strobe lights on the road surface and providing voice navigation throughout the whole process has the highest improvement in driver safety. These findings suggest that integrating voice navigation into safety systems may further enhance safety in expressway work zones.

关键词

工作区 / 预警策略 / 非整次RSR评价方法 / 交通安全

Key words

work zone / warning strategies / non-integer RSR evaluation method / traffic safety

参考文献

[1] ABDALLAH A, IBRAHIM A, RUSSELL—VERNON C, et al. Advancing safety in short—term utility work zones: assessing the role of work zone intrusion alert technologies (WZIATs) [J]. Transportation Research Interdisciplinary Perspectives, 2024, 26: 101133.

[2] 任立海, 聂珍龙, 于潇, 等 . 基于脑电信号的不良驾驶状态识别研究综述[J]. 中国公路学报, 2024, 37(8): 216-230.

[3] REN L H, NIE Z L, YU X, et al. Review on recognition of unsatisfactory driving status based on electroencephalogram[J]. China Journal of Highway and Transport, 2024, 37(8): 216-230.

[4] 严利鑫, 胡鑫辉, 刘清梅, 等 . 道路交通事故严重程度预测及致因分析[J]. 华东交通大学学报, 2024, 41(5): 65-73.

[5] YAN L X, HU X H, LIU Q M, et al. Road traffic accident severity prediction and causation analysis[J]. Journal of East China Jiaotong University, 2024, 41(5): 65-73.

[6] 夏然 . 高速公路养护作业区追尾冲突模型构建及安全改善研究[D]. 镇江: 江苏大学, 2020.

[7] XIA R . Research on rear—end conflict model construction and safety improvement in freeway work zone[D]. Zhenjiang: Jiangsu University, 2020.

[8] 吴文燕 . 基于改进元胞自动机的高速公路养护施工路段安全研究[D]. 济南: 山东交通学院, 2021.

[9] WU W Y . Security study on expressway maintenance construction section based on improved cellular automata[D]. Jinan: Shandong Jiaotong University, 2021.

[10] THAPA D, MISHRA S . Using worker’s naturalistic response to determine and analyze work zone crashes in the presence of work zone intrusion alert systems[J]. Accident Analysis & Prevention, 2021, 156: 106125.

[11] ALMALLAH M, HUSSAIN Q, ALHAJYASEEN W K M, et al. Improved traffic safety at work zones through animation—based variable message signs[J]. Accident Analysis & Prevention, 2021, 159: 106284.

[12] 交通部联邦公路局. 美国交通工程设施手册城市道路和公路（2009 版）[M]. 赵晓华, 伍毅平, 边扬, 等 译. 北京: 中国建筑工业出版社, 2020.

[13] FEDERAL HIGHWAY ADMINISTRATION, U.S. DEPARTMENT OF TRANSPORTATION. Manual on uniform traffic control devices for streets and highways(2009 edition)[M]. ZHAO X H, WU Y P, BIAN Y, et al(translation). Beijing : China Construction Industry Press, 2020.

[14] CHEN Z C, LI H, BAO Y Q, et al. Identification of spatio—temporal distribution of vehicle loads on long—span bridges using computer vision technology[J]. Structural Control and Health Monitoring, 2016, 23(3): 517-534.

[15] WANG A G, HUANG C X, WANG J Y, et al. The association between physiological and eye—tracking metrics and cognitive load in drivers: a meta—analysis[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2024, 104: 474-487.

[16] DOU J Z, XU C, WU W Y, et al. Using a visual attentional task and eye—tracking to investigate the effects of pre—takeover resource engagement on attention restoration in conditionally automated driving[J]. Traffic Injury Prevention, 2025, 26(5): 506-514.

[17] CRAIG A, TRAN Y, WIJESURIYA N, et al. Regional brain wave activity changes associated with fatigue[J]. Psychophysiology, 2012, 49(4): 574-582.

[18] YANG Y Q, YE Z H, EASA S M, et al. Effect of driving distractions on driver mental workload in work zone’s warning area[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2023, 95: 112-128.

[19] 吕新兵, 孟丽萍, 潘春华, 等 . TOPSIS 联合RSR 综合评价法在CHS—DRG 运营绩效体系建设中的应用研究[J]. 中国卫生经济, 2024, 43(9): 23-28.

[20] LYU X B, MENG L P, PAN C H, et al. Research on the application of TOPSIS combined with RSR comprehensive evaluation method in the construction of CHS—DRG operational performance system[J]. Chinese Health Economics, 2024, 43(9): 23-28.

[21] 李智强, 王少成, 柴华, 等 . 基于AHP和熵权法组合赋权的空间目标威胁评估方法[J]. 信息工程大学学报, 2024, 25(6): 751-756.

[22] LI Z Q, WANG S C, CHAI H, et al. A spatial target threat assessment method based on combined weighting by ahp and entropy weighting [J]. Journal of Information Engineering University, 2024, 25(6): 751-756.

基金资助

国家自然科学基金项目(62376059)

河北省交通厅科技项目(SH2018-5)

中央引导地方科技发展专项(2023L3033)

福建省本科高校教育教学研究项目(FBJY20230045)

教育部产学合作协同育人项目(231104575143120)

教育部产学合作协同育人项目(241002554295249)