• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于具有车道间相关性的与照明相关的微观交通模型对高速公路连续隧道的二次事故风险进行建模和缓解

Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency.

机构信息

National Engineering and Research Center for Mountainous Highways, China Merchants Chongqing Communications Research & Design Institute Co., Ltd., Chongqing 400067, China.

School of Smart City, Chongqing Jiaotong University, Chongqing 400067, China.

出版信息

Int J Environ Res Public Health. 2023 Feb 9;20(4):3066. doi: 10.3390/ijerph20043066.

DOI:10.3390/ijerph20043066
PMID:36833757
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9967854/
Abstract

This paper models and mitigates the secondary crash (SC) risk for serial tunnels on the freeway which is incurred by traffic turbulence after primary crash (PC) occurrence and location-heterogeneous lighting conditions along serial tunnels. A traffic conflict approach is developed where SC risk is quantified using a surrogate safety measure based on the simulated vehicle trajectories after PC occurs from a lighting-related microscopic traffic model with inter-lane dependency. Numerical examples are presented to validate the model, illustrate SC risk pattern over time, and evaluate the countermeasures for SC, including adaptive tunnel lighting control (ATLC) and advanced speed and lane-changing guidance (ASLG) for connected vehicles (CVs). The results demonstrate that the tail of the stretching queue on the PC occurrence lane, the adjacent lane of the PC-incurred queue, and areas near tunnel portals are high-risk locations. In serial tunnels, creating a good lighting condition for drivers is more effective than advanced warnings in CVs to mitigate SC risk. Combined ATLC and ASLG is promising since ASLG informs CVs of an immediate response to traffic turbulence on the lane where PC occurs and ATLC alleviates SC risks on adjacent lanes via smoothing the lighting condition variations and reducing inter-lane dependency.

摘要

本文针对高速公路串联隧道中因首次事故（PC）发生后交通紊流和沿串联隧道位置异质照明条件而导致的次生事故（SC）风险进行建模和缓解。开发了一种交通冲突方法，其中 SC 风险使用基于与车道相关的微观交通模型的车辆轨迹模拟的替代安全措施来量化，该模型具有车道间相关性。提出了数值示例来验证模型，说明 SC 风险随时间的变化模式，并评估 SC 的对策，包括自适应隧道照明控制（ATLC）和联网车辆（CV）的高级速度和换道引导（ASLG）。结果表明，PC 发生车道上的伸展队列的尾部、PC 造成的队列的相邻车道以及隧道入口附近的区域是高风险区域。在串联隧道中，为驾驶员创造良好的照明条件比 CV 中的高级预警更能有效地减轻 SC 风险。组合的 ATLC 和 ASLG 很有前途，因为 ASLG 通知 CV 对发生 PC 的车道上的交通紊流立即做出响应，而 ATLC 通过平滑照明条件变化和减少车道间依赖性来减轻相邻车道的 SC 风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/28516d298cc2/ijerph-20-03066-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/b995f23492dd/ijerph-20-03066-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/428ec80f1dfc/ijerph-20-03066-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/8a1c7fb2f970/ijerph-20-03066-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/8763b733050c/ijerph-20-03066-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/aa247a4f845b/ijerph-20-03066-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/5874d3ca804d/ijerph-20-03066-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/0b3d69e50d36/ijerph-20-03066-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/45f0cd0dd0f8/ijerph-20-03066-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/b5eae966b018/ijerph-20-03066-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/495ddfddb851/ijerph-20-03066-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/69ad77525cc0/ijerph-20-03066-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/a3649de3e64d/ijerph-20-03066-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/98222d25327d/ijerph-20-03066-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/e85c3e0f541b/ijerph-20-03066-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/9ad3bcb3a494/ijerph-20-03066-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/81336853b4c8/ijerph-20-03066-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/80e43bf3697a/ijerph-20-03066-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/0d0e92a41822/ijerph-20-03066-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/98a1b7928684/ijerph-20-03066-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/e9658bc92dce/ijerph-20-03066-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/57f21ee1ea76/ijerph-20-03066-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/20b16e09bcb7/ijerph-20-03066-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/28516d298cc2/ijerph-20-03066-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/b995f23492dd/ijerph-20-03066-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/428ec80f1dfc/ijerph-20-03066-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/8a1c7fb2f970/ijerph-20-03066-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/8763b733050c/ijerph-20-03066-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/aa247a4f845b/ijerph-20-03066-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/5874d3ca804d/ijerph-20-03066-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/0b3d69e50d36/ijerph-20-03066-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/45f0cd0dd0f8/ijerph-20-03066-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/b5eae966b018/ijerph-20-03066-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/495ddfddb851/ijerph-20-03066-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/69ad77525cc0/ijerph-20-03066-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/a3649de3e64d/ijerph-20-03066-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/98222d25327d/ijerph-20-03066-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/e85c3e0f541b/ijerph-20-03066-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/9ad3bcb3a494/ijerph-20-03066-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/81336853b4c8/ijerph-20-03066-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/80e43bf3697a/ijerph-20-03066-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/0d0e92a41822/ijerph-20-03066-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/98a1b7928684/ijerph-20-03066-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/e9658bc92dce/ijerph-20-03066-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/57f21ee1ea76/ijerph-20-03066-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/20b16e09bcb7/ijerph-20-03066-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1237/9967854/28516d298cc2/ijerph-20-03066-g020.jpg

相似文献

1
Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency.基于具有车道间相关性的与照明相关的微观交通模型对高速公路连续隧道的二次事故风险进行建模和缓解
Int J Environ Res Public Health. 2023 Feb 9;20(4):3066. doi: 10.3390/ijerph20043066.
2
Safety and operational impact of connected vehicles' lane configuration on freeway facilities with managed lanes.具有车道管理设施的高速公路上，车联网的车道配置对安全和运行的影响。
Accid Anal Prev. 2020 Sep;144:105616. doi: 10.1016/j.aap.2020.105616. Epub 2020 Jun 6.
3
Revisiting freeway single tunnel crash characteristics analysis: A six-zone analytic approach.重新审视高速公路单隧道碰撞特性分析：六区分析方法。
Accid Anal Prev. 2020 Jul;142:105542. doi: 10.1016/j.aap.2020.105542. Epub 2020 May 21.
4
Assessment of freeway traffic parameters leading to lane-change related collisions.导致与变道相关碰撞的高速公路交通参数评估。
Accid Anal Prev. 2006 Sep;38(5):936-48. doi: 10.1016/j.aap.2006.03.004. Epub 2006 May 26.
5
Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels.探讨高速公路隧道中考虑二次碰撞情况下的碰撞事故伤害严重程度的影响因素。
Int J Environ Res Public Health. 2023 Feb 20;20(4):3723. doi: 10.3390/ijerph20043723.
6
Assessing the crash risk of mixed traffic on multilane rural highways using a proactive safety approach.采用主动安全方法评估多车道农村公路混合交通的碰撞风险。
Accid Anal Prev. 2023 Aug;188:107099. doi: 10.1016/j.aap.2023.107099. Epub 2023 May 7.
7
Development of a lane change risk index using vehicle trajectory data.基于车辆轨迹数据的变道风险指数开发。
Accid Anal Prev. 2018 Jan;110:1-8. doi: 10.1016/j.aap.2017.10.015. Epub 2017 Nov 6.
8
Impacts of speed variations on freeway crashes by severity and vehicle type.速度变化对严重程度和车型的高速公路事故的影响。
Accid Anal Prev. 2018 Dec;121:213-222. doi: 10.1016/j.aap.2018.09.015. Epub 2018 Sep 25.
9
Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories.基于经验车辆轨迹的车辆摆动时换道安全性和运行影响的短期预测。
Accid Anal Prev. 2020 Feb;135:105345. doi: 10.1016/j.aap.2019.105345. Epub 2019 Nov 18.
10
Interactive risk analysis on crash injury severity at a mountainous freeway with tunnel groups in China.中国山区含隧道群高速公路碰撞事故伤害严重度的交互式风险分析。
Accid Anal Prev. 2018 Feb;111:56-62. doi: 10.1016/j.aap.2017.11.024. Epub 2017 Nov 21.

本文引用的文献

1
Longitudinal traffic conflict analysis of autonomous and traditional vehicle platoons in field tests via surrogate safety measures.基于替代安全措施的现场测试中自动驾驶与传统车队的纵向交通冲突分析。
Accid Anal Prev. 2022 Nov;177:106822. doi: 10.1016/j.aap.2022.106822. Epub 2022 Sep 11.
2
A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records.基于数据驱动的贝叶斯网络模型用于评估具有交通违规和事故记录的个体驾驶员的概率性碰撞风险。
Accid Anal Prev. 2022 Oct;176:106790. doi: 10.1016/j.aap.2022.106790. Epub 2022 Aug 4.
3
Learning the representation of surrogate safety measures to identify traffic conflict.
学习代理安全措施的表示方法以识别交通冲突。
Accid Anal Prev. 2022 Sep;174:106755. doi: 10.1016/j.aap.2022.106755. Epub 2022 Jun 14.
4
Understanding drivers' awareness, habits and intentions inside road tunnels for effective safety policies.了解道路隧道内驾驶员的意识、习惯和意图，以制定有效的安全政策。
Accid Anal Prev. 2022 Jul;172:106690. doi: 10.1016/j.aap.2022.106690. Epub 2022 May 6.
5
Transfer learning-based highway crash risk evaluation considering manifold characteristics of traffic flow.基于流形特性的交通转移学习的高速公路事故风险评估。
Accid Anal Prev. 2022 Apr;168:106598. doi: 10.1016/j.aap.2022.106598. Epub 2022 Feb 15.
6
A hybrid machine learning model for predicting Real-Time secondary crash likelihood.用于预测实时二次碰撞可能性的混合机器学习模型。
Accid Anal Prev. 2022 Feb;165:106504. doi: 10.1016/j.aap.2021.106504. Epub 2021 Nov 26.
7
A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling.代理安全措施综述及其在联网和自动驾驶汽车安全建模中的应用。
Accid Anal Prev. 2021 Jul;157:106157. doi: 10.1016/j.aap.2021.106157. Epub 2021 May 8.
8
Evaluating the impact of Road Rangers in preventing secondary crashes.评估 Road Rangers 在预防二次事故中的影响。
Accid Anal Prev. 2021 Jun;156:106129. doi: 10.1016/j.aap.2021.106129. Epub 2021 Apr 29.
9
Assessing the likelihood of secondary crashes on freeways with Adaptive Signal Control System deployed on alternate routes.评估在部署于迂回路线上的自适应信号控制系统的高速公路上发生二次事故的可能性。
J Safety Res. 2021 Feb;76:314-326. doi: 10.1016/j.jsr.2020.12.020. Epub 2021 Jan 12.
10
Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation.基于减速度阈值（DRAC）的碰撞估计中阈值确定方法的比较。
Accid Anal Prev. 2021 Apr;153:106051. doi: 10.1016/j.aap.2021.106051. Epub 2021 Feb 24.