Accident; analysis and prevention最新文献_第7页

Analysis of the impact of acoustic stimulation on vigilance decrement and drowsiness on expressways and its habituation 高速公路上声刺激对警觉性下降和嗜睡的影响及其习惯化分析。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-16 DOI: 10.1016/j.aap.2025.108359

Yasuhiro Shiomi , Tingjian Zou

This study aims to evaluate the effectiveness of acoustic stimulation in enhancing driver’s vigilance, improving driving performance, and preventing inattentive driving on expressways. While numerous studies have investigated the effects of acoustic stimuli on drivers’ attention, the influence of different types of stimuli on sustained attention and driving behavior remains unclear. Particularly, the habituation effect to the stimuli during driving has not been investigated. In this study, several types of acoustic stimuli—monotone sounds, verbal messages, and emotional sounds—are examined as potential countermeasures for vigilance decrement. Their effects on inattentive driving and the stability of driving behavior are assessed and compared with a control condition using a driving simulator (DS) experiment in which each participant was asked to continuously operate a DS for 20 min under each experimental condition. The experimental results with 30 participants reveal that: (1) acoustic stimulation initially produces an awakening effect, but its effectiveness tends to decline over time due to habituation; and (2) among the three types of acoustic stimuli tested, emotional sounds have a stronger and more sustained effect on maintaining driver alertness, showing less susceptibility to habituation than monotone or verbal stimuli. These findings suggest that emotional acoustic stimuli may serve as a promising basis for the development of in-vehicle or infrastructure-based systems aimed at preventing inattentive driving and improving road safety.

本研究旨在评估声刺激在高速公路上提高驾驶员警觉性、改善驾驶性能和防止驾驶疏忽方面的有效性。虽然有大量研究调查了声刺激对驾驶员注意力的影响，但不同类型的刺激对持续注意力和驾驶行为的影响尚不清楚。特别是，驾驶过程中对刺激的习惯化效应尚未得到研究。在这项研究中，几种类型的声音刺激-单调的声音，口头信息和情绪声音-被检查作为警觉性下降的潜在对策。通过驾驶模拟器（DS）实验，评估其对不注意驾驶和驾驶行为稳定性的影响，并与对照条件进行比较。在驾驶模拟器实验中，每个参与者在每个实验条件下连续操作DS 20分钟。30名受试者的实验结果表明：(1)声刺激最初产生唤醒效应，但随着时间的推移，由于习惯作用，其效果趋于下降；(2)在三种声音刺激中，情绪声音对驾驶员警觉性的维持效果更强、更持久，对驾驶员习惯化的敏感性低于单调声音和言语刺激。这些发现表明，情绪声刺激可以作为一个有前途的基础，为车载或基础设施系统的发展，旨在防止疏忽驾驶和提高道路安全。

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引用次数: 0

Intelligent defensive driving for autonomous vehicles: Framework, strategy and verification 自动驾驶汽车的智能防御驾驶：框架、策略和验证。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-16 DOI: 10.1016/j.aap.2025.108355

Ting Zhang , Zixuan Wang , Hong Wang, Jun Li

As autonomous driving advances to higher levels, conventional decision-making algorithms for autonomous vehicles (AVs) often inadequately address long-tail issues composed of low-frequency, high-uncertainty, and extreme scenarios, leading to recurrent safety incidents. How to leverage humans’ excellent defensive driving experience refined through expert knowledge to upgrade autonomous driving to intelligent defensive driving, thereby systematically improving driving safety serves as the core objective of this paper. To tackle this challenge, this paper proposes an integrated research scheme for the intelligent defensive driving of AVs. Firstly, an overall framework for intelligent defensive driving of AVs is constructed, which includes a systematic classification method for defensive driving scenarios and a hierarchical design process for defensive driving. Secondly, an online defensive driving monitoring mechanism for AVs is designed based on experience-triggered conditions, and a safety decision-making strategy is developed integrating formalized defensive driving experience. Finally, intelligent defensive driving performance for AVs is verified based on the “perception insufficiency” classification. The simulation results show that the proposed research scheme can utilize the defensive driving trigger mechanism to anticipate potential risks in multiple risk scenarios, improving the average values of the minimum relative braking distance, minimum time-to-collision and advance braking time by 7.49 m, 1.56 s, and 2.84 s, respectively, while significantly reducing the probability of emergency accidents and maintaining system robustness and real-time computational performance.

随着自动驾驶向更高水平发展，传统的自动驾驶决策算法往往无法充分解决由低频、高不确定性和极端情况组成的长尾问题，导致安全事故反复发生。如何利用人类通过专家知识提炼出来的优秀防御性驾驶经验，将自动驾驶升级为智能防御性驾驶，从而系统地提高驾驶安全性是本文的核心目标。针对这一挑战，本文提出了自动驾驶智能防御驾驶的综合研究方案。首先，构建了自动驾驶智能防御驾驶的总体框架，包括系统的防御驾驶场景分类方法和分层次的防御驾驶设计流程；其次，设计了基于经验触发条件的自动驾驶汽车在线防御驾驶监控机制，并结合形式化的防御驾驶经验制定了安全决策策略；最后，基于“感知不足”分类对自动驾驶汽车的智能防御驾驶性能进行验证。仿真结果表明，所提出的研究方案能够利用防御性驾驶触发机制预测多种风险场景下的潜在风险，使最小相对制动距离、最小碰撞时间和提前制动时间的平均值分别提高7.49 m、1.56 s和2.84 s，同时显著降低了紧急事故发生的概率，保持了系统的鲁棒性和实时性。

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引用次数: 0

Impact of the enhanced Fatality Analysis Reporting System on drug detection in fatally injured drivers 加强死亡分析报告系统对检出致命受伤司机药物的影响。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-15 DOI: 10.1016/j.aap.2025.108354

Stanford Chihuri , Guohua Li

The prevalence of drugged driving has been increasing in the United States. In 2018, the Fatality Analysis Reporting System (FARS) enhanced toxicology data collection by expanding the number of reportable nonalcohol drugs beyond the previous limit of three. In this repeated cross-sectional study, we used difference-in-differences analyses, Poisson regression, and joinpoint regression to assess changes in the detection of alcohol and nonalcohol drugs among fatally injured drivers before (2013–2017; n = 68,741) and after (2018–2022; n = 69,764) the enhancement. The prevalence of alcohol remained stable over time, averaging 37 %, with similar values in 2013–2017 (37.1 %) and 2018–2022 (37.9 %). In contrast, the prevalence of any nonalcohol drug increased from 39.9 % to 55.0 % (p < 0.001). The prevalence of marijuana rose from 14.4 % to 26.4 % (p < 0.001), stimulants from 9.3 % to 25.1 % (p < 0.001), and depressants from 9.1 % to 13.7 % (p < 0.001). Between the two periods, the prevalence of ≥ 1 nonalcohol drug increased by 9 %, with marijuana and stimulants showing the largest gains. Drivers who tested positive for both alcohol and marijuana increased from 8.3 % to 11.8 % (p < 0.001). The proportion that tested positive for ≥ 2 nonalcohol drugs rose from 13.6 % to 19.2 % (p < 0.001), and for ≥ 3 drugs from 2.0 % to 5.8 % (p < 0.001). The mean number of nonalcohol drugs detected increased from 1.36 to 1.50 (p < 0.001). In adjusted models, the odds of detecting any nonalcohol drug associated with the enhanced toxicology data collection increased 42 % [adjusted odds ratio (aOR) 1.42, 95 % confidence interval (CI) 1.37, 1.47], with greater increase in detecting ≥ 2 drugs (aOR 1.46, 95 % CI 1.40, 1.52) and ≥ 3 drugs (aOR 3.01, 95 % CI 2.80, 3.24). These findings highlight improved detection of polysubstance use among fatally injured drivers following the adoption of a comprehensive toxicological testing data file by FARS in 2018.

在美国，服药后驾车的现象越来越普遍。2018年，死亡分析报告系统（FARS）通过扩大可报告的非酒精药物的数量，加强了毒理学数据收集，超过了之前的三种限制。在这项重复的横断面研究中，我们使用了差中差分析、泊松回归和连接点回归来评估在增强之前（2013-2017年，n = 68,741）和之后（2018-2022年，n = 69,764）致命受伤司机中酒精和非酒精药物检测的变化。随着时间的推移，酒精的患病率保持稳定，平均为37%，2013-2017年（37.1%）和2018-2022年（37.9%）的数值相似。相比之下，非酒精类药物的患病率从39.9%上升到55.0%

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引用次数: 0

Beyond distraction: unraveling touchscreen effects on driver takeover performance and visual attention dynamics in Level 3 automated driving 超越分心：揭示触屏对3级自动驾驶驾驶员接管性能和视觉注意力动态的影响

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-12 DOI: 10.1016/j.aap.2025.108348

Jiateng Li, Jun Ma

Rapid advances in automated driving technology and the widespread adoption of in-vehicle information systems (IVIS) have led to an increasing prevalence of drivers engaging in non-driving-related tasks (NDRTs) during autonomous operation, thereby introducing potential safety hazards. In this study, we conducted a driving simulator experiment with 30 participants to examine the effects of IVIS NDRTs (i.e., navigation, video, audio, and reading tasks) and takeover time budgets on takeover timing, takeover quality, and visual behavior. Results from linear mixed-effects models indicate that IVIS touchscreen interactions significantly prolonged takeover time and lane change time, increased maximum lateral acceleration, and reduced minimum time-to-collision (TTC), suggesting that drivers adopted aggressive control behaviors during takeovers, which in turn elevated collision risk. Moreover, visual behavior analysis revealed an increased proportion of long glances directed away from the forward roadway and a delayed reallocation of visual attention to key regions (such as mirrors, the road, and the malfunctioning vehicle) following the takeover request. These findings enhance our understanding of human factors in automated driving and provide empirical evidence for optimizing driver-vehicle interaction protocols and improving the safety of riding in conditionally automated driving systems.

自动驾驶技术的快速发展和车载信息系统（IVIS）的广泛采用，导致驾驶员在自动驾驶过程中从事与驾驶无关的任务（NDRTs）的现象越来越普遍，从而带来了潜在的安全隐患。在本研究中，我们对30名参与者进行了驾驶模拟器实验，以检验IVIS NDRTs（即导航、视频、音频和阅读任务）和接管时间预算对接管时间、接管质量和视觉行为的影响。线性混合效应模型结果表明，IVIS触摸屏交互显著延长了接管时间和变道时间，增加了最大横向加速度，降低了最小碰撞时间（TTC），表明驾驶员在接管过程中采取了积极的控制行为，从而增加了碰撞风险。此外，视觉行为分析显示，在接收请求后，人们将目光从前方道路上移开的比例增加了，并且将视觉注意力重新分配到关键区域（如后视镜、道路和故障车辆）的时间延迟了。这些发现增强了我们对自动驾驶中人为因素的理解，并为优化驾驶员-车辆交互协议和提高有条件自动驾驶系统的乘坐安全性提供了经验证据。

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引用次数: 0

Drivers’ dynamic perception of accident risk and safety in underground road merging areas 地下道路合流区驾驶员对事故风险与安全的动态感知

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-11 DOI: 10.1016/j.aap.2025.108347

Peipei Guo , Fangtong Jiao , Zhigang Du , Feng Sun , Xinke Tang

The multi-entry underpass road tunnels is affected by various factors, including long downhill approaches outside the tunnel, monotonous visual environments inside the tunnel, underground merging of the main and secondary roads, and limited sight distance and sight zone. These combined conditions can lead to perception and judgment errors among drivers, significantly increasing the accident risk of rear-end and lateral crashes. This study used video data from a real vehicle test and conducted a subjective perception experiment with a driving simulator. It collected key indicators related to crash accident risk and prevention, including Identify Merging Time (IMT), Perceive Hazard Time (PHT), and Assess Safety Time (AST), to analyze the dynamic perception of risk and safety at the entrances of the main and secondary roads under 6 different speeds. And a Linear Mixed Model (LMM) was applied to evaluate the effect of speed on each indicator. Results showed that IMT decreased with increasing speed for both main and secondary roads, with the main road exhibited the highest Identify Merging Delay Rate (IMDR) at 38.667 %, indicating that drivers traveling at higher speeds struggled to identify the underground merging area in time. The Perceive Hazard Distance (PHD) for both main and secondary roads extended with increasing speed. Compared to the main road, drivers on the secondary road perceived hazards earlier within 38.167 to 46.683 m downstream of the physical gore point. This earlier perception was related to their frequent use of rearview mirrors to assess merging opportunities and the expanded sight zone in the secondary road merging area. Through LMM analysis, secondary road drivers’ PHD is less dependent on speed and is more influenced by the merging process itself. Overall, at higher speeds, reaction time is notably reduced, leading to delayed identification, hazard perception, and safety assessment. Hence, these findings provide valuable references for optimizing underground merging area design and enhancing drivers’ safety perception in multi-entry underpass road tunnels.

多入口下穿式公路隧道受隧道外下坡通道长、隧道内视觉环境单调、主次道路地下合并、视线距离和视域有限等多种因素的影响。这些综合条件可能导致驾驶员的感知和判断错误，大大增加了追尾和侧面碰撞的事故风险。本研究使用了真实车辆测试的视频数据，并在驾驶模拟器上进行了主观感知实验。收集识别合并时间（IMT）、感知危险时间（PHT）和评估安全时间（AST）等与碰撞事故风险和预防相关的关键指标，分析6种不同车速下主次道路入口的动态风险和安全感知。采用线性混合模型（LMM）评价速度对各指标的影响。结果表明：主干道和副干道的IMT均随车速的增加而降低，其中主干道的识别合并延迟率（IMDR）最高，为38.667%，说明高速行驶的驾驶员难以及时识别地下合并区；主次道路的感知危险距离（PHD）随车速的增加而增大。与主干道相比，次要干道驾驶员在物理血点下游38.167 ~ 46.683 m范围内感知危险的时间更早。这种早期感知与他们经常使用后视镜来评估合并机会以及在次要道路合并区域扩大视野有关。通过LMM分析，二级道路驾驶员的PHD受速度的影响较小，受归并过程本身的影响较大。总的来说，在更高的速度下，反应时间明显缩短，导致识别、危险感知和安全评估延迟。研究结果为优化地下合流区设计，提高多入口地下通道隧道驾驶员的安全感知提供了有价值的参考。

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引用次数: 0

Modeling interactive crash avoidance behaviors: A multi-agent state-space transformer-enhanced reinforcement learning framework 交互式避撞行为建模：多智能体状态空间变换增强强化学习框架

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-11 DOI: 10.1016/j.aap.2025.108334

Qingwen Pu , Kun Xie , Hongyu Guo , Yuan Zhu

Understanding vehicle–pedestrian interactions at urban intersections is critical for enhancing traffic safety. This study aims to model the interactive crash avoidance behavior of road users in near-miss scenarios, addressing the complexities of their decision-making process. Utilizing high-resolution trajectory data collected by unmanned aerial vehicles (UAV), this study proposed a multi-agent state-space Transformer enhanced deep deterministic policy gradient (MA-SST-DDPG) framework to model the vehicle–pedestrian interactions in near-miss scenarios. The framework integrates the state-space model for capturing long-term temporal dependencies and Transformers for dynamically prioritizing critical features, enhancing its ability to learn from rare safety–critical scenarios. The MA-SST-DDPG framework effectively learned sequential decision-making over continuous action spaces, effectively prioritizing critical states and capturing dynamic interactions in vehicle–pedestrian near-miss scenarios. Compared to alternative approaches, it demonstrated superior performance in simulating realistic evasive behaviors. Cross-dataset evaluation confirmed the generalizability of the proposed model on external datasets. Additionally, we employed the proposed model to generate vehicle–pedestrian interactions under varying combinations of initial speeds. Results showed that the simulated interactions successfully replicated the dynamics of real-world near-miss events. Higher initial vehicle and pedestrian speeds were linked to increased conflict rates. Moreover, pedestrians were more likely to yield when vehicles traveled faster and pedestrians walked slower, whereas slower vehicles tended to yield to faster-moving pedestrians. The outcomes of this study can facilitate the development of safety-aware simulations that closely mimic interactive crash avoidance behaviors of road users, paving the way for exploring proactive measures to prevent crashes.

了解城市十字路口车辆与行人的相互作用对于提高交通安全至关重要。本研究旨在模拟道路使用者在险情情景下的互动避碰行为，以解决其决策过程的复杂性。利用无人机（UAV）收集的高分辨率轨迹数据，提出了一种多智能体状态空间Transformer增强的深度确定性策略梯度（MA-SST-DDPG）框架，用于模拟近靶场景下的车-行人相互作用。该框架集成了用于捕获长期时间依赖性的状态空间模型和用于动态确定关键特征优先级的transformer，增强了其从罕见的安全关键场景中学习的能力。MA-SST-DDPG框架在连续动作空间中有效地学习了顺序决策，有效地确定了关键状态的优先级，并捕获了车辆与行人擦肩而过场景中的动态交互。与其他方法相比，该方法在模拟现实回避行为方面表现出优越的性能。跨数据集评估证实了该模型在外部数据集上的泛化性。此外，我们采用所提出的模型来生成不同初始速度组合下的车辆-行人相互作用。结果表明，模拟的相互作用成功地复制了现实世界中近靶事件的动态。车辆和行人的初始速度越快，冲突率越高。此外，当车辆行驶更快，行人走得更慢时，行人更有可能让步，而较慢的车辆往往会向快速移动的行人让步。这项研究的结果可以促进安全意识模拟的发展，密切模仿道路使用者的互动碰撞避免行为，为探索预防碰撞的主动措施铺平道路。

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引用次数: 0

Solution to data imbalance and complex interactions in traffic conflict modeling: a hypergraph and generative AI approach 交通冲突建模中数据不平衡和复杂交互的解决方案：超图和生成人工智能方法。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-08 DOI: 10.1016/j.aap.2025.108338

Kaiming Guan, Junyi Zhang, Wei Ye, Ying Jiang

Existing traffic conflict models face challenges in handling minority class samples and capturing dynamic interactions in complex traffic scenarios. These limitations hinder model generalization and real-world applicability. This study employs an enhanced Two-dimensional Time-to-collision (2D-TTC) metric combined with vehicle interaction relationships to predict traffic conflicts of multiple patterns. To address imbalance in conflict and non-conflict events, both undersampling and oversampling techniques are employed, while a generative adversarial network with self-attention layers is leveraged to overcome the shortcomings of oversampling methods. Indeed, this approach proved highly effective, elevating the model’s F1-score from 76.35 % with undersampling alone to 94.21 %. Additionally, several machine learning and deep learning models are compared, with the hypergraph attention network combined with Shapley additive explanations (S-HGAT) demonstrating the strongest learning capability. Furthermore, vehicle speed is identified as the most influential factor associated with traffic conflicts. A comprehensive re-evaluation of feature combinations reveals that the top six features—vehicle speed, the number of vehicles ahead, the standard deviation and the average of vehicle speeds within the traffic flow, distance with the road markings, and peak traffic hour indicators—result in the highest model F1-score of 98.41 % and accuracy of 97.66 %. Finally, the real-world implications of these findings are discussed.

现有的交通冲突模型在处理少数类样本和捕获复杂交通场景下的动态交互方面面临挑战。这些限制阻碍了模型的泛化和现实世界的适用性。本研究采用一种增强的二维碰撞时间（2D-TTC）指标，结合车辆相互作用关系来预测多种模式的交通冲突。为了解决冲突和非冲突事件中的不平衡问题，采用了欠采样和过采样技术，同时利用具有自关注层的生成对抗网络来克服过采样方法的缺点。事实上，这种方法被证明是非常有效的，将模型的f1分数从单欠采样的76.35%提高到94.21%。此外，还比较了几种机器学习和深度学习模型，其中超图注意网络结合Shapley加性解释（S-HGAT）显示出最强的学习能力。此外，车速是影响交通冲突的最主要因素。对特征组合进行综合再评价后发现，车速、前方车辆数量、车流内车速的标准差和平均值、与道路标线的距离、高峰交通小时指标等前6个特征的模型f1得分最高，为98.41%，准确率为97.66%。最后，讨论了这些发现对现实世界的影响。

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引用次数: 0

A cross-scale traffic-communication control framework for improving safety through proactive congestion mitigation in mixed traffic 一个跨尺度交通通信控制框架，通过主动缓解混合交通中的拥堵来提高安全性

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-05 DOI: 10.1016/j.aap.2025.108339

Zhigang Wu , Meng Li , Yanyong Guo , Zhibin Li , Shunchao Wang

Large-scale traffic accidents are often triggered by sudden shockwaves in congested flow, typically caused by unpredictable driving behaviors. The collaboration among connected and autonomous vehicles (CAVs) offer potential to mitigating traffic congestion and accidents, yet it remains vulnerable to failures in vehicle behavior coordination due to unstable long-range communication. To address these issues, this study proposes a Cross-Network Collaboration-based Congestion Mitigation (CNC-CM) framework, which establishes a feedback response mechanism between the traffic system and the communication network. At the communication layer, a distance-to-delay interval backtracking algorithm is developed to optimize long-range hybrid communication routing, ensuring timely and reliable command delivery under varying network conditions. At the traffic control layer, a multi-scale cooperative strategy is designed: a micro-level barrier consensus control restrains disruptive lane-changing by human-driven vehicles (HDVs), while a macro-level delay-corrected cruising control eliminates stop-and-go waves within enclosed congestion clusters. By integrating communication constraints into traffic control decisions, this cross-scale, multi-layer approach proactively dissipates incipient traffic jams before they escalate into safety hazards. Simulation results demonstrate that the proposed control framework enhances driving safety by over 54.11% through completely eliminating traffic congestion, while also significantly improving traffic efficiency, reducing energy consumption, and enhancing communication quality. Notably, the framework maintains robust performance even under low CAV penetration rates, confirming its effectiveness in mixed traffic environments with unpredictable human driving behaviors.

大规模交通事故往往是由拥挤车流中的突发冲击波引发的，通常是由不可预测的驾驶行为引起的。车联网和自动驾驶汽车之间的协作为缓解交通拥堵和事故提供了潜力，但由于远程通信不稳定，它仍然容易受到车辆行为协调失败的影响。为了解决这些问题，本研究提出了一个基于跨网络协作的拥塞缓解（CNC-CM）框架，该框架在交通系统和通信网络之间建立了反馈响应机制。在通信层，提出了一种距离-时延区间回溯算法，对远程混合通信路由进行优化，保证了在不同网络条件下命令的及时可靠传递。在交通控制层，设计了一种多尺度合作策略：微观层面的障碍共识控制抑制了人类驾驶车辆（HDVs）的破坏性变道行为，而宏观层面的延迟校正巡航控制消除了封闭拥堵集群内的走走停停波。通过将通信约束整合到交通控制决策中，这种跨规模、多层的方法可以在初期的交通堵塞升级为安全隐患之前主动消散。仿真结果表明，该控制框架在完全消除交通拥堵的同时，显著提高了交通效率，降低了能耗，提高了通信质量，行车安全性提高了54.11%以上。值得注意的是，即使在低CAV渗透率下，该框架也保持了强大的性能，证实了其在具有不可预测的人类驾驶行为的混合交通环境中的有效性。

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引用次数: 0

Do colored lane markings improve road safety? Causal evidence from Seoul 彩色车道标记能改善道路安全吗？来自首尔的因果证据

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-05 DOI: 10.1016/j.aap.2025.108336

Seungyeon Lee , Eun Hak Lee

Colored lane markings are a recent traffic safety intervention in South Korea, designed to improve driver awareness and visual guidance. This study aims to evaluate their effectiveness in reducing traffic crashes. Specifically, 82 road segments in Seoul where the markings were installed were analyzed by comparing crash trends before and after the intervention using data from 2010 to 2024. To estimate the intervention’s effect, a counterfactual analysis was conducted by constructing a baseline scenario representing crash trends in the absence of the intervention. The causal impact of the colored markings was then identified by comparing this baseline with observed outcomes. The results show that the implementation of colored lane markings led to an average 26.7 % reduction in crash rates at statistically significant sites. To identify where the intervention was most effective, the relationship between surrounding land use and observed safety outcomes was examined. The analysis indicates that the markings were more effective on highways and arterial roads, which tend to have higher speeds and simpler traffic conditions. In contrast, roads in dense urban areas showed limited improvements. This outcome is attributable to complex traffic conditions and high levels of visual and environmental clutter. Taken together, these findings suggest that the intervention is highly effective and provides safety benefits on arterial networks.

彩色车道标记是韩国最近的交通安全干预措施，旨在提高驾驶员的意识和视觉引导。本研究旨在评估它们在减少交通事故方面的有效性。具体而言，对首尔82个安装了标志的路段进行了分析，并利用2010年至2024年的数据，比较了干预前后的碰撞趋势。为了评估干预的效果，通过构建一个基线情景来进行反事实分析，该情景代表了在没有干预的情况下的崩溃趋势。然后通过将基线与观察结果进行比较，确定彩色标记的因果影响。结果表明，在统计上重要的地点，彩色车道标记的实施使事故发生率平均降低了26.7%。为了确定干预措施最有效的地方，研究了周围土地利用与观察到的安全结果之间的关系。分析表明，这些标志在高速公路和主干道上更有效，因为这些道路往往具有更高的速度和更简单的交通条件。相比之下，人口密集城市地区的道路改善有限。这一结果可归因于复杂的交通状况和高度的视觉和环境混乱。综上所述，这些发现表明该干预措施非常有效，并为动脉网络提供了安全益处。

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引用次数: 0

Evaluation of association between observed driving speeds and the occurrence of crashes using naturalistic driving study data 使用自然驾驶研究数据评估观察到的驾驶速度与碰撞发生之间的关系。

IF 6.2 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-12-03 DOI: 10.1016/j.aap.2025.108335

John N. Ivan , Yaohua Zhang , Nalini Ravishanker

The SHRP2 Naturalistic Driving Study (NDS) data were used to investigate association between actual driving speeds before known crashes and at other times. Associations were evaluated for the same driver at a location where a crash occurred and similar locations where crashes did not occur, relative to the speeds of other drivers at those locations. It was found that an increase in the speed differential relative to other drivers at the same location between 6 and 10 s before a crash occurred was significantly associated with a crash occurring. The quantile of the average speed over that five-second period served as a better predictor than the quantile of the maximum speed. Crashes were also more associated with road locations classified as limited access highways, minor arterials, and major collectors. These findings are consistent across different drivers and types of road locations. The best-performing model classified all of the crashes in the dataset perfectly, and less than half of the cases classified as crashes were not crashes. This suggests an ability to identify conditions that are at least 50 percent likely to result in a crash. The results could be used by road agencies to identify observed vehicle speed variations that are likely to result in crashes, as well as by vehicle manufacturers to develop algorithms for identifying high-risk conditions for crashes considering speeds of other vehicles in the vicinity.

SHRP2自然驾驶研究（NDS）数据用于调查已知碰撞前和其他时间的实际驾驶速度之间的关系。研究人员对同一名司机在发生车祸的地点和在类似地点没有发生车祸的情况下，相对于其他司机在这些地点的车速，进行了关联评估。研究发现，在事故发生前6到10秒，与同一地点的其他司机相比，车速差的增加与事故的发生有很大关系。在这五秒钟内，平均速度的分位数比最大速度的分位数更能预测速度。事故也更多地与道路位置相关，这些位置被归类为有限通道高速公路、次要动脉和主要收集器。这些发现在不同的司机和不同类型的道路位置上是一致的。表现最好的模型对数据集中的所有崩溃都进行了完美的分类，被分类为崩溃的情况中只有不到一半不是崩溃。这表明它有能力识别至少有50%可能导致撞车的情况。这些结果可以被道路管理机构用来识别可能导致碰撞的观察到的车辆速度变化，也可以被汽车制造商用来开发算法来识别碰撞的高风险条件，考虑到附近其他车辆的速度。

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引用次数: 0