Accident; analysis and prevention最新文献_第5页

Exploring the impact of built environment on traffic risk perception of school-aged children: A case study of old residential neighborhoods in Changsha City, Hunan Province, China 建筑环境对学龄儿童交通风险感知的影响——以湖南省长沙市老旧住区为例

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-17 DOI: 10.1016/j.aap.2025.107920

Fan Yang , Jiale Li , Chengzhi Zhang , Fangrong Chang , Suwen Xiong

The built environment of old residential neighborhoods poses heightened perceived traffic risks for school-aged children due to their limited cognitive ability to assess hazards, underdeveloped understanding of traffic patterns, and inadequate infrastructure in these areas. However, studies on the impacts of neighborhood environments on children’s traffic risk perceptions remain limited. This study aims to reveal the influence of individual attributes, as well as macro- and micro-level environmental factors, on children’s risk perception using a hierarchical ordered logit model with random effects. Data were collected through a questionnaire survey of 404 school-aged children across fifty old residential neighborhoods in Changsha, China. The results indicated that girls generally perceive higher levels of traffic risk than boys. At the macro-level, low population density and high activity facilities coverage were associated with reduced risk perceptions among children. At the micro-level, obstructed views of intersections, curbside parking, and road damage were found to increase risk perceptions among children. Additionally, random effects related to gender, intersection type, intersection visibility, curbside parking, and sidewalk availability suggested the influence of unobserved factors. The findings underscore the need for targeted interventions, such as improving intersection visibility, managing curbside parking, and repairing road damage, to mitigate traffic risks. Policymakers and urban designers should focus on these aspects to enhance safety and create child-friendly residential environments.

由于学龄儿童对危险评估的认知能力有限，对交通模式的理解不充分，以及这些地区基础设施的不足，旧住宅区的建成环境给学龄儿童带来了更高的交通风险感知。然而，关于邻里环境对儿童交通风险感知影响的研究仍然有限。本研究旨在利用具有随机效应的分层有序logit模型揭示个体属性以及宏观和微观环境因素对儿童风险感知的影响。通过对长沙市50个老旧小区404名学龄儿童进行问卷调查，收集数据。结果表明，女孩普遍认为交通风险高于男孩。在宏观层面上，低人口密度和高活动设施覆盖率与儿童风险认知降低有关。在微观层面上，十字路口遮挡的视野、路边停车和道路损坏都增加了儿童的风险认知。此外，与性别、交叉口类型、交叉口能见度、路边停车和人行道可用性相关的随机效应表明未观察因素的影响。研究结果强调了有针对性的干预措施的必要性，例如提高交叉路口的能见度，管理路边停车，修复道路损坏，以减轻交通风险。政策制定者和城市设计师应该关注这些方面，以提高安全性，创造儿童友好的居住环境。

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引用次数: 0

Innovative prediction and causal analysis of accident vehicle towing probability using advanced gradient boosting techniques on extensive road traffic scene data 利用先进的梯度增强技术对大量道路交通场景数据进行事故车辆拖拽概率的创新预测与原因分析。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-13 DOI: 10.1016/j.aap.2024.107909

Ronghui Zhang , Yang Liu , Zihan Wang , Junzhou Chen , Qiang Zeng , Lai Zheng , Hui Zhang , Yulong Pei

Accurate prediction and causal analysis of road crashes are crucial for improving road safety. One critical indicator of road crash severity is whether the involved vehicles require towing. Despite its importance, limited research has utilized this factor for predicting vehicle towing probability and analyzing its causal factors. This study addresses this gap by predicting the probability of vehicle towing in road crashes based on road scene features and identifying key causal factors. Utilizing the Transportation Injury Mapping System (TIMS) dataset from California, USA, encompassing 12 years, 14 relevant features, and over 2 million road crash records, research team developed a prediction model using advanced gradient boosting techniques. Our model outperforms Random Forest, GBDT, and XGBoost in predictive accuracy. Employing the Shapley Additive Explanation (SHAP) method, researchers elucidate seven key factors influencing towing necessity. These findings introduce a novel predictive approach and offer valuable insights for road crash risk assessment and road safety planning.

道路交通事故的准确预测和原因分析对提高道路安全至关重要。道路交通事故严重程度的一个关键指标是相关车辆是否需要拖拽。尽管它很重要，但利用该因子预测车辆拖拽概率和分析其原因的研究还很有限。本研究通过基于道路场景特征预测道路碰撞中车辆牵引的概率并识别关键原因来解决这一差距。利用来自美国加利福尼亚州的交通伤害地图系统（TIMS）数据集，包括12年，14个相关特征和超过200万的道路碰撞记录，研究小组利用先进的梯度增强技术开发了一个预测模型。我们的模型在预测精度上优于随机森林、GBDT和XGBoost。采用Shapley加性解释（SHAP）方法，阐明了影响拖曳必要性的七个关键因素。这些发现提出了一种新的预测方法，并为道路碰撞风险评估和道路安全规划提供了有价值的见解。

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引用次数: 0

An integrative approach to generating explainable safety assessment scenarios for autonomous vehicles based on Vision Transformer and SHAP 基于Vision Transformer和SHAP的自动驾驶汽车可解释安全评估场景综合生成方法。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-13 DOI: 10.1016/j.aap.2024.107902

Minhee Kang , Keeyeon Hwang , Young Yoon

Automated Vehicles (AVs) are on the cusp of commercialization, prompting global governments to organize the forthcoming mobility phase. However, the advancement of technology alone cannot guarantee the successful commercialization of AVs without insights into the accidents on the read roads where Human-driven Vehicles (HV) coexist. To address such an issue, The New Car Assessment Program (NCAP) is currently in progress, and scenario-based approaches have been spotlighted. Scenario approaches offer a unique advantage by evaluating AV driving safety through carefully designed scenarios that reflect various real-world situations. While most scenario studies favor the data-driven approach, the studies have several shortcomings, including perspectives of data, AI models, and scenario standards. Hence, we propose a holistic framework for generating functional, logical, and concrete scenarios. The framework composes explainable scenarios (X-Scenarios) based on real-driving LiDAR data, and visual trend interpretation using eXplainable AI (XAI). The framework consists of four components as follows: (1) voxelization of LiDAR PCD and extraction of kinematic features; (2) classification of critical situations and generation of attention maps using visual XAI and Vision Transformer (ViT) to generate range values of elements in logical scenarios; (3) analysis of the importance and correlations among input data features using SHapley Additive exPlanations (SHAP) for selecting scenarios based on the most relevant criteria; and (4) composition of AV safety assessment scenarios. X-scenarios generated from our framework involve the parameters of ego vehicles and surrounding objects on the highways and urban roads. With our framework highly trustworthy AV safety assessment scenarios can be created. This novel work provides an integrated solution to generate trustworthy scenarios for AV safety assessment by explaining the scenario selection process.

自动驾驶汽车（AVs）正处于商业化的风口浪尖，促使全球政府组织即将到来的移动阶段。然而，如果不深入了解人类驾驶汽车（HV）共存的道路上发生的事故，仅靠技术的进步并不能保证自动驾驶汽车的成功商业化。为了解决这一问题，“新车评估计划”（NCAP）正在进行中，基于场景的方法备受关注。通过精心设计反映各种现实情况的场景来评估自动驾驶汽车的安全性，这种方法具有独特的优势。虽然大多数情景研究倾向于数据驱动的方法，但这些研究存在一些缺点，包括数据视角、人工智能模型和情景标准。因此，我们提出了一个整体框架来生成功能、逻辑和具体的场景。该框架由基于真实驾驶激光雷达数据的可解释场景（x - scenario）和使用可解释人工智能（XAI）的视觉趋势解释组成。该框架由以下四个部分组成：(1)激光雷达PCD体素化和运动特征提取；(2)利用visual XAI和visual Transformer （ViT）生成逻辑场景中元素的范围值，对关键情景进行分类并生成注意图；(3)利用SHapley加性解释（SHAP）分析输入数据特征之间的重要性和相关性，根据最相关的标准选择场景；(4)自动驾驶汽车安全评估方案的组成。从我们的框架中生成的x场景涉及高速公路和城市道路上的自我车辆和周围物体的参数。利用我们的框架，可以创建高度可信的自动驾驶安全评估场景。这项新颖的工作提供了一个集成的解决方案，通过解释场景选择过程来生成可信赖的自动驾驶安全评估场景。

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引用次数: 0

Partially temporally constrained modeling of speeding crash-injury severities on freeways and non-freeways before, during, and after the stay-at-home order 在居家令之前、期间和之后高速公路和非高速公路上超速碰撞伤害严重程度的部分时间约束建模。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-09 DOI: 10.1016/j.aap.2025.107917

Li Song , Shijie Li , Qiming Yang , Bing Liu , Nengchao Lyu , Wei David Fan

Speeding crashes remain high injury severities after the stay-at-home order in California, highlighting a need for further investigation into the fundamental cause of this increment. To systematically explore the temporal impacts of the stay-at-home order on speeding behaviors and the corresponding crash-injury outcomes, this study utilizes California-reported single-vehicle speeding crashes on freeways (access-controlled) and non-freeways (non-access-controlled) before, during, and after the order. Significant injury factors and in-depth heterogeneity across observations are identified by random parameter logit models with heterogeneity in means and variances. Without segmenting the data by periods, the partially temporally constrained approach is employed to statistically determine varying and stabilized parameters over time through the whole dataset. Different likelihood ratio tests reveal significant temporal instabilities and stabilities of factors between two roadways and three periods. The potential impacts of observation selection issues on the marginal effect calculations of the partially constrained models are also systematically investigated. Significant variations in the probability of severe injury rate per week after the order are also found based on the Mann-Whitney U tests. The hysteretic effects of the order on the crash frequency and severity are observed on both freeways and non-freeways. Overall, seven variables are found to have stable effects, while fifteen variables exhibit unstable effects over time. Significant temporal variations in driver behaviors, including driving under the influence, cell phone usage, hit-and-run, failure to use seat belt, entering or leaving the ramp, and reaction to previous collisions, are observed before, during, or after the order. Specific countermeasures and effects of heterogeneity in means and variances are also discussed. These findings provide insights into understanding the temporal impacts of the stay-at-home order on injury severities, which are valuable to decision-makers and researchers for future order practice, restriction improvement, and complementary policy development.

在加州颁布了“居家令”之后，超速撞车事故造成的伤害严重程度仍然很高，这凸显了对这一增长的根本原因进行进一步调查的必要性。为了系统地探索“居家令”对超速行为和相应的碰撞伤害结果的时间影响，本研究利用了加州报告的“居家令”之前、期间和之后在高速公路（通道控制）和非高速公路（非通道控制）上发生的单车辆超速事故。通过均值和方差异质性的随机参数logit模型来识别显著的损伤因素和深度异质性。在不按周期分割数据的情况下，采用部分时间约束方法在整个数据集中统计地确定随时间变化和稳定的参数。不同的似然比检验表明，各因素在两条道路和三个时期之间具有显著的时间不稳定性和稳定性。系统地研究了观测选择问题对部分约束模型边际效应计算的潜在影响。根据曼-惠特尼U测试，还发现了订单后每周严重受伤率的显著变化。在高速公路和非高速公路上均观察到顺序对碰撞频率和严重程度的滞后效应。总体而言，七个变量具有稳定的影响，而15个变量随着时间的推移表现出不稳定的影响。驾驶员行为的显著时间变化，包括酒后驾驶、使用手机、肇事逃逸、未系安全带、进入或离开坡道，以及对先前碰撞的反应，在命令发布之前、期间或之后都可以观察到。本文还讨论了均值和方差异质性的具体对策和影响。这些发现为理解居家令对伤害严重程度的时间影响提供了见解，对决策者和研究人员未来的禁令实践、限制改进和配套政策制定有价值。

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引用次数: 0

Evaluating crash severity at highway-rail grade crossings using an analytic hierarchy process-based hazard index model 基于层次分析法的公路网平交道口碰撞严重程度评价。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-09 DOI: 10.1016/j.aap.2025.107918

Amin Keramati , Pan Lu , Afrooz Moatari-Kazerouni

Due to the substantial mass disparity between trains and highway vehicles, crashes at Highway-Rail Grade Crossings (HRGCs) are often severe. Therefore, it is essential to develop systematic frameworks for allocating federal and state funds to improve safety at the highest-risk grade crossings. Common techniques for hazard prioritization at HRGCs include the hazard index and the collision prediction formula. A few research projects and state departments of transportation (DOTs) have employed hybrid models that integrate crash hazard indices with prediction models to create comprehensive safety decision-making frameworks. In addition, ranking grade crossings based on their forecasted crash severity likelihood remains largely unexplored, partly due to the complexity of integrating crash severity outputs with hazard indices. This research introduces a new mixed hazard ranking model, the Analytic Hierarchy Process Hazard Index (AHP-HI), which serves as a decision-making tool for ranking grade crossings based on their potential for crash severity. The AHP-HI model combines the analytic hierarchy process (AHP) and the competing risk model (CRM), a prediction model that estimates the likelihood of crash severity for crossings. Risk analysis using the AHP-HI model categorizes public grade crossings in North Dakota into four risk levels, with 4.73% of the crossings identified as high risk.

由于火车和公路车辆之间的巨大质量差距，公路-铁路平交道口（HRGCs）的碰撞通常很严重。因此，有必要制定系统的框架来分配联邦和州的资金，以改善风险最高的平交道口的安全。灾害优先排序的常用技术包括灾害指数和碰撞预测公式。一些研究项目和国家交通部门（DOTs）采用混合模型，将碰撞危险指数与预测模型相结合，建立综合安全决策框架。此外，基于预测的碰撞严重程度可能性对平交道口进行排名在很大程度上仍未被探索，部分原因是将碰撞严重程度输出与危险指数相结合的复杂性。本文提出了一种新的混合危险排序模型——层次分析法危险指数（AHP-HI），该模型可作为一种基于碰撞严重程度对平交道口进行排序的决策工具。AHP- hi模型结合了层次分析法（AHP）和竞争风险模型（CRM），后者是一种估计交叉碰撞严重程度可能性的预测模型。使用AHP-HI模型进行风险分析，将北达科他州的公共平交道口分为四个风险级别，其中4.73%的平交道口被确定为高风险。

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引用次数: 0

Exploring the Mechanism for Increased Risk in Freeway Tunnel Approach Zones: A Perspective on Temporal-spatial Evolution of Driving Predictions, Tasks, and Behaviors 探索高速公路隧道引线区风险增加的机制：驾驶预测、任务和行为的时空演化视角。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-08 DOI: 10.1016/j.aap.2024.107914

Runzhao Bei , Zhigang Du , Nengchao Lyu , Liang Yu , Yongzheng Yang

Freeway tunnel approach zones, situated outside the tunnel, do not undergo the same sudden changes in luminous environment and visual references that entrance zones experience. Despite this, accident data indicates that approach zones present similar safety risks to entrance zones, both of which are significantly higher than other tunnel sections. The reasons for the heightened risks in approach zones remain unclear in existing research. To address this knowledge gap, this study conducted real vehicle tests and subjective perception experiments. The Task Analysis of Driving Scenarios (TADS) was employed to analyze driving behavior patterns and develop a set of evaluation metrics, including four key driving behavior nodes (P1_SGD, P2_EF, P3_FF, P4_SAD), safety and efficacy indices for active deceleration behaviors (I1_ADS, I2_ADE), and two indicators for understanding anomalous behaviors (SR, AOI_PFN). By skillfully selecting scenarios to control variables, this research examined how limited visibility in tunnel approach zones and spatial intervisibility tunnels contribute to safety risks in these zones. Additionally, the Predictive Processing Model (PPM) was used to elucidate the temporal and spatial evolution of driving predictions, tasks, and behaviors under normal conditions. The findings reveal that, although heavy driving tasks cannot be avoided, under normal conditions, predictions develop gradually with minimal prediction errors, enabling effective navigation. However, limited visibility in tunnel approach zones and spatially intervisible tunnels lead to inaccuracies and deviations in predictions, resulting in significant prediction errors as drivers approach the tunnel. This causes more aggressive driving behaviors, disrupting the delicate balance of predictions and tasks.

高速公路隧道入口区域位于隧道外部，不像入口区域那样经历光照环境和视觉参考的突然变化。尽管如此，事故数据表明，引路区与入口区存在相似的安全风险，两者都明显高于其他隧道段。在现有的研究中，接近区风险增加的原因尚不清楚。为了解决这一知识差距，本研究进行了真实车辆测试和主观感知实验。采用驾驶场景任务分析（TADS）对驾驶行为模式进行分析，并建立了一套评价指标，包括4个关键驾驶行为节点（P1_SGD、P2_EF、P3_FF、P4_SAD）、主动减速行为的安全性和有效性指标（I1_ADS、I2_ADE）以及理解异常行为的2个指标（SR、AOI_PFN）。通过巧妙地选择场景来控制变量，本研究考察了隧道引道区域的有限能见度和隧道的空间互视性对这些区域的安全风险的影响。此外，预测处理模型（PPM）用于阐明在正常条件下驱动预测、任务和行为的时空演变。研究结果表明，尽管繁重的驾驶任务无法避免，但在正常情况下，预测会逐渐发展，预测误差最小，从而实现有效的导航。然而，由于隧道接近区能见度有限，隧道在空间上不可见，导致预测不准确和偏差，导致司机接近隧道时的预测误差很大。这会导致更激进的驾驶行为，破坏预测和任务之间的微妙平衡。

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引用次数: 0

A cognitive digital twin approach to improving driver compliance and accident prevention 一个认知数字孪生方法，以提高驾驶员合规性和事故预防。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-07 DOI: 10.1016/j.aap.2024.107913

Yi Gu , Shuhang Li , Ji Qi , Bangzheng Fu , Renzhi Tang , Lifeng Yang , Sen Tian , Zhihao Jiang

Advanced Driver Assistance Systems (ADAS) are crucial for enhancing driving safety by alerting drivers to unrecognized risks. However, traditional ADAS often fail to account for individual decision-making processes, including drivers’ perceptions of the environment and personal driving styles, which can lead to non-compliance with the provided assistance. This paper introduces a novel Cognitive-Digital-Twin-based Driving Assistance System (CDAS), leveraging a personalized driving decision model that dynamically updates based on the driver’s control and observation actions. By incorporating these individual behaviors, CDAS can tailor its assistance options to predict and adapt to the driver’s responses across various scenarios, ensuring both the necessity and safety of its interventions. Through two comprehensive experimental validations, we demonstrate that the cognitive digital twin (CDT) closely aligns with actual driver observation behaviors. By incorporating additional driver observation actions – an input not readily leveraged by data-driven methods without large annotated datasets – the CDT also achieves superior lane-changing predictions compared to deep learning classifiers relying solely on environmental states. Furthermore, CDAS significantly outperforms traditional ADAS in terms of risk reduction and user acceptance, showcasing its potential to enhance driving safety and adaptability effectively. These findings suggest that CDAS represents a substantial advancement towards more personalized and effective driving assistance.

先进驾驶辅助系统（ADAS）通过提醒驾驶员注意未被识别的风险，对提高驾驶安全至关重要。然而，传统的ADAS往往不能考虑到个人的决策过程，包括司机对环境的看法和个人驾驶风格，这可能导致不遵守所提供的帮助。本文介绍了一种基于认知-数字孪生的新型驾驶辅助系统（CDAS），该系统利用基于驾驶员控制和观察行为动态更新的个性化驾驶决策模型。通过整合这些个体行为，CDAS可以定制其辅助方案，以预测和适应驾驶员在各种情况下的反应，确保其干预的必要性和安全性。通过两个综合实验验证，我们证明了认知数字孪生（CDT）与实际驾驶员观察行为密切相关。与仅依赖环境状态的深度学习分类器相比，通过合并额外的驾驶员观察动作（没有大型注释数据集的数据驱动方法不容易利用的输入），CDT还实现了更好的变道预测。此外，在降低风险和用户接受度方面，CDAS明显优于传统ADAS，显示了其有效提高驾驶安全性和适应性的潜力。这些发现表明，CDAS代表着朝着更个性化和更有效的驾驶辅助迈出了实质性的一步。

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引用次数: 0

Quantifying spatial inequities in traffic injury rates through the integration of urban road network measures and social vulnerability

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-06 DOI: 10.1016/j.aap.2025.107916

Pengfei Cui , Mohamed Abdel-Aty , Xiaobao Yang , Chenzhu Wang , Yali Yuan

Mitigating traffic injury rate plays an essential role in sustainable urban development and is closely related to public health and human well-being. The inequity of traffic injury rate undermines equitable access to transportation infrastructure and poses a significant threat to the safety of residents during their commutes. Although previous studies have examined the association between socio-demographic characteristics and regional traffic crash risk, they seldom consider the spatial heterogeneity of the traffic injury rate inequity especially for the vulnerable groups. This study tackles three main challenges in quantifying spatial inequity in traffic injury rate, including identifying appropriate spatial units for macro-level crash modelling, integrating network topology measures, and examining the inequities suffered by vulnerable group. The global-scale spatial lag model (SLM) and local-scale geographically weighted regression (GWR) were employed at the census tract, Voronoi diagram and grid cell levels in New York City. The results highlight significant spatial variations in social vulnerability, showing that vulnerable indicators of housing cost burden, older age, minority, and those living in mobile homes are linked to increased traffic injury rate, especially in urban core regions. Furthermore, network topology measures indicates that increased network complexity and the buildings density would increase traffic injury rate. The traffic injury rate inequity level of each region was evaluated to identify areas with significant inequities for prioritization in future traffic planning. Recommendations like subsidized traffic insurance rates, enhanced public transportation services, and educational campaigns tailored for vulnerable groups are proposed. By prioritizing the needs of vulnerable groups and addressing the structural factors contributing to traffic injuries, policymakers can create safer and more equitable urban environment.

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引用次数: 0

Spatiotemporal multi-feature fusion vehicle trajectory anomaly detection for intelligent transportation: An improved method combining autoencoders and dynamic Bayesian networks 面向智能交通的时空多特征融合车辆轨迹异常检测：一种结合自编码器和动态贝叶斯网络的改进方法。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-03 DOI: 10.1016/j.aap.2024.107911

Mingqi Qiu , Shuhua Mao , Jiangbin Zhu , Yingjie Yang

With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection. Autoencoders, as powerful unsupervised learning tools, are used for feature extraction and fusion, allowing for a more comprehensive understanding of vehicle behavior, which is essential for identifying anomalies. The Mahalanobis distance-improved dynamic Bayesian network further enhances the model’s detection accuracy and robustness for time series data, improving the efficiency of large-scale data processing and significantly enhancing the ability to fuse and analyze spatiotemporal information. The primary motivation of this research is to improve the detection capabilities of intelligent transportation systems for vehicle trajectory anomalies, thereby strengthening traffic safety. Experimental verification shows that the proposed combined model performs excellently, with significant improvements in detection accuracy. This research not only enhances existing anomaly detection technologies but also provides strong technical support for future intelligent transportation systems, ultimately contributing to overall road safety and reducing traffic accident rates. Additionally, the practical implications include reducing traffic congestion and environmental impacts, making urban transportation systems more efficient and sustainable.

随着智能交通系统的不断发展，交通安全已成为社会关注的焦点，而车辆轨迹异常检测技术已成为保障交通安全的重要手段。然而，目前的技术在处理时空数据和多特征融合方面面临着重大挑战，包括大数据处理方面的困难，在这些方面还有改进的空间。为了解决这些问题，本文提出了一种结合自编码器、马氏距离和动态贝叶斯网络进行异常检测的新方法。自动编码器作为强大的无监督学习工具，可用于特征提取和融合，从而更全面地了解车辆行为，这对于识别异常至关重要。Mahalanobis距离改进动态贝叶斯网络进一步提高了模型对时间序列数据的检测精度和鲁棒性，提高了大规模数据处理的效率，显著增强了融合和分析时空信息的能力。本研究的主要目的是提高智能交通系统对车辆轨迹异常的检测能力，从而加强交通安全。实验验证表明，该组合模型性能优异，检测精度显著提高。该研究不仅增强了现有的异常检测技术，而且为未来的智能交通系统提供了强有力的技术支持，最终有助于提高整体道路安全，降低交通事故率。此外，实际意义还包括减少交通拥堵和环境影响，使城市交通系统更加高效和可持续。

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引用次数: 0

Unravelling situational awareness of multi-tasking pedestrians through average gaze fixation durations: An accelerated failure time modelling approach 通过平均注视时间揭示多任务行人的态势感知：一种加速故障时间建模方法。

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-01-03 DOI: 10.1016/j.aap.2024.107912

Kudurupaka Vamshi Krishna, Pushpa Choudhary

Pedestrians use visual cues (i.e., gaze) to communicate with the other road users, and visual attention towards the surrounding environment is essential to be situationally aware and avoid oncoming conflicts. However, multi-tasking activities compromise visual attention behaviour. Average Fixation Duration (AFD) was captured in six Areas of Interest (AOI) when engaged in activities like texting, talking, listening to music (LM) and gazing at billboards (GBB) while crossing the road. Quantification of situational awareness is accomplished using Weibull Accelerated Failure Time (AFT) model with AFD as a duration variable. This approach helps to understand ongoing cognitive attention required for the user to process the information conveyed by the AOI. The survival rate obtained from Weibull AFT model is defined as the probability of continuing gaze fixation on an AOI at a given time instance. The study demonstrated that the continuation of gaze fixation increased greatly when texting compared to other multi-tasking activities, which was attributed to a decrease in situational awareness. Talking, LM and GBB-involved pedestrians shifted their gaze to another AOI within a maximum of 300 ms, except for vehicle AOI. The LM activity, perceived as less task-intensive and less risky, compensated for their gaze fixation behaviour by spending less time on different AOIs. In addition, billboards near pedestrian crossing locations impact gaze fixation behaviour similar to talking on the phone. The study suggested mitigative policies and strategies to curb distracted walking. Additionally, the aim is to design human–computer interaction-based incident warning systems for real-world situations using augmented reality glasses.

行人使用视觉线索（即凝视）与其他道路使用者交流，对周围环境的视觉关注对于了解情况和避免迎面而来的冲突至关重要。然而，多任务活动会损害视觉注意行为。平均注视持续时间（AFD）被记录在6个兴趣区域（AOI）中，包括在过马路时发短信、说话、听音乐（LM）和盯着广告牌（GBB）。采用威布尔加速故障时间（AFT）模型，以AFD为持续时间变量，实现态势感知的量化。这种方法有助于理解用户处理AOI传达的信息所需的持续认知注意力。由Weibull AFT模型得到的存活率定义为在给定时间实例下，注视对象持续注视一个AOI的概率。研究表明，与其他多任务活动相比，发短信时凝视的持续时间大大增加，这归因于情境意识的下降。除了车辆AOI外，LM和gbb涉及的行人在最多300 ms内将目光转移到另一个AOI。LM活动被认为是较低的任务强度和较低的风险，通过在不同的aoi上花费较少的时间来补偿他们的凝视固定行为。此外，人行横道附近的广告牌会影响人们的注视行为，类似于打电话。该研究建议采取缓解政策和策略来遏制走路分心。此外，目标是使用增强现实眼镜为现实世界的情况设计基于人机交互的事件预警系统。

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