Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision

IF 5.7 1区 工程技术 Q1 ERGONOMICS Accident; analysis and prevention Pub Date : 2024-11-23 DOI:10.1016/j.aap.2024.107851
Han Yue
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Abstract

Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and deep learning techniques provides a cost-effective alternative for extracting streetscape features. However, prior studies often rely solely on semantic segmentation, overlooking distinctions in feature shapes and contours. This study addresses these limitations by combining semantic segmentation and object detection networks to comprehensively measure streetscape features from Baidu SVIs. Semantic segmentation identifies pixel-level proportions of features such as roads, sidewalks, buildings, fences, trees, and grass, while object detection captures discrete elements like vehicles, pedestrians, and traffic lights. Zero-inflated negative binomial regression models are employed to analyze the impact of these features on three crash types: vehicle-vehicle (VCV), vehicle–pedestrian (VCP), and single-vehicle crashes (SVC). Results show that incorporating streetscape features from combined deep learning methods significantly improves crash prediction. Vehicles have a significant impact on VCV and SVC crashes, whereas pedestrians predominantly affect VCP crashes. Road surfaces, sidewalks, and plants are associated with increased crash risks, while buildings and trees correlate with reduced vehicle crash frequencies. This study highlights the advantages of integrating semantic segmentation and object detection for streetscape analysis and underscores the critical role of environmental characteristics in road traffic crashes. The findings provide actionable insights for urban planning and traffic safety strategies.
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利用街景图像和计算机视觉研究与道路交通事故相关的街景环境特征。
研究街景特征与道路交通事故之间的关系对于提高道路安全至关重要。传统的实地调查往往效率低下,而且缺乏全面的空间覆盖。利用街景图像(SVI)和深度学习技术为提取街景特征提供了一种具有成本效益的替代方法。然而,之前的研究往往只依赖于语义分割,忽略了特征形状和轮廓的区别。本研究结合语义分割和物体检测网络,从百度 SVI 中全面测量街景特征,从而解决了这些局限性。语义分割可识别道路、人行道、建筑物、围栏、树木和草地等特征的像素级比例,而物体检测则可捕捉车辆、行人和交通灯等离散元素。采用零膨胀负二项回归模型来分析这些特征对三种碰撞类型的影响:车辆-车辆(VCV)、车辆-行人(VCP)和单车碰撞(SVC)。结果表明,结合深度学习方法的街景特征可显著改善碰撞预测。车辆对 VCV 和 SVC 碰撞事故有重大影响,而行人则主要影响 VCP 碰撞事故。路面、人行道和植物与碰撞风险增加有关,而建筑物和树木则与车辆碰撞频率降低有关。这项研究凸显了将语义分割和物体检测整合到街景分析中的优势,并强调了环境特征在道路交通事故中的关键作用。研究结果为城市规划和交通安全战略提供了可行的见解。
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来源期刊
CiteScore
11.90
自引率
16.90%
发文量
264
审稿时长
48 days
期刊介绍: Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.
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