Identifying built environment factors influencing driver yielding behavior at unsignalized intersections: A naturalistic open-source dataset collected in Minnesota

IF 3.9 2区 工程技术 Q1 ERGONOMICS
Tianyi Li , Joshua Klavins , Te Xu , Niaz Mahmud Zafri , Raphael Stern
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引用次数: 0

Abstract

Introduction: Many factors influence the yielding result of driver–pedestrian interactions, including traffic, vehicle, roadway, pedestrian attributes, and more. While researchers have examined the individual influence of these factors on interaction outcomes, there is a noticeable absence of comprehensive, naturalistic studies in current literature, particularly those investigating the impact of the built environment on driver-yielding behavior. Method: To address this gap, our study introduces an extensive open-source dataset, compiled from video data at 18 unsignalized intersections across Minnesota. Documenting more than 3000 interactions, this dataset provides a detailed view of driver–pedestrian interactions and over 50 distinct contextual variables. The data, which covers individual driver–pedestrian interactions and contextual factors, is made publicly available at https://hdl.handle.net/11299/254556. Using logistic regression, we developed a classification model that predicts driver yielding based on the identified variables. Results: Our analysis indicates that vehicle speed, the presence of parking lots, proximity to parks or schools, and the width of major road crossings significantly influence driver yielding at unsignalized intersections. Conclusions: Through our findings and by publishing one of the most comprehensive driver–pedestrian datasets in the United States, our study will support communities across Minnesota and the United States in their ongoing efforts to improve road safety for pedestrians and be helpful for automated vehicle design. Practical Applications: We have compiled a dataset on driver–pedestrian interactions at 18 unsignalized intersections in Minnesota, making it one of the most extensive datasets available in the United States. This dataset can be utilized by researchers and local agencies to enhance intersection safety and walkability. Furthermore, our study proposes recommendations for increasing pedestrian safety at intersections, providing valuable insights that local governments can use as guidance for designing future intersections.
影响人-人交互结果的因素很多,包括交通、车辆、道路、行人属性等。虽然研究人员已经研究了这些因素对相互作用结果的个体影响,但目前文献中明显缺乏全面的、自然的研究,特别是那些研究建筑环境对驾驶员屈服行为影响的研究。方法:为了解决这一差距,我们的研究引入了一个广泛的开源数据集,该数据集来自明尼苏达州18个无信号十字路口的视频数据。该数据集记录了3000多个交互,提供了司机与行人交互的详细视图和50多个不同的上下文变量。这些数据涵盖了司机与行人之间的个人互动和环境因素,可在https://hdl.handle.net/11299/254556上公开获取。使用逻辑回归,我们开发了一个分类模型,该模型基于已识别的变量预测驾驶员产量。结果:我们的分析表明,在无信号交叉口,车速、停车场的存在、靠近公园或学校以及主要道路交叉口的宽度显著影响驾驶员的退让行为。结论:通过我们的研究结果,并通过发布美国最全面的驾驶员-行人数据集之一,我们的研究将支持明尼苏达州和美国各地的社区不断努力改善行人的道路安全,并有助于自动车辆的设计。实际应用:我们编制了一个数据集,关于明尼苏达州18个无信号十字路口的驾驶员-行人相互作用,使其成为美国最广泛的数据集之一。研究人员和地方机构可以利用该数据集来提高交叉口的安全性和步行性。此外,我们的研究还提出了提高十字路口行人安全的建议,为地方政府未来设计十字路口提供了有价值的见解。
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来源期刊
CiteScore
6.40
自引率
4.90%
发文量
174
审稿时长
61 days
期刊介绍: Journal of Safety Research is an interdisciplinary publication that provides for the exchange of ideas and scientific evidence capturing studies through research in all areas of safety and health, including traffic, workplace, home, and community. This forum invites research using rigorous methodologies, encourages translational research, and engages the global scientific community through various partnerships (e.g., this outreach includes highlighting some of the latest findings from the U.S. Centers for Disease Control and Prevention).
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