Non-crash Based Safety Analysis: Innovative Models or Urban Intersections and Highways

基于非碰撞的安全分析：城市交叉口和高速公路的创新模型

• 批准号: RGPIN-2016-06208
• 负责人: Ismail, Karim
• 金额: $ 1.97万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749066
• 关键词: Non; crash; Based; Safety; Analysis

The transportation system is in a continual development to meet every increasing demand for mobility, safety, and increased capacity. This is required along with increasing emphasis on sustainability and resource-efficiency. In the quest to create more reliable models and analysis techniques, researchers are collecting as well as analyzing ever-increasing volumes of data. The proposed research supports the development of two frontiers: [i] creating more reliable models for safety prediction without the reliance on crash data and [ii] creating a more unified framework to analyze crash and non-crash data.The first goal of the proposed research attempts to advance the knowledge on how safety can be predicted without resorting to crash data. This involves the search for clues' in the traffic stream which can reflect safety hazards. More interestingly, if such clues can be obtained from hypothetical or simulated traffic facilities. In building these models, a special focus will be allocated to non-motorized models of travel. The transferability of calibrated models will also be investigated in order to explore the generality and applicability of these models.The second goal of the proposed research attempts to develop a unified framework to model crash and non-crash events for urban intersections as well as freeway acceleration lanes. Modelling of driver, pedestrian, and cyclist choices as they navigate traffic facilities will be attempted. Subsequently, it will be investigated whether such model can be used to predict the incidence of crashes on these facilities.

为了满足人们对机动性、安全性和运力不断增长的需求，交通运输系统正在不断发展。这是在日益强调可持续性和资源效率的同时需要的。为了创造更可靠的模型和分析技术，研究人员正在收集和分析不断增加的数据量。拟议的研究支持两个前沿领域的发展：[i]创建更可靠的安全预测模型，而不依赖于碰撞数据；[ii]创建更统一的框架来分析碰撞和非碰撞数据。提出的研究的第一个目标是试图推进关于如何在不诉诸碰撞数据的情况下预测安全性的知识。这包括在交通流中寻找可以反映安全隐患的线索。更有趣的是，如果这些线索可以从假设或模拟的交通设施中获得。在建立这些模型时，将特别关注非机动的旅行模型。为了探索这些模型的通用性和适用性，还将研究校准模型的可转移性。本研究的第二个目标是建立一个统一的框架来模拟城市交叉路口和高速公路加速车道的碰撞和非碰撞事件。将尝试建立驾驶员、行人和骑自行车者在交通设施导航时的选择模型。随后，将研究该模型是否可以用于预测这些设施的碰撞发生率。