A multimodal framework for studying mobility and transportation safety in an urban network

研究城市网络中流动性和交通安全的多模式框架

• 批准号: RGPIN-2014-06666
• 负责人: MirandaMoreno, Luis
• 金额: $ 2.48万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636257
• 关键词: multimodal; framework; studying; mobility; transportation

The improvement of urban mobility and traffic safety for all modes of transportation is essential for developing sustainable urban transportation systems. This requires the development of decision-making tools and methods for traffic safety analysis that integrate a wide array of data, such as safety outcomes, built environment, road geometry designs, traffic controls, motorized and non-motorized traffic flows and weather conditions. Moreover, to better understand safety-related problems and solutions, it is necessary to collect and monitor data regarding the spatio-temporal distribution of all the involved variables. This reveals several particular challenges that must be addressed, namely: 1) the need to monitor and collect data related to traffic conditions for all modes in the entire network over time; 2) the need to develop methods to estimate accurate traffic exposure measures and map risk in a network; 3) The need to develop new approaches and models that help in the understanding of the complex relationships between contributing risk factors, exposure, and surrogate/crash safety outcomes. This research program is designed to bring these three streams of research. The overall goal of the research program proposed herein is to address several key issues that arise when transportation agencies conduct integral safety studies for all modes and elements in a network. This research program is an integral component of the long-term vision of my research program at McGill, which is to develop innovative solutions for sustainable urban transport systems by integrating leading-edge research and technologies. The proposed research program will address the challenges listed earlier; more specifically, the objectives fall into the following three groups: i) Developing innovative data collection systems for safety applications, including automatic video-based exposure and surrogate measures, as well as tools combining multiple data sources, such as GPS smartphone devices and automatic counting tools. ii) Improving methods for estimating and mapping traffic intensity for all road users, which includes the development of classification and matching algorithms for factoring counts and extrapolation methods to estimate daily flows at intersections and street sections, iii) Developing advanced modeling frameworks and integral decision-making tools for safety analysis. The long term objectives of this research are the development of automatic multimodal traffic data collection tools and measures that bridge classical approaches with surrogate methods, and improve the understanding of the mechanics of and factors leading to crashes. In the long-term, this program also aims at improving the accuracy of current methods to estimate traffic volumes for all modes in different built environments and road facilities as well as developing world-standard safety models and tools for research and practical applications.This research is expected to generate a significant body of new knowledge on traffic data collection and monitoring methods, as well as risk modeling using new safety methods based on crash and behavioral data. Macro and micro level modeling tools will be used to incorporate a multimodal safety approach in the decision making process for an optimal allocation of resources. This research program is also expected to significantly contribute to the identification of the best design practices and treatments to improve urban transportation safety and multimodal mobility.

改善所有交通方式的城市流动性和交通安全对于发展可持续的城市交通系统至关重要。这需要开发用于交通安全分析的决策工具和方法，整合广泛的数据，例如安全结果、建筑环境、道路几何设计、交通控制、机动和非机动交通流量以及天气状况。此外，为了更好地了解安全相关问题和解决方案，有必要收集和监测所有涉及变量的时空分布数据。这揭示了必须解决的几个特殊挑战，即：1）需要随着时间的推移监控和收集与整个网络中所有模式的流量状况相关的数据； 2）需要开发方法来估计准确的流量暴露测量并绘制网络中的风险图； 3）需要开发新的方法和模型，帮助理解影响风险因素、暴露和替代/碰撞安全结果之间的复杂关系。该研究计划旨在带来这三个研究领域。本文提出的研究计划的总体目标是解决运输机构对网络中的所有模式和要素进行整体安全研究时出现的几个关键问题。该研究项目是我在麦吉尔研究项目长期愿景的一个组成部分，该项目是通过整合前沿研究和技术来开发可持续城市交通系统的创新解决方案。拟议的研究计划将解决前面列出的挑战；更具体地说，目标分为以下三组： i) 开发用于安全应用的创新数据收集系统，包括基于视频的自动曝光和替代措施，以及结合多个数据源的工具，例如 GPS 智能手机设备和自动计数工具。 ii) 改进估计和绘制所有道路使用者的交通强度的方法，其中包括开发用于因子计数的分类和匹配算法以及估计交叉口和街道路段每日流量的外推方法，iii) 开发先进的建模框架和整体决策制作安全分析工具。这项研究的长期目标是开发自动多模式交通数据收集工具和措施，将经典方法与替代方法联系起来，并提高对导致碰撞的机制和因素的理解。从长远来看，该计划还旨在提高当前方法的准确性，以估计不同建筑环境和道路设施中所有模式的交通量，并开发用于研究和实际应用的世界标准安全模型和工具。预计将产生大量关于交通数据收集和监控方法的新知识，以及使用基于碰撞和行为数据的新安全方法进行风险建模。将使用宏观和微观建模工具将多模式安全方法纳入决策过程，以实现资源的优化分配。该研究项目预计还将极大地有助于确定最佳设计实践和处理方法，以改善城市交通安全和多式联运。