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

点击翻译按钮获取中文摘要

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.