Integrating Non-Motorist Facility Data into Comprehensive Road Safety Assessment

Project Description

This project aims to enhance pedestrian and bicyclist safety understanding, bolster educational and professional capacities, and facilitate the practical implementation of computer vision techniques in transportation planning and engineering, ultimately leading to safer transportation systems for pedestrians and cyclists. The project will address challenges in gathering pedestrian facilities data and assessing safety concerns for pedestrians and bicyclists, encompassing a comprehensive process involving literature review, case study design, data collection and preparation, model development and validation, result analysis, and recommendation formulation.

Through innovative approaches utilizing satellite images and image processing techniques such as spatial analytics and deep learning models, the project intends to extract crucial information about pedestrian and bicyclist facilities and nighttime streetlight conditions. By leveraging deep neural networks and statistical analysis, the project aims to compare longitudinal datasets, predict injury risks, identify high-risk areas, and unravel potential risk factors and relationships contributing to pedestrian and bicycle accidents, thereby informing evidence-based decisions and interventions. The outcomes of this project will be disseminated through technical reports and academic discussion, contributing to the understanding of non-motorist safety, encouraging further research, and providing educational resources for transportation programs.