Developing an Intelligent Transportation Management Center (ITMC) with a Safety Evaluation Focus for Smart Cities

In the context of smart cities, ensuring transportation safety is a complex task that involves understanding the impact of new technologies, measuring the effectiveness of safety measures, and identifying high-risk locations. However, recent advances in communication and big data analytics have made it possible to address these challenges in a more efficient manner. Traditional transportation management centers (TMCs) are limited in their ability to analyze large amounts of data for safety evaluation. To overcome this limitation, this project aims to develop an intelligent transportation management center (ITMC) that utilizes automated video analysis to assess safety. By leveraging Intelligent Transportation Systems (ITS) technologies and big data analytics, the proposed ITMC can proactively evaluate safety at signalized intersections. Unlike conventional methods that rely on crash data, the ITMC uses safety surrogate measures (SSMs) to identify near-crash situations and calculate proactive risk. In this study, the results obtained from a machine vision model were used along with the Post Encroachment Time (PET) safety surrogate measure (SSM) to assess safety proactively at a selected signalized intersection. The study utilized the latest YOLO series model, YOLOX, for deep learning to detect and classify road users in video frames from four intersection traffic cameras.