DOI: 10.15787/vtt1/p9gyi6
title: Developing an Intelligent Transportation Management Center (ITMC) with a Safety Evaluation Focus for Smart Cities (04-110)

description: Project Description: The Intelligent Transportation Management Center (ITMC) project was launched to address the limitations of traditional Transportation Management Centers (TMCs) by integrating advanced technologies such as machine learning, big data science, and image processing. The research, spanning from mid-November to mid-December 2022, focused on a signalized intersection at H Street and Broadway in Chula Vista, San Diego. The ITMC aimed to go beyond the human-dependent operations of conventional TMCs, which are prone to errors, by automating the detection and analysis of near-crash events across multiple transportation modes including vehicles, trucks, bicycles, motorcycles, and pedestrians. The use of Post Encroachment Time (PET), a recognized surrogate safety measure (SSM), allowed for a more proactive approach to safety evaluation. To process the collected video ﬁles for detection and tracking of the road users, YOLOX, the latest iteration of the YOLO series, was used as the deep learning modeling framework. This project involved collecting vast amounts of video data to not only detect incidents but also to perform spatiotemporal analyses, thus identifying the conditions under which near- crashes are most likely to occur. By examining these incidents at various times—peak versus oﬀ- peak hours, and weekdays versus weekends—and during diﬀerent stages of the traﬃc signal cycle, the research provides insights into the dynamics of safety risks at the intersection. The spatial analysis component of the study produced heatmap visualizations that pinpoint areas of frequent near-crash events, eﬀectively highlighting the intersection’s safety hotspots. This visual tool aids in illustrating the concentration of risk exposure, which is essential for transportation planning and decision-making. Ultimately, the project's comprehensive approach oﬀers a potential model for enhancing transportation safety through technology-driven solutions. Data Scope: Four high-deﬁnition cameras were installed by the research team on the signal mast arms at the intersection of H Street and Broadway in Chula Vista, San Diego. These cameras captured continuous 720p high-deﬁnition video at 10 frames per second, meticulously documenting the traﬃc conditions from all directions. The footage was systematically saved on an hourly basis in Audio Video Interleave (.avi) format, employing Motion JPEG (MJPEG) compression, culminating in a comprehensive dataset encompassing 3.8 million interactions among road users. This dataset was enriched with a variety of associated variables for each interaction, among which the PET variable was paramount for the safety evaluation. Utilizing this variable, a detailed analysis was conducted to assess the potential safety risks at the intersection. The intricate details of the dataset and the variables are further elaborated upon in the subsequent section of the documentation. Video ﬁles were processed using the YOLOX deep learning model, and the data management was facilitated by connecting each camera to an NVIDIA® Jetson AGX Xavier™ edge device, ﬁtted with a 5TB USB hard drive. A Cradlepoint IBR1700 Series Ruggedized Router with LTE interface was employed to ensure the eﬃcient retrieval of the video ﬁles over the cellular network, with the ﬁles being transferred to the university GPU server at notos.sdsu.edu for object detection and further analysis. Data Speciﬁcation: The processed data was meticulously organized into structured formats compatible with SQL databases and CSV ﬁles, featuring 23 distinct variables. These variables are detailed in the table (included below), which includes the type of each variable and the range of values it encompasses.