A Mobile App for Intersectional Traffic Optimization through Real-Time Vehicle-to-Infrastructure (V2I) Communication and Cyber-Physical Control

• Published in: 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS) pp. 260 - 261
• Main Authors: Xu, Haowen, Yuan, Jinghui, Wang, Chieh, Shao, Yunli, Berres, Andy, Laclair, Tim
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2022
• Subjects: Connected and Autonomous Vehicles; Cyber-Physical Control; Cyberinfrastructure; Mobile applications; Mobile Edge Computing; Mobile Sensing; Real-time systems; Sensors; Timing; Traffic control; Vehicle-to-infrastructure; Vehicle-to-Infrastructure Communication; Velocity control
• ISSN: 2155-6814
• DOI: 10.1109/MASS56207.2022.00044

This extended abstract presents a novel mobile app that enables two-way Vehicle-to-Infrastructure (V2I) Communication to reduce traffic congestion and excessive fuel consumption at signalized intersections. The app is developed using intelligent speed control algorithms powered with real-time signal timing information collected through the Internet of Things (IoT)-connected transportation infrastructure. Based on the nearby traffic condition and traffic signal green window, our app can advise drivers to maintain an optimal vehicle speed for individual vehicles to avoid stop-and-go traffic patterns. We demonstrate the capability of our app as an on-board speed optimization unit through a real-vehicle experiment conducted in a Connected and Automated Vehicle Environment (CAVE) Laboratory. Our mobile app is implemented using cross-platform technologies and based on the commonly accepted communication protocol, allowing it to support multiple types of mobile devices and connect to various models of signal light controller.