TY - GEN
T1 - A Mobile App for Intersectional Traffic Optimization through Real-Time Vehicle-to-Infrastructure (V2I) Communication and Cyber-Physical Control
AU - Xu, Haowen
AU - Yuan, Jinghui
AU - Wang, Chieh
AU - Shao, Yunli
AU - Berres, Andy
AU - Laclair, Tim
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Connected and Autonomous Vehicles
KW - Cyber-Physical Control
KW - Cyberinfrastructure
KW - Mobile Edge Computing
KW - Mobile Sensing
KW - Vehicle-to-Infrastructure Communication
UR - http://www.scopus.com/inward/record.url?scp=85146111952&partnerID=8YFLogxK
U2 - 10.1109/MASS56207.2022.00044
DO - 10.1109/MASS56207.2022.00044
M3 - Conference contribution
AN - SCOPUS:85146111952
T3 - Proceedings - 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
SP - 260
EP - 261
BT - Proceedings - 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
Y2 - 20 October 2022 through 22 October 2022
ER -
