Assessment of Queue Warning Application on Signalized Intersections for Connected Freight Vehicles

Abstract

Connected vehicle (CV) systems are at the core of intelligent transportation systems (ITS) for their capability to support a variety of ITS applications and to unite vehicles and infrastructure elements into a well-integrated transportation system. CV refers to vehicles that exchange information with each other and with the infrastructure. The queue warning application (Q-WARN) uses CV technologies to allow vehicles within the queue to broadcast their queued status information automatically to upstream vehicles and to infrastructure. Queue warnings are sent to oncoming vehicles to prevent rear-end or other secondary collisions. This paper focuses on Q-WARN applications for freight vehicles at signalized intersections adjacent to I-80 in Wyoming, which are characterized by heavy truck traffic. The algorithms use the latitude/longitude coordinates of freight CVs and intersections to form a communication link and to share information. Tests were performed in VISSIM microsimulation with Econolite ASC/3 software-in-the-loop controller emulator for different CV market penetration rates. Three locations in Wyoming were used as test-bed cases. The developed Q-WARN algorithms are successful in reducing vehicle delays by an average of 2% to 5%. Time to collision (TTC) significantly increased with an increase in CV rates, by two to five times. The abundance of information obtained from CVs can be used further to enhance signal control algorithms. The developed algorithms can easily be implemented in the field, since they use existing CV communication protocols and signal control logic.