Operation and Safety Performance Assessment of Queue Warning Applications on Signalized Intersections for Connected Freight Vehicles

Download or read book Operation and Safety Performance Assessment of Queue Warning Applications on Signalized Intersections for Connected Freight Vehicles written by Sara Bashir and published by . This book was released on 2020 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connected Vehicle (CV) technologies have been a core component for the Intelligent Transportation Systems (ITS) due to their capability to support a variety of ITS applications and unite vehicles and infrastructure elements into a well-integrated transportation system. Connected vehicles refer to those that exchange information with each other and the infrastructure using Dedicated Short-Range communication (DSRC), known as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Infrastructure-to-Vehicle (I2V) communications under Vehicular ad hoc Network (VANET). These communications improve safety, mobility, and productivity of transportation systems users. This research develops and tests CV-based algorithms for a Queue Warning (Q-WARN) application for heavy freight vehicles. The Q-WARN application uses CV technologies to allow vehicles within the queue to automatically broadcast their queued status information (e.g., rapid deceleration, disabled status, and lane location) to upstream vehicles as well as to infrastructure. The queue warnings are sent to oncoming vehicles to prevent rear-end or other secondary collisions. It can also provide drivers the appropriate warning to help them safely reduce their speed, make the decision of changing lanes, or use alternative routes through enhancing drivers’ situational awareness. This thesis focuses on Q-WARN applications for freight vehicles at signalized intersections that are located adjacent to I-80 in Wyoming, which are characterized by heavy truck traffic. Four locations in the state of Wyoming, characterized by heavy freight traffic, are selected as the test-case intersections. The algorithms developed are using the latitude/longitude (lat/lon) coordinates of freight CVs and intersections to form a communication link and share information. The tests were performed in VISSIM microsimulation with Econolite ASC/3 Software-in-the-Loop (SIL) traffic signal controller emulator. The scope of analyses is twofold. Firstly, from the traffic operation approach: queuing, total delays, and vehicle spacing were scrutinized at intersection. Secondly, from the traffic safety perspective, Time-to-Collision (TTC) and Post-Encroachment Time (PET), as two commonly used Surrogate Measures of Safety (SMoS), were assessed. Through analyses of simulation results, in-depth comparisons of these traffic flow and safety parameters at baseline (0%), 10%, 25%, 50%, 75%, and 100% CV Market Penetration Rates (MPRs) under the effect of Q-WARN application were performed. The simulation scenarios developed through communication protocol can be easily implemented in the field, since it uses universal technologies. The results from developed Q-WARN algorithms revealed that the application has a remarkable potential to improve traffic operation parameters at signalized intersections, as well as providing safety benefits, especially when applied to heavy freight vehicles. Findings indicated successful traffic operation performance of the developed Q-WARN application in reducing vehicle delays by an average of 2% to 5%, as well as increasing the spacing between vehicles up to 134%, which could be an indicator of safety benefits. The results depicted that as the CV-MPR increases, the means of both SMoS increases, leading to safety enhancement under the impact of Q-WARN application. The abundance of information obtained from CV vehicles can be used to further enhance different control algorithms and applications.