Developing an Adaptive Connected Vehicle Transit Signal Priority Control System

Author(s):  
Hossam M. Abdelghaffar ◽  
Kyoungho Ahn ◽  
Hesham A. Rakha
2020 ◽  
Author(s):  
Noah J. Goodall ◽  
Brian L. Smith ◽  
Ramkumar Venkatanarayana

Wireless communication between vehicles and the transportation infrastructure will provide significantly more timely and comprehensive information about arterials and their performance. However, most measures-of-effectiveness were developed based on data available from traditional “point” sensors. The information made available in a connected vehicle environment requires new metrics that can fully utilize the data. This paper identifies several new arterial performance metrics made available in a connected vehicle environment, as well as several existing metrics that can be evaluated more accurately and frequently than before. The new metrics are person-delay, sudden deceleration, change in lateral acceleration, and aggregate regulation compliance. Person-delay measures a vehicle’s lost time multiplied by the number of passengers, and allows for more efficient movement of high-occupancy vehicles and sophisticated transit signal priority. Sudden deceleration and change in lateral acceleration measure activities such as unexpected braking and swerving, which may be leading indicators of unsafe conditions. Aggregate regulation compliance detects unsafe driving behavior that is difficult to collect in the field, such as speeding and illegal U-turns. Engineers can address problem areas through signal timing changes traffic calming, and other measures. The proposed metrics all require high-resolution detection, and are difficult or impossible to measure with existing point detection. For each new metric, its compatibility with connected vehicles is discussed, and required SAE J2735 DSRC Message Set Dictionary data elements are identified.


Author(s):  
Zorica Cvijovic ◽  
Milan Zlatkovic ◽  
Aleksandar Stevanovic ◽  
Yu Song

Connected vehicle (CV) technologies enable safe and interoperable wireless communication among vehicles and the infrastructure with the possibility to run many applications that can improve safety, and enhance mobility. This paper develops CV-based algorithms which use transit vehicle speed and the estimated time that the vehicle needs to arrive at an intersection to trigger transit signal priority (TSP) initiation. This information is updated each second based on the traffic conditions such as speed, a current distance of a transit vehicle to the intersection, and queue conditions. The algorithm uses the actual speed of a transit vehicle and its latitude/longitude (lat/lon) coordinates to compute the time that the vehicle needs to reach the stop line. It was tested on a real-world network using VISSIM traffic simulation, but can easily be implemented in the field, since it is using world coordinates. The upgraded algorithm was applied to a future bus rapid transit (BRT) scenario, and included different levels of conditional TSP, which depend on three combined conditions: the time that a transit vehicle needs to reach the stop line, the number of passengers on board, and the lateness that the transit vehicle experiences. The test-case network used for model building is a corridor consisting of ten signalized intersections along State Street in Salt Lake City, UT. The CV algorithms coupled with TSP can yield notable delay reductions for both the regular bus and the BRT of 33% and 12%, respectively.


2021 ◽  
Vol 67 (2) ◽  
pp. 1-12
Author(s):  
Zorica Cvijovic ◽  
Milan Zlatkovic ◽  
Aleksandar Stevanovic ◽  
Yu Song

Connected Vehicles (CV) are an emerging technology with a large potential to improve traffic operations and safety. This paper develops and tests advanced CV-based multi-level conditional Transit Signal Priority (TSP). The algorithms are using the latitude/longitude (lat/lon) coordinates of CV vehicles and intersections to establish communication, share information and request priority. The TSP strategies are implemented through controllers’ built-in features and logic processor, using Econolite ASC/3 as a representative traffic signal controller. The tests were performed in VISSIM microsimulation with the ASC/3 Software-in-the-Loop (SIL) controller emulator. State Street in Salt Lake City, UT, is selected as a test-case corridor. The paper shows that the developed signal control priority (SCP) algorithms are successful in reducing delays for target vehicles in excess of 6%, without significant impacts on other traffic. The information obtained from CV vehicles can be used to further enhance control algorithms and create adaptive SCP programs.


2019 ◽  
Vol 11 (23) ◽  
pp. 6819
Author(s):  
Sangjun Park ◽  
Kyoungho Ahn ◽  
Hesham A. Rakha

Traffic signal priority is an operational technique employed for the smooth progression of a specific type of vehicle at signalized intersections. Transit signal priority is the most common type of traffic signal priority, and it has been researched extensively. Conversely, the impacts of freight signal priority (FSP) has not been widely investigated. Hence, this study aims to evaluate the energy and environmental impacts of FSP under connected vehicle environment by utilizing a simulation testbed developed for the multi-modal intelligent transportation signal system. The simulation platform consists of VISSIM microscopic traffic simulation software, a signal request messages distributor program, an RSE module, and an Econolite ASC/3 traffic controller emulator. The MOVES model was employed to estimate the vehicle fuel consumption and emissions. The simulation study revealed that the implementation of FSP significantly reduced the fuel consumption and emissions of connected trucks and general passenger cars; the network-wide fuel consumption was reduced by 11.8%, and the CO2, HC, CO, and NOX emissions by 11.8%, 28.3%, 24.8%, and 25.9%, respectively. However, the fuel consumption and emissions of the side-street vehicles increased substantially due to the reduced green signal times on the side streets, especially in the high truck composition scenario.


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