scholarly journals Transit signal priority in a connected vehicle environment: User throughput and schedule delay optimization approach

Author(s):  
Roozbeh Mohammadi ◽  
Claudio Roncoli ◽  
Milos N. Mladenovic
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.


Author(s):  
Yunli Shao ◽  
Zongxuan Sun

This work proposes a unified framework for the eco-approach application that integrates traffic prediction, vehicle optimization, and implementation. The eco-approach application is formulated as either a car-following optimization problem or a single vehicle optimization problem, depending on whether a preceding vehicle exists. The traffic prediction scheme anticipates future traffic conditions and describes the traffic dynamics on the road segment of interest using state variables: traffic flow, density, and speed. With the information enabled by connectivity, the traffic state estimation is updated using an observer. Uncertainties in the traffic prediction are considered using a robust optimization approach. The robust optimization problem is discretized and solved by an efficient nonlinear programming solver. The proposed eco-approach framework is implemented to a single lane single intersection scenario for 12, 8, 4, and 1 connected vehicle scenarios. The fuel benefits vary from 11.0% to 6.7% as the penetration rates of connectivity decrease. The performance is satisfactory compared to the 12.0% fuel benefits with perfection traffic prediction.


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