IMTRACS - Improving traffic signal control utilizing connected vehicle data and surface detection
In recent years, connected vehicles (CVs) have created opportunities to share and receive information from traffic management systems such as traffic signals. Previous CTR studies have investigated and demonstrated the potential of using data from CVs as a complement or replacement for stationary loop detectors in Swedish traffic signal control (the time gap method with one or more LHOVRA functions). The conclusion was that CVs have the potential to increase traffic performance, but in order to fully benefit from CVs, the control strategy needs to be changed so that the optimization is not only based on vehicle detections at fixed locations but rather utilizes the information from extended trajectories of individual vehicles.
The time gap method and LHOVRA functions are designed based on fixed placement of detectors and detection of vehicles at limited but well-selected locations along the roadways. In contrast, CVs can provide the opportunity to detect vehicles even upstream of the first detector, obtain updated information between the detectors, and perhaps even information about the intended movement direction. There is also a fast development of surface detection e.g. using radar, lidar, and video cameras, which, unlike inductive loops, enables detection of individual vehicles' positions and speeds.
The aim of the project is to investigate if, and how, the control of Swedish traffic signals can be improved by using such new types of data to improve traffic efficiency, safety and environment. Today's intentions to maximize throughput, prioritize heavy vehicles or highways (the L&H functions), minimize the risk of rear-end accidents (the O function), minimize the use of amber time (the V-function), etc. will still be in focus, but efforts will be taken to improve the functions utilizing a vehicle-based description of the traffic. It is expected that the control will become more flexible, and it will be easier to identify and adapt the control to unexpected traffic patterns, such as when an emergency vehicle passes, a vehicle breaks down, or accidents occur in the vicinity of a traffic signal, making the control more resilient.
Project partners:
VTI, Linköpings universitet (LiU)
Contact:
Johan Olstam, project leader
Project members:
Kinjal Bhattacharyya (VTI)
Ellen Grumert (VTI)
PhD Candidate (LiU/VTI) - to be recruited