Evaluation of Traffic Signal Systems Effectiveness in Connected Vehicle Environments Using Trajectory Analytics

• Published in: Transportation research record Vol. 2675; no. 11; pp. 509 - 521
• Main Authors: Ostojic, Marija, Mahmassani, Hani S.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.11.2021
• ISSN: 0361-1981; 2169-4052
• DOI: 10.1177/03611981211018470

One of the ways to design more effective signal control strategies is to leverage and synthesize connected vehicle generated (CVG) information to identify traffic states for the controller to operate in a predictive, yet vehicle-actuated manner. The contribution of this paper is twofold: (1) it presents a framework for an advanced, online, signal control logic in a connected environment that utilizes information from connected vehicles (CVs) to augment high-resolution controller and/or sensor data, and (2) it applies the trajectory analytics to compare the performance of the new controller schemes with CVG data and functionalities relative to conventional, vehicle-actuated, control. The framework puts forward a predictive control logic that schedules phases in an acyclic manner over a variable planning horizon. Phase duration is continually evaluated in response to updated requests for service distributed among equipped vehicles and associated performance indicators. Within the same connected control setup, two measures of effectiveness of a decision were compared to determine the upper bound on the potential effectiveness of a more responsive control strategy. Finally, trajectory analytics was used to evaluate the effectiveness of the CV technology-based control scheme against the conventional one. The findings indicate that both control system performance assessment and optimization objectives should change with access to CVG data. Unlike current state of the practice controllers, the developed method is able to handle high and low demand states equally well. The designed connected controller is shown to be robust in handling varying traffic conditions and demand levels.