Field implementation feasibility study of cumulative travel-time responsive (CTR) traffic signal control algorithm

The cumulative travel-time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within vissim and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50–60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance.