Network-Wide Traffic State Estimation and Rolling Horizon-Based Signal Control Optimization in a Connected Vehicle Environment

This paper presents an innovative method to adaptively optimize traffic signal plans based on the estimation of traffic situation achieved from the information of various penetration rates of Connected Vehicles (CVs). The network-wide signal control problem is formulated as a linear optimization problem. Moreover, we develop a Kalman filter (KF) and Neural Network (NN) algorithms to predict and update the traffic situation under mixed non-connected and connected vehicles environment. To capture the dynamic of the traffic flow, we employ the cell transmission model synched with the Vissim traffic simulator. The methodology is tested using a challenging network of six intersections. We test our model for various Penetration Rates (PR) of the CV to provide a comparative analysis. The performance of the method is also compared with a conventional actuated-coordinated traffic signal plan. The results show that with a bare minimum PR (say more than 30%), our proposed methodology outperforms the actuated traffic signal plan. (note that the minimum PR is subject to further ongoing research in the literature, to the extent that lower PRs might be plausible). Though a 100% PR is highly desirable, our method can fetch the maximum benefit just by 60% PR.

Automated summary

The study proposes an innovative method to adaptively optimize traffic signal plans based on the estimation of various penetration rates of Connected Vehicles (CVs). By using Kalman filter and Neural Network algorithms to predict and update traffic situation, the methodology outperforms conventional actuated-coordinated traffic signal plans at lower than 100% penetration rates. The results show that the proposed method can achieve maximum benefits even at 60% penetration rate, indicating its effectiveness in traffic signal optimization.