Optimal Traffic Control at Smart Intersections: Automated Network Fundamental Diagram

Recent advances in artificial intelligence and wireless communication technologies have created new potentials for communicant autonomous vehicle (CAV) to reduce congestion in urban networks. In this research, we develop a stochastic analytical model for optimal traffic control at smart intersections for CAVs. We also present the automated network fundamental diagram (ANFD) as a macro-level modeling tool for urban networks with smart intersections. In the proposed cooperative control strategy, we make use of the inter-platoon headway for consecutive passes of CAV platoons through each other at non-signalized intersections with no delay. For this to happen, the arrival and departure of platoons in crossing directions need to be synchronized. To improve the system robustness (synchronization success probability), we add a marginal gap to the minimum inter-platoon headway to make up for the operational error in the synchronization process. We then develop a stochastic traffic model for the smart intersections. Our results show that the effects of increasing the platoon size and the marginal gap length on the network capacity are not positive monotonic. In fact, the capacity can be maximized by optimizing these cooperative control variables. Hence, we analytically solve the traffic optimization problem for the platoon size and marginal gap length and derive a closed-form solution for a normal distribution of the operational error. The performance of the network with smart intersections is presented by a stochastic ANFD, derived analytically and verified numerically using the results of a simulation model. The simulation results show that the proposed cooperative control strategy can improve the capacity up to 138% when the system is optimally controlled.
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Cooperative traffic control in automated networks