Adaptive performance optimization for large-scale traffic control systems

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators

Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning

One of the most critical components of an urban transportation system is the coordination of intersections in arterial networks. With the advent of data-driven approaches for traffic control systems, deep reinforcement learning (RL) has gained significant traction in traffic control research. Proposed deep RL solutions to traffic control are designed to directly modify either phase order or timings; such approaches can lead to unfair situations -- bypassing low volume links for several cycles -- in the name of optimizing traffic flow. To address the issues and feasibility of the present approach, we propose a deep RL framework that dynamically adjusts the offsets based on traffic states and preserves the planned phase timings and order derived from model-based methods. This framework allows us to improve arterial coordination while preserving the notion of fairness for competing streams of traffic in an intersection. Using a validated and calibrated traffic model, we trained the policy of a deep RL agent that aims to reduce travel delays in the network. We evaluated the resulting policy by comparing its performance against the phase offsets obtained by a state-of-the-practice baseline, SYNCHRO. The resulting policy dynamically readjusts phase offsets in response to changes in traffic demand. Simulation results show that the proposed deep RL agent outperformed SYNCHRO on average, effectively reducing delay time by 13.21% in the AM Scenario, 2.42% in the noon scenario, and 6.2% in the PM scenario. Finally, we also show the robustness of our agent to extreme traffic conditions, such as demand surges and localized traffic incidents

Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control

We propose a two-stage, on-line signal control strategy for dynamic networks using a linear decision rule (LDR) approach and a distributionally robust optimization (DRO) technique. The first (off-line) stage formulates a LDR that maps real-time traffic data to optimal signal control policies. A DRO problem is solved to optimize the on-line performance of the LDR in the presence of uncertainties associated with the observed traffic states and ambiguity in their underlying distribution functions. We employ a data-driven calibration of the uncertainty set, which takes into account historical traffic data. The second (on-line) stage implements a very efficient linear decision rule whose performance is guaranteed by the off-line computation. We test the proposed signal control procedure in a simulation environment that is informed by actual traffic data obtained in Glasgow, and demonstrate its full potential in on-line operation and deployability on realistic networks, as well as its effectiveness in improving traffic