Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework that dynamically adjusts traffic signal offsets at arterial intersections, improving flow and reducing delays while maintaining phase predictability.

Contribution

The novel framework dynamically adjusts offsets based on traffic states, preserving phase order and timing, which enhances arterial coordination over existing methods.

Findings

Reduced average delay by 13.21% in AM scenario

Improved robustness to traffic demand surges

Outperformed baseline offsets in simulation

Abstract

Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven approaches for traffic control systems. To date, proposed deep RL-based traffic schemes control phase activation or duration. Yet, such approaches may bypass low volume links for several cycles in order to optimize the network-level traffic flow. Here, we propose a deep RL framework that dynamically adjusts 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 maintaining phase order and timing predictability. 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 deployed along a segment of Huntington Drive in the city of Arcadia. The resulting policy dynamically readjusts phase offsets in response to changes in traffic demand. Simulation results show that the proposed deep RL agent outperformed the baseline 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 when offsets are adjusted in 15-minute intervals. Finally, we also show the robustness of our agent to extreme traffic conditions, such as demand surges in off-peak hours and localized traffic incidents