Abstract
Traffic light control falls into two main categories: Agnostic systems that do not exploit knowledge of the current traffic state, e.g., the positions and velocities of vehicles approaching intersections, and holistic systems that exploit knowledge of the current traffic state. Emerging fifth generation (5G) wireless networks enable Vehicle-to-Infrastructure (V2I) communication to reliably and quickly collect the current traffic state. However, to the best of our knowledge, the optimized traffic light management without and with current traffic state information has not been compared in detail. This study fills this gap in the literature by designing representative Deep Reinforcement Learning (DRL) agents that learn the control of multiple traffic lights without and with current traffic state information. Our agnostic agent considers mainly the current phase of all traffic lights and the expired times since the last change. In addition, our holistic agent considers the positions and velocities of the vehicles approaching the intersections. We compare the agnostic and holistic agents for simulated traffic scenarios, including a road network from Barcelona, Spain. We find that the holistic system substantially increases average vehicle velocities and flow rates, while reducing CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, average wait and trip times, as well as a driver stress metric.
Highlights
This section introduces the MDP that we developed in this study, including states, actions, and rewards, as well as the Deep Reinforcement Learning (DRL) algorithm to learn the control of the traffic environment
This DRL approach showed to be able to effectively learn the intelligent control of traffic light signaling at multiple intersections from interaction with its environment
We compared the performance of an agnostic agent, that cannot communicate with vehicles in the traffic network, with the performance of a holistic agent, that features a V2I communication interface and knows the positions and velocities of all vehicles
Summary
E FFECTIVE transportation systems are a key requirement for economic competitiveness and environmental sustainability. Current and upcoming standards, such as IEEE 802.11p, LTE-V, and 5G, allow the exchange of information between individual vehicles and the traffic infrastructure, eventually providing the infrastructure with holistic knowledge of the current state of the traffic system. This should, in theory, enable highly informed control decisions and facilitate congestion mitigation. We find that compared to the agnostic agent, the holistic agent achieves significantly higher average vehicle velocities and flow rates, as well as significantly shorter average trip times through the road networks. For high traffic demands at a single intersection, the holistic agent increases the average vehicle velocities only slightly compared to the agnostic agent.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Open Journal of Intelligent Transportation Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
