A Learning-based Adaptive Signal Control System with Function Approximation
2016 (English)In: IFAC PAPERSONLINE, 2016, Vol. 49, no 3, 5-10 p.Conference paper (Refereed)
Traffic signal control plays a crucial role in traffic management and operation practice. In the past decade, adaptive signal control systems have shown the abilities to improve the effectiveness of the transportation system in many aspects. This paper proposes an adaptive signal control system in the context of group-based phasing techniques. The adaptive signal control system is modeled as a multi agent System capable of acquiring knowledge on-line based on the perceived traffic states and the feedback from the external environment,. Reinforcement learning is applied as the learning algorithm resulting in intelligent timing decisions. Feature based function approximation method is incorporated into the reinforcement learning framework for the purpose of improving learning efficiency as well as the quality of signal timing decisions. The assessment of such a learning-based signal control system is carried out by using an opensource microscopic traffic simulation software, SUMO. A benchmarking system, the optimized group-based vehicle actuated signal control system, compared with the learning-based signal control systems regarding mobility efficiency. The simulation results show that the proposed adaptive group based signal control system has the potential to improve the mobility efficiency regardless of the settings of traffic demands.
Place, publisher, year, edition, pages
2016. Vol. 49, no 3, 5-10 p.
Adaptive signal control, group-based phasing, multi-agent system, reinforcement learning, function approximation
Transport Systems and Logistics
IdentifiersURN: urn:nbn:se:kth:diva-192980DOI: 10.1016/j.ifacol.2016.07.002ISI: 000381502600002OAI: oai:DiVA.org:kth-192980DiVA: diva2:974710
14th IFAC Symposium on Control in Transportation Systems (CTS), MAY 18-20, 2016, Istanbul, TURKEY
QC 201609272016-09-272016-09-232016-09-27Bibliographically approved