A modified reinforcement learning algorithm for solving coordinated signalized networks
| Autor: | Ozgur Baskan, Soner Haldenbilen, Cenk Ozan, Halim Ceylan |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Signal timing optimization
Engineering Mathematical optimization Traffic control Coordinated signalized network Traffic model Value (computer science) Transportation Learning algorithms Optimum signals road transport Set (abstract data type) Link-flow Reinforcement learning Timing circuits Network performance Reinforcement learning algorithm Civil and Structural Engineering algorithm learning TRANSYT-7F business.industry SIGNAL (programming language) Signal timing Base (topology) Road network Numerical applications Computer Science Applications transportation system Objective function values Automotive Engineering Demand conditions Artificial intelligence numerical model business optimization signal Algorithms |
| Zdroj: | Transportation Research Part C: Emerging Technologies. 54:40-55 |
| ISSN: | 0968-090X |
| DOI: | 10.1016/j.trc.2015.03.010 |
| Popis: | This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased. © 2015 Elsevier Ltd. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect