Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection
| Autor: | Dae-ho Kim, Ok-Ran Jeong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
traffic flow prediction Real-time computing Control (management) 02 engineering and technology lcsh:Chemical technology Biochemistry Article Analytical Chemistry Traffic signal 0502 economics and business 0202 electrical engineering electronic engineering information engineering Reinforcement learning cooperative traffic signal control lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation 050210 logistics & transportation deep reinforcement learning 05 social sciences ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Traffic flow Atomic and Molecular Physics and Optics Traffic congestion 020201 artificial intelligence & image processing State (computer science) Intersection (aeronautics) |
| Zdroj: | Sensors, Vol 20, Iss 1, p 137 (2019) Sensors Volume 20 Issue 1 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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