Multi-Agent and Fuzzy Inference Based Framework for Urban Traffic Simulation

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network. In this paper, a model-free approach is used to optimize signal timing for complicated multiple four-phase signalized intersections. We propose a multi-agent deep reinforcement learning framework that aims to optimize traffic flow using data within traffic signal intersections and data coming from other intersections in a Multi-Agent Environment in what is called Multi-Agent Reinforcement Learning (MARL). The proposed model consists of state-of-art techniques such as Double Deep Q-Network and Hindsight Experience Replay (HER). This research uses HER to allow our framework to quickly learn on sparse reward settings. We tested and evaluated our proposed model via a Simulation of Urban MObility simulation (SUMO). Our results show that the proposed method is effective in reducing congestion in both peak and off-peak times.

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Multi-agent Simulation with Mathematical Optimization of Urban Traffic Using Open Geographic Data

2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) ◽

10.1109/iiai-aai.2016.201 ◽

2016 ◽

Cited By ~ 2

Author(s):

Takuya Matsumoto ◽

Kazutoshi Sakakibara ◽

Makoto Ohara ◽

Hisashi Tamaki

Keyword(s):

Mathematical Optimization ◽

Urban Traffic ◽

Geographic Data ◽

Agent Simulation ◽

Multi Agent

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Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing

Energies ◽

10.3390/en12071402 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1402 ◽

Cited By ~ 10

Author(s):

Haibo Zhang ◽

Xiaoming Liu ◽

Honghai Ji ◽

Zhongsheng Hou ◽

Lingling Fan

Keyword(s):

Cooperative Control ◽

Traffic Signal ◽

Urban Traffic ◽

Data Driven ◽

Experimental Comparison ◽

Signal Control ◽

Signal Timing ◽

Agent Based ◽

Multi Agent ◽

Traffic Signal Timing

Data-driven intelligent transportation systems (D2ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

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