Hierarchical Multi-agent System in Traffic Network Signalization with Improved Genetic Algorithm

Tan, Min; Chuo, Helen Sin Ee; Chin, Renee Ka Yin; Yeo, Kiam Beng; Teo, Kenneth Tze Kin

doi:10.1109/iicaiet.2018.8638464

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…KNN is another method that performs well at data level fusion to produce travel time [35], traffic state prediction [116], and fusing incoming traffic direction [114]. SA proves its ability at decision level fusion in traffic light management system [120], [121], [122]. The flexibility of the hybrid models that they work at feature level and decision level fusions.…”

Section: A Analysismentioning

confidence: 99%

“…The result shows the positive impact on the road network's average speed when drivers use the proposed route. Tan et al [121] highlight a local and supervisory agent concept in a hierarchical-based multi-agent system. A local agent has the capability to perform the best control based on the decision made within limited knowledge exploration, while a supervisory agent has a long-term learning experience to provide the best control decision to all local agents.…”

Section: ) Software Agentmentioning

confidence: 99%

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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

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Section: A Analysismentioning

confidence: 99%

Section: ) Software Agentmentioning

confidence: 99%

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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“…In (Belbachir et al, 2019;Jin & Ma, 2018;Jin & Ma, 2019;Li, Shahidehpour, Bahramirad et al, 2016;Liu et al, 2017;Tan et al, 2018), the authors find that the traffic road control problem at the intersections is a multi-agent system, in which each agent adjusts the traffic lights according to the traffic variations in real time.…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Road Traffic Regulation Approach Using a Wireless Sensor Network

Rida

Hasbi

2021

International Journal of Service Science, Management, Engineering, and Technology

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In this paper, we detail and evaluate a coordinated approach to determining the sequence and duration of green lights at several intersections as part of the Intelligent Transportation System. We present the architecture of a wireless network used to track variations in adjacent intersections. Our algorithm exploits the collected data to determine the sequence of the green lights based on three objectives: (i) reduce the length of queues in the intersection, (ii) prioritize sending vehicle flows to intersections with lower traffic density than the most congested, (iii) synchronize traffic signals between adjacent intersections to create green waves. Traffic simulations have been simulated by the SUMO traffic simulator, they show that our solution manages to react to traffic change and reduce waiting time compared to isolated control strategies.

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“…In [16,31,[36][37][38][39], the authors find that the traffic road control problem at the intersections is a multi-agent system, in which each agent adjusts the traffic lights according to the traffic variations in real-time.…”

Section: Related Workmentioning

confidence: 99%

Coordinated Signal Control System in Urban Road Network

Rida

Ouadoud

Hasbi³

2020

Int. J. Onl. Eng.

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Road congestion has become one of the major problems of urban cities due to the growth in the number of vehicles and the limitation of road infrastructure. The consequences of this congestion can be seen through a difficult movement of vehicles, an increase in CO2 emissions, increasing energy consumption, and stress for drivers. Intelligent Transport Systems (ITS) based on wireless sensor networks (WSN) have emerged with new solutions and application potentials in the context of the Intelligent city. In this article, we study an approach of using a wireless sensor network to control the traffic lights of several intersections and optimize their performance by reducing the average waiting time of users. the proposed adaptive traffic light controller determines the traffic lights sequence in real time based on the current traffic situation at the local intersection, the expected arrivals and the degree of congestion at destination intersections. To evaluate our approach, we construct an intersection network of twenty-four intersections with high traffic intensity using the SUMO simulator, the results of the simulation demonstrate the efficiency and practicality of the proposed algorithm.

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Hierarchical Multi-agent System in Traffic Network Signalization with Improved Genetic Algorithm

Cited by 6 publications

References 12 publications

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

A Collaborative Road Traffic Regulation Approach Using a Wireless Sensor Network

Coordinated Signal Control System in Urban Road Network

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