Modelling and Analysis of an Efficient Traffic Network Using Ant Colony Optimization Algorithm

Nahar, Shahrizul Anuar Abu; Hashim, Fazida Hanim

doi:10.1109/cicsyn.2011.20

Cited by 16 publications

(17 citation statements)

References 9 publications

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“…However, a small number of ants cannot cause the above mentioned convergence rate. Nahar and Hashim (2011) proposed a traffic congestion control method based on different preferences. These preferences allow the algorithm to reduce average travelling time by adjusting ant colony variables.…”

Section: Variable-based Acpmentioning

confidence: 99%

“…Their results show that the number of ants is directly correlated with the algorithm performance. The number of agents should not be less than the threshold defined in the algorithm (Nahar and Hashim, 2011). Ant colony optimisation Propose a preference-based approach for the preference-based shortest path using ACO.…”

Section: Variable-based Acpmentioning

confidence: 99%

“…Ant colony optimisation Propose a preference-based approach for the preference-based shortest path using ACO. shortest path search Nahar and Hashim (2011) Modelling and analysis of an Create an optimum traffic efficient traffic network using ant system and a platform for colony optimisation algorithm vehicle congestion control. Liu et al (2007) Ant colony system algorithm Find an optimal path by for path routing of urban considering time and traffic vehicles distance.…”

Section: Variable-based Acpmentioning

confidence: 99%

“…Ok et al (2011) Path length Although the best values for a set of number of links parameters (e.g., number of ants and iteration) are introduced to improve ACO performance, the algorithm is unsuitable for finding the shortest path based on preference. Nahar and Hashim (2011) Traffic distribution This approach is analogous to proposed algorithm by travel time Ok et al (2011). However this technique does not perform well when the number of agent in the network is less than 100 agents, which leads to high overhead.…”

Section: Referencesmentioning

confidence: 99%

See 3 more Smart Citations

Ant colony optimisation for vehicle traffic systems: applications and challenges

Jabbarpour

Malakooti

Noor

et al. 2014

IJBIC

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Ant-based algorithms simulate the cooperative behaviour of real ants in finding food resources. A significant number of studies have focused on the self-organised behaviour of ants in the natural environment to develop effective systems for dynamic problems. Ant-based systems have special properties such as scalability, adaptability, and dynamicity, which are the main requirements for solving vehicle traffic congestion problem. Thus, ant-based algorithms are now being adopted by vehicle traffic systems (VTSs) to guide vehicles to less congested paths. However, literature shows that comprehensive reviews are lacking in this field. The main contribution of this paper is the review and classification of the most relevant systems based on novel taxonomy. A survey that includes statistical analyses on ant-based VTS was conducted to identify the limitations and evaluation process of VTS. This paper concludes by proposing a general framework in applying ant colony optimisation to VTS. . He received his Master of Data Communication and Networking in 2012 at the same university. His current research interests include ad hoc networks, quality of service, swarm intelligence and vehicle traffic routing systems.Hossein Malakooti received his Master of Data Communication and Networking at the

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Section: Variable-based Acpmentioning

confidence: 99%

Section: Variable-based Acpmentioning

confidence: 99%

Section: Variable-based Acpmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

See 2 more Smart Citations

Ant colony optimisation for vehicle traffic systems: applications and challenges

Jabbarpour

Malakooti

Noor

et al. 2014

IJBIC

View full text Add to dashboard Cite

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“…Therefore ant-colony algorithm is widely applied to solve vehicle routing problem, although some modifications have been applied depending on different circumstances. Nahar and Hashim [26] proposed a traffic congestion control method based on different preferences to create an optimal traffic system. Figure 3 shows the method used in determining the optimum path.…”

Section: A Ant-colony Algorithmmentioning

confidence: 99%

A Survey on Urban Traffic Optimisation for Sustainable and Resilient Transportation Network

Sang

Zhou

Yang

et al. 2016

2016 9th International Conference on Developments in eSystems Engineering (DeSE)

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-Nowadays, sustainability and resilience have become a major consideration that cannot be neglected in urban development. People are starting to consider utilizing the urban infrastructure environment to maintain and improve the functionality and availability of the urban system when unexpected events take place. Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. It has huge impact on urban sustainability and resilience such as overload of the city's infrastructure, and air pollution, etc. This paper presents a survey on the challenges of developing sustainable and resilient transportation networks and the current urban traffic optimisation methods, as a possible solution to address such challenges. It aims to describe and define the state of the art on the research on sustainable and resilient transportation networks in urban development and a taxonomy of different traffic optimisation methods used for avoiding traffic congestion and improve urban traffic management.

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A Learning Approach for Road Traffic Optimization in Urban Environments

Mejdoubi¹,

Zytoune²,

Fouchal³

et al. 2020

Lecture Notes in Computer Science

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In many urban areas where road drivers are suffering from the huge road traffic flow, conventional traffic management methods have become inefficient. One alternative is to let road-side units or vehicles learn how to calculate the optimal path based on the traffic situation. This work aims to provide the optimal path in terms of travel time for the vehicles seeking to reach their destination avoiding road traffic congestion and in the least possible time. In this paper we apply a reinforcement learning technique, in particular Q-learning, that is employed to learn the best action to take in different situations, where the transiting delay from a state to another is used to determinate the rewards. The simulation results confirm that the proposed Q-learning approach outperformed the greedy existing algorithm and present better performances.

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Modelling and Analysis of an Efficient Traffic Network Using Ant Colony Optimization Algorithm

Cited by 16 publications

References 9 publications

Ant colony optimisation for vehicle traffic systems: applications and challenges

Ant colony optimisation for vehicle traffic systems: applications and challenges

A Survey on Urban Traffic Optimisation for Sustainable and Resilient Transportation Network

A Learning Approach for Road Traffic Optimization in Urban Environments

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