An Ant Colony Optimization Approach for the Preference-Based Shortest Path Search

Ok, Seung-Ho; Seo, Woo Jin; Ahn, Jin Ho; Kang, Sungho; Moon, Byungin

doi:10.1007/978-3-642-10844-0_63

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…Liu et al 31 Convergent speed A set of parameters are introduced to maintain the operation of the ACO algorithm in a steady state with high convergent speed. The parameter set is related to specific problem space and may not work for other algorithms such as proposed algorithm by Ok et al 32 and Nahar and Hashim. 33 Weyns et al 34 Traffic distribution The use of a multi-agent mechanism provides this system with more flexibility for dynamic conditions.…”

Section: Vehicle-to-vehicle Communicationmentioning

confidence: 99%

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Ragavan¹,

Venkatalakshmi²,

Vijayalakshmi³

2020

Computational Intelligence

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Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV-based route selection method provides better performance.

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Section: Vehicle-to-vehicle Communicationmentioning

confidence: 99%

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Ragavan¹,

Venkatalakshmi²,

Vijayalakshmi³

2020

Computational Intelligence

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“…In [8] a preference based shortest path determination using ACO is investigated. In this case shortest path is obtained taking into consideration the preferred paths of the agents.…”

Section: Related Workmentioning

confidence: 99%

NetLogo implementation of an ant colony optimisation solution to the traffic problem

Jerry

Kuang

Opare

et al. 2015

IET Intelligent Transport Systems

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“…shortest path problem), researchers have started to improve the steps or found optimal values for various parameters of the ACO to prepare it for dynamic problems such as vehicular environments. Some limited studies have been conducted into finding the best values for the parameters in ACO to reduce vehicle congestion on the roads (Liu et al (2007); Nahar and Hashim (2011); Ok et al (2011)). On the other hand, most of the efforts were performed on modifying the ants performance or the probability function in the second step of ACO.…”

Section: Progress Of Ant-based Vehicle Congestion Control Systemsmentioning

confidence: 99%

Ant-based vehicle congestion avoidance system using vehicular networks

Jabbarpour

Jalooli

Shaghaghi

et al. 2014

Engineering Applications of Artificial Intelligence

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Vehicle traffic congestion leads to air pollution, driver frustration, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a considerable challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Instead of using static algorithms, e.g. Dijkstra and A*, we present a bio-inspired algorithm, food search behavior of ants, which is a promising way of solving traffic congestion in vehicular networks. We have called this the Antbased Vehicle Congestion Avoidance System (AVCAS). AVCAS combines the average travel speed prediction of traffic on roads with map segmentation to reduce congestion as much as possible by finding the least congested shortest paths in order to avoid congestion instead of recovering from it. AVCAS collects real-time traffic data from vehicles and road side units to predict the average travel speed of roads traffic. It utilizes this information to perform an ant-based algorithm on a segmented map resulting in avoidance of congestion. Simulation results conducted on various vehicle densities show that the proposed system outperforms the existing systems in terms of average travel time, which decreased by an average of 11.5%, and average travel speed which increased by an average of 13%. In addition, AVCAS handles accident conditions in a more efficient way and decreases congestion by using alternative paths.

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An Ant Colony Optimization Approach for the Preference-Based Shortest Path Search

Cited by 9 publications

References 7 publications

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

NetLogo implementation of an ant colony optimisation solution to the traffic problem

Ant-based vehicle congestion avoidance system using vehicular networks

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