Avoiding Traffic Jam Using Ant Colony Optimization - A Novel Approach

Naganjaneyulu, P. V.; Prasad, Kavirayani R.

doi:10.1109/iccima.2007.61

Cited by 33 publications

(19 citation statements)

References 4 publications

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“…As pointed out earlier, ant-based routing algorithms have been already investigated for traffic management (Alves et al, 2010;Bedi, Mediratta, Dhand, Sharma, & Singhal, 2007;Krömer et al, 2011). In current work, researchers try to adapt the basic algorithm to avoid the negative emergent effects outlined before.…”

Section: Ant-based Routing Algorithmsmentioning

confidence: 97%

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Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

Dallmeyer

Schumann

Lattner

et al. 2014

Journal of Intelligent Transportation Systems

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Traffic routing is a well-established optimization problem in traffic management. Here, we address dynamic routing problems where the load of roads is taken into account dynamically, aiming at the optimization of required travel times. We investigate ant-based algorithms that can handle dynamic routing problems, but suffer from negative emergent effects like road congestions. These negative effects are inherent in the design of ant-based algorithms. In this article we propose an inverse ant-based routing algorithm to (a) maintain the positive features of ant-based algorithms for dynamic routing problems, while (b) avoiding the occurrence of negative emerging effects, like road congestion. We evaluated the performance of the proposed algorithm by comparing its results with two alternative routing algorithms, namely, A * , which is a static routing algorithm, and an iterative approach. In particular, the iterative approach is used for providing an upper bound, as it uses routing knowledge in a number of calibration runs, to determine the actual load, before the effective routing is done. For the evaluation we used the agent-based traffic simulation system MAINSIM. The evaluation was done with one synthetic and two real-world scenarios, to outline the practical relevance of our findings. Based on these evaluations, we can conclude that the inverse ant-based routing approach is particularly suited for a scenario with a high traffic density, as it can adapt the routing of each vehicle, while avoiding the negative emerging effects of conventional ant-based routing algorithms.

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Section: Ant-based Routing Algorithmsmentioning

confidence: 97%

“…The idea of inferring the current traffic situation from the pheromone trace has also been suggested by Bedi et al (2007). In their approach, Bedi et al discuss an approach for picking the next edge to travel toward the destination.…”

Section: Ant-based Routing Algorithmsmentioning

confidence: 97%

Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

Dallmeyer

Schumann

Lattner

et al. 2014

Journal of Intelligent Transportation Systems

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“…The first one is a dispersion of traffic flow and exhaust gas, the second one is a reduction of CO gas, and the third one is an increase of the transportation efficiency. Bedi et al (2007) proposed the Dynamic System for Avoiding Traffic Jam (DSATJ), inspired in Ant Colony Optimization (ACO) algorithms, which aims at choosing an alternative optimum path to avoid traffic jams. In their proposal, traffic jams are detected through pheromone values on edges.…”

Section: Density-aware Systems Designed To Avoid Traffic Jamsmentioning

confidence: 99%

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Barrachina

Garrido

Fogue

et al. 2015

Wireless Pers Commun

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Road traffic is experiencing a drastic increase in recent years, thereby increasing the every day traffic congestion problems, especially in metropolitan areas. Governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. Currently, most of the existing vehicle density estimation approaches, such as inductive loop detectors, or traffic surveillance cameras, require very specific infrastructure to be installed on the road. In this paper, we present a novel solution to accurately estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for Vehicular Networks, allows Intelligent Transportation Systems to continuously estimate vehicular density by accounting for the number of beacons received per Road Side Unit (RSU), and also considering the roadmap topology where the RSUs are located. Simulation results reveal that, unlike previous proposals solely based on the number of beacons received, our approach accurately estimates the vehicular density, and therefore our approach can be integrated within automatic traffic controlling systems to predict traffic jams, and thus introducing countermeasures.

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“…For this purpose, we introduce a novel routing algorithm, derived from the existing class of Ant Colony Optimization (ACO) algorithms. ACO has widespread applications in traffic, such as traffic simulation [9], routing and jam avoidance algorithms [10], [11], and traffic assignment [8]. However, most…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization for traffic dispersion routing

Alves

Ast

Cong

et al. 2010

13th International IEEE Conference on Intelligent Transportation Systems

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Abstract-Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for combinatorial optimization problems. This paper introduces a new type of ACO algorithm that will be used for routing along multiple routes in a network as opposed to optimizing a single route. Contrary to traditional routing algorithms, the Ant Dispersion Routing (ADR) algorithm has the objective of determining recommended routes for every driver in the network, in order to increase network efficiency. We present the framework for the new ADR algorithm, as well as the design of a new cost function that translates the motivations and objectives of the algorithm. The proposed approach is illustrated with a small simulationbased case study for the Singapore Expressway Network.

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Avoiding Traffic Jam Using Ant Colony Optimization - A Novel Approach

Cited by 33 publications

References 4 publications

Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Ant Colony Optimization for traffic dispersion routing

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