Ant colony inspired algorithm for adaptive traffic routing

Krömer, Pavel; Martinovič, Jan; Radecký, Michal; Tomis, Radek; Snášel, Václav

doi:10.1109/nabic.2011.6089614

Cited by 11 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In future work, we will be implementing this to our own version of routing, which is the combination of Highway Hierarchies [7,8] and heuristics [5,4] and also to our approach of road network updating during accidents. We presented earlier in [2]. Next step is preparation of other types of indexes for routing.…”

Section: Resultsmentioning

confidence: 99%

Using Extended Raster File for Real Time Traffic Information Mining

Radecký

Martinovič

Fedorčák

et al. 2012

Computer Information Systems and Industrial Management

Self Cite

View full text Add to dashboard Cite

Abstract. Gauging and analyzing the ever-growing strain on todays roadways is an issue that needs to be adressed with urgency. The rate at which technology is developing and the amount of vehicles now equipped with GPS systems are factors that ensure the potential for creating statistics to gauge this strain. When processing data gathered from monitored vehicles, it is necessary to implement procedures that identify specific roadways upon which a given vehicles movement is recorded. With respects to the volume of this data, a method for indexing these data files becomes a critical issue. Within the following text, we will present a process of collecting and processing data in the FLOREON+ Traffic system and spacial indexing using a raster index that processes queries at a much greater speed than standard indexing.

show abstract

Section: Resultsmentioning

confidence: 99%

Using Extended Raster File for Real Time Traffic Information Mining

Radecký

Martinovič

Fedorčák

et al. 2012

Computer Information Systems and Industrial Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…With this information and the traffic infrastructure, different routes are computed iteratively, trying to minimize the probability that the car will get stuck in a traffic jam. Krömer et al (2011) also point out the need for handling dynamic routing problems for traffic routing. They also point out that collecting real-world data can be a complex task that often has limitations in the amount of data and the area covered.…”

Section: Ant-based Routing Algorithmsmentioning

confidence: 99%

“…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: 99%

“…Of course, a simple approach is to assume a fixed average speed that can be used for every road and to use this in the calculation of the weights of the road network. It has turned out that this simple approach often can help, but this also turned out not to be sufficient for roads whose load changes in time (Ehmke & Mattfeld, 2011;Krömer, Martinovic, Radecký, Tomis, & Snásel, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, the dynamic problem is reformulated to a 78 static one that has a larger complexity than the initial static one, as for each edge in the routing road network, time-dependent traveling speed information is available. But it turns out that the data acquisition takes a lot of effort (Ehmke & Mattfeld, 2011;Krömer et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract