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Polibits

On-line version ISSN 1870-9044

Polibits  n.51 México Jan./Jun. 2015

https://doi.org/10.17562/PB-51-4 

Mobile ACORoute-Route Recommendation Based on Communication by Pheromones

 

Carla S. G. Pires, Marilton S. de Aguiar, and Paulo R. Ferreira

 

Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas (UFPEL), Pelotas, RS, Brazil (e-mail: carlasmpires@gmail.com, marilton@inf.ufpel.edu.br, paulo.ferreira.jr@gmail.com).

 

Manuscript received on December 19, 2014,
Accepted for publication on April 7, 2015,
Published on June 15, 2015.

 

Abstract

Urban mobility problems affects the vast majority of cities nowadays. Thus, systems that provide real time information to assist in planning routes and choosing the most appropriate paths are essential to make transport more effective. As an alternative solution to problems related to mobility in cities, there are the so-called Intelligent Transportation Systems (ITS) which include the Route Recommendation Systems (RRS) and methodologies for congestion prediction that combine Information and Communication Technology (ICT) with Artificial Intelligence (AI) technology to improve the quality of transport systems. In this context, this work proposes the use of pheromone-based communication for building an ITS that offers information about real time traffic flow, taking into account the mobility of vehicles and passengers and the traffic dynamics. The general goal is to provide an Android solution able to suggest users routes calculated by the hybrid algorithm between A* and pheromone mechanism. The idea is to avoid areas of heavy traffic congestion.

Key words: Route recommendation systems, intelligent transportation systems, pheromone-based communication.

 

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REFERENCES

[1] J. Wahle, O. Annen, C. Schuster, L. Neubert, and M. Schreckenberg, "A dynamic route guidance system based on real traffic data," European Journal of Operational Research, vol. 131, no. 2, pp. 302-308, 2001, artificial Intelligence on Transportation Systems and Science. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0377221700001302        [ Links ]

[2] B. Ferris, K. Watkins, and A. Borning, "Location-aware tools for improving public transit usability," Pervasive Computing, IEEE, vol. 9, no. 1, pp. 13-19, January-March 2010.         [ Links ]

[3] S. Kurihara, "Traffic-congestion forecasting algorithm based on pheromone communication model," Ant Colony Optimization - Techniques and Applications, vol. 104, pp. 167-175, 2013. [Online]. Available: http://www.academia.edu/2613986/ANT_COLONY_OPTIMIZATION_-_TECHNIQUES_AND_APPLICATIONS        [ Links ]

[4] O. Masutani, Y. Ando, H. Sasaki, H. Iwasaki, Y. Fukazawa, and S. Honiden, "Pheromone model: Application to traffic congestion prediction," in Engineering Self-Organising Systems, ser. Lecture Notes in Computer Science, S. Brueckner, G. Marzo Serugendo, D. Hales, and F. Zambonelli, Eds., vol. 3910. Springer Berlin Heidelberg, 2006, pp. 182-196. [Online]. Available: http://dx.doi.org/10.1007/11734697_14        [ Links ]

[5] W. Narzt, U. Wilflingseder, G. Pomberger, D. Kolb, and H. Hortner, "Self-organising congestion evasion strategies using ant-based phero-mones," let Intelligent Transport Systems, vol. 4, 2010.         [ Links ]

[6] J. Ochiai and H. Kanoh, "Hybrid ant colony optimization for real-world delivery problems based on real time and predicted traffic in wide area road network," Fourth International conference on Computer Science and Information Technology - CCSIT 2014, vol. 4, no. 2, February 2014. [Online]. Available: http://airccse.org/V4N19.html        [ Links ]

[7] J. L. Adler and V. J. Blue, "Toward the design of intelligent traveler information systems," Transportation Research Part C: Emerging Technologies, vol. 6, no. 3, pp. 157-172, 1998. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0968090X98000126        [ Links ]

[8] I. Steinmacher, V. Vieira, A. C. Salgado, P. Tedesco, V. Times, C. Ferraz, E. Huzita, and A. P. Chaves, "The UbiBus project: Using context and ubiquitous computing to build advanced public transportation systems to support bus passengers," VIII Simposio Brasileiro de Sistemas de Informa(:áao, 2012. [Online]. Available: http://www.cin.ufpe.br/~ubibus/publications.html        [ Links ]

[9] A. Tito, F. Borgiani, R. dos Santos, P. Tedesco, and A. Salgado, "Contextual information in user information systems in public transportation: A systematic review," in 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), sept. 2012, pp. 361-366.         [ Links ]

[10] C. Blum and D. Merkle, Swarm Intelligence: Introduction and Applications, 1st ed. Springer Publishing Company, Incorporated, 2008.         [ Links ]

[11] D. Teodorovic, "Swarm intelligence systems for transportation engineering: Principles and applications," Transportation Research Part C: Emerging Technologies, vol. 16, no. 6, pp. 651-667, 2008. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0968090X08000272        [ Links ]

[12] R. Hoar, J. Penner, and C. Jacob, "Evolutionary swarm traffic: if ant roads had traffic lights," in Proceedings of the 2002 Congress on Evolutionary Computation, CEC'02, vol. 2, 2002, pp. 1910-1915.         [ Links ]

[13] D. S. dos Santos and A. L. Bazzan, "Distributed clustering for group formation and task allocation in multiagent systems: A swarm intelligence approach," Applied Soft Computing, vol. 12, no. 8, pp. 2123-2131, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1568494612001044        [ Links ]

[14] P. Lucic and D. Teodorovic, "Transportation modeling: An artificial life approach," in Proceedings ofthe 14th IEEE International Conference on Tools with Artificial Intelligence, ser. ICTAI'02. Washington, DC, USA: IEEE Computer Society, 2002, pp. 216-. [Online]. Available: http://dl.acm.org/citation.cfm?id=850952.853815        [ Links ]

[15] H. J. Barbosa, Ed., Ant Colony Optimization - Techniques and Applications. Croatia: InTech Chapters published, 2013.         [ Links ]

[16] Y. Ando, Y. Fukazawa, O. Masutani, H. Iwasaki, and S. Honiden, "Performance of pheromone model for predicting traffic congestion," in Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, ser. AAMAS'06. New York, NY, USA: ACM, 2006, pp. 73-80. [Online]. Available: http://doi.acm.org/10.1145/1160633.1160642        [ Links ]

[17] "Waze mobile," https://www.waze.com/wiki/How_Waze_calculates_routes, Waze Ltd., 2013, access date: 03 jan. 2014.         [ Links ]

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