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access icon free Decentralised intelligent transport system with distributed intelligence based on classification techniques

© The Institution of Engineering and Technology
Received 15/02/2016, Accepted 05/09/2016, Revised 01/09/2016, Published 06/09/2016

This study is focused on a decentralised intelligent transportation systems with distributed intelligence based on classification techniques. The rationale behind this architecture is to offer a fully distributed, flexible and scalable system. The architecture encompasses the entire process of capture and management of available road data, enabling the generation of services to promote transportation efficiency. Besides that, thanks to the embedded classification techniques, the system is capable of predicting and reacting to certain events, facing them in an appropriate way. The aim of this work is to demonstrate how the system works in two different real-world use cases. To achieve this objective, how the architecture acts to deal with some incidences is proven. In addition, both use cases serve to show the effective communication between the different components of the system. Besides this, this work demonstrates the fundamental role played by the artificial intelligence techniques working in the system. The well-known C4.5 algorithm has been used for the accurate prediction of traffic congestion and pollution level. The authors explain in this work the reasons for using this classification technique, and the previous experiments performed.

Inspec keywords: pattern classification; artificial intelligence; distributed processing; traffic engineering computing; intelligent transportation systems

Other keywords: C4.5 algorithm; available road data management; artificial intelligence techniques; decentralised intelligent transport system; pollution level prediction; distributed intelligence; available road data capture; traffic congestion prediction; classification techniques

Subjects: Expert systems and other AI software and techniques; Traffic engineering computing; Data handling techniques; Distributed systems software

References

    1. 1)
      • 21. Rebentrost, P., Mohseni, M., Lloyd, S.: ‘Quantum support vector machine for big data classification’, Phys. Rev. Lett., 2014, 113, (13), p. 130503.
    2. 2)
      • 17. Steinwart, I., Christmann, A.: ‘Support vector machines’ (Springer Science & Business Media, 2008).
    3. 3)
      • 12. Moreno, A., Onieva, E., Perallos, A., et al: ‘Cooperative decision-making its architecture based on distributed rsus’, in García-Chamizo, J.M., Fortino, G., Ochoa, S.F. (Eds.) ‘Ubiquitous computing and ambient intelligence. Sensing, processing, and using environmental information’ (Springer, 2015), pp. 84–90.
    4. 4)
      • 34. Osaba, E., Yang, X.S., Diaz, F., et al: ‘An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems’, Eng. Appl. Artif. Intell., 2016, 48, pp. 59–71.
    5. 5)
      • 2. Weiß, C.: ‘V2x communication in Europe – from research projects towards standardization and field testing of vehicle communication technology’, Comput. Netw., 2011, 55, (14), pp. 3103–3119.
    6. 6)
      • 27. Cortes, C., Vapnik, V.: ‘Support-vector networks’, Mach. Learn., 1995, 20, (3), pp. 273–297.
    7. 7)
      • 7. Compass4D. Available at http://www.compass4d.eu/en/about/, accessed on 18 July 2016.
    8. 8)
      • 20. Goodman, K.E., Lessler, J., Cosgrove, S.E., et al: ‘A clinical decision tree to predict whether a bacteremic patient is infected with an esbl-producing organism’, Clin. Infect. Dis., 2016, 63, (7), pp. 896–903.
    9. 9)
      • 22. Quinlan, J.R.: ‘Discovering rules by induction from large collections of examples’, in Michie, D. (Ed.): ‘Expert systems in the micro electronic age’ (Edinburgh University Press, 1979).
    10. 10)
      • 33. Fraternali, P., Rossi, G., Sánchez-Figueroa, F.: ‘Rich internet applications’, IEEE Internet Comput., 2010, 14, (3), pp. 9–12.
    11. 11)
      • 1. Weiland, R.J., Purser, L.B.: ‘Intelligent transportation systems’, in Transp. in the New Millennium, (Transportation Research Board, Washington, DC, USA, 2000).
    12. 12)
      • 8. SAFESPOT. Available at http://www.safespot-eu.org, accessed on 18 July 2016.
    13. 13)
      • 24. Yao, Z., Liu, P., Lei, L., et al: ‘R-c4. 5 decision tree model and its applications to health care dataset’. Int. Conf. on Services Systems and Services Management, 2005, Proc. of ICSSSM'05, 2005, vol. 2, pp. 1099–1103.
    14. 14)
      • 29. Gray, J.B., Fan, G.: ‘Classification tree analysis using target’, Comput. Stat. Data Anal., 2008, 52, (3), pp. 1362–1372.
    15. 15)
      • 16. Wang, S.C.: ‘Artificial neural network’, in Wang, S.C. (Ed.): ‘Interdisciplinary computing in java programming’ (Springer, 2003), pp. 81–100.
    16. 16)
      • 37. Kennedy, J., Eberhart, R.: ‘Particle swarm optimization’. Proc. of IEEE Int. Conf. on Neural Networks, Perth, Australia, 1995, vol. 4, pp. 1942–1948.
    17. 17)
      • 4. Toulminet, G., Boussuge, J., Laurgeau, C.: ‘Comparative synthesis of the 3 main European projects dealing with cooperative systems (cvis, safespot and coopers) and description of coopers demonstration site 4’. Int. IEEE Conf. on Intelligent Transportation Systems, 2008, pp. 809–814.
    18. 18)
      • 6. CVIS: Cooperative vehicle-infrastructure systems. Available at http://www.ecomove-project.eu/links/cvis/, accessed on 18 July 2016.
    19. 19)
      • 35. Osaba, E., Yang, X.S., Diaz, F., et al: ‘A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy’, Soft Comput., 2016, pp. 1–14.
    20. 20)
      • 28. Rastogi, R., Shim, K.: ‘Public: a decision tree classifier that integrates building and pruning’. VLDB, 1998, vol. 98, pp. 24–27.
    21. 21)
      • 30. Mansoori, E.G., Zolghadri, M.J., Katebi, S.D.: ‘Sgerd: a steady-state genetic algorithm for extracting fuzzy classification rules from data’, IEEE Trans. Fuzzy Syst., 2008, 16, (4), pp. 1061–1071.
    22. 22)
      • 11. Quinlan, J.R.: ‘C4.5: programs for machine learning’ (Elsevier, 2014).
    23. 23)
      • 18. Quinlan, J.R.: ‘Induction of decision trees’, Mach. Learn., 1986, 1, (1), pp. 81–106.
    24. 24)
      • 15. Kotsiantis, S.: ‘Supervised machine learning: A review of classification techniques’. Proc. of the 2007 Conf. on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, 2007, pp. 3–24.
    25. 25)
      • 25. Adhatrao, K., Gaykar, A., Dhawan, A., et al: ‘Predicting students’ performance using id3 and c4. 5 classification algorithms’, Int. J. Data Mining Knowl. Manage. Process, 2013, 3, (5), p. 39.
    26. 26)
      • 14. Bailey, K.: ‘Typologies and taxonomies: an introduction to classification techniques’ (Sage Publications, Newbury Park, CA, 1994).
    27. 27)
      • 26. Onieva, E., Lopez-Garcia, P., Masegosa, A., et al: ‘A comparative study on the performance of evolutionary fuzzy and crisp rule based classification methods in congestion prediction’, Transp. Res. Proc., 2016, 14, pp. 4458–4467.
    28. 28)
      • 36. Goldberg, D.: ‘Genetic algorithms in search, optimization, and machine learning’ (Addison-Wesley Professional, 1989).
    29. 29)
      • 10. DRIVE-C2X: Accelerate cooperative mobility. Available at http://www.drive-c2x.eu/project, accessed on 18 July 2016.
    30. 30)
      • 5. Alexander, P., Haley, D., Grant, A.: ‘Cooperative intelligent transport systems: 5.9-ghz field trials’, Proc. IEEE, 2011, 99, (7), pp. 1213–1235.
    31. 31)
      • 3. Papadimitratos, P., La Fortelle, A., Evenssen, K., et al: ‘Vehicular communication systems: enabling technologies, applications, and future outlook on intelligent transportation’, IEEE Commun. Mag., 2009, 47, (11), pp. 84–95.
    32. 32)
      • 9. COMeSafety2. Available at http://www.ecomove-project.eu/links/comesafety/, accessed on 18 July 2016.
    33. 33)
      • 13. Dunham, M.H.: ‘Data mining: Introductory and advanced topics’ (Pearson Education India, 2006).
    34. 34)
      • 23. Mašetic, Z., Subasi, A.: ‘Detection of congestive heart failures using c4. 5 decision tree’, Southeast Eur. J. Soft Comput., 2013, 2, (2), pp. 74–77.
    35. 35)
      • 19. Razavi, B.S.: ‘Predicting the trend of land use changes using artificial neural network and Markov chain model (case study: Kermanshah city)’, Res. J. Environ. Earth Sci., 2014, 6, (4), pp. 215–226.
    36. 36)
      • 31. Carvalho, D.R., Freitas, A.A.: ‘A hybrid decision tree/genetic algorithm method for data mining’, Inf. Sci., 2004, 163, (1), pp. 13–35.
    37. 37)
      • 32. Derrac, J., Garca, S., Molina, D., et al: ‘A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms’, Swarm Evol. Comput., 2011, 1, (1), pp. 3–18.
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