Skip to main content
SpringerLink
Log in

Computational Intelligence in Intelligent Transportation Systems: An Overview

  • Chapter
  • First Online:
Innovative Trends in Computational Intelligence

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Computational intelligence refers to the ability of a computing device or machine to learn specific tasks based on its data and experience. Computational intelligence is invoked as an alternative solution to computing problems given that the problem resolution is complex due to uncertainties involved. The application of computational intelligence techniques to various domains of application is gaining popularity due to its capability of providing human-like knowledge, such as cognition, recognition, understanding, learning and others. The solution, therefore, involves some methodologies that are present in the natural world, in the bio-inspired world. The solution performs task in the same way as human beings do, such as natural computation, artificial immune system, fuzzy logic systems and artificial neural networks. This paper is an overview of computational intelligence as applied to intelligent transportation systems. The intelligence in transportation refers to innovation in methodologies, resulting in better performance and efficiency, or creation of additional services. This research domain is in infancy stage, and the goal of the paper is to contribute to its advancement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Government of Taiwan, Brief introduction to Intelligent Transportation System, ITS (2020). Accessed: 8 July 2020. [Online]. Available: https://www.freeway.gov.tw/UserFiles/File/Traffic/A1%20Brief%20introduction%20to%20Intelligent%20Transportation%20System,%20ITS.pdf"

  2. L. Qi, Research on intelligent transportation system technologies and applications, in Presented at the 2008 Workshop on Power Electronics and Intelligent Transportation System, (Guangzhou, China, 2008)

    Google Scholar 

  3. M. Machin, J.A. Sanguesa, P. Garrido, F.J. Martinez, On the use of artificial intelligence techniques in intelligent transportation systems, in Presented at the IEEE Wireless Communica-tions and Networking Conference Workshops (WCNCW), (Barcelona, Spain, 2018)

    Google Scholar 

  4. European Union, Directive 2010/40/EU of the European Parliament and of the Council of 7 July 2010 on the framework for the deployment of Intelligent Transport Systems in the field of road transport and for interfaces with other modes of transport. (2010). [Online]. Available: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2010:207:0001:0013:EN:PDF.

  5. J.C. Bezdek, Intelligence: Computational versus artificial. IEEE Trans. Neural Netw. 4(5), 737–747 (1993)

    Google Scholar 

  6. DifferenceBetween.net. Difference Between AI and CI. http://www.differencebetween.net/technology/difference-between-ai-and-ci/ Accessed 29 June 2020.

  7. R. Iqbal, F. Doctor, B. More, S. Mahmud, U. Yousuf, Big data analytics: Computational intelligence techniques and application areas. Technol. Forecasting Soc. Change, 1–3 (2018)

    Google Scholar 

  8. W. Duch, What is computational intelligence and where Is it going? Challenges for computational intelligence, in Studies in computational intelligence, ed. by W. Duch, J. Mańdziuk, vol. 63, (Springer Berlin, Heidelberg, 2007)

    Google Scholar 

  9. J.D. Ser, E. Osaba, J.J. Sanchez-Medina, I. Fister, I. Fister, Bioinspired computational intelligence and transportation systems: A long road ahead. IEEE Trans. Intell. Transp. Syst. 21(2), 466–495 (2020). https://doi.org/10.1109/TITS.2019.2897377

    Article  Google Scholar 

  10. R. Wang, W. Ji, Computational intelligence for information security: A survey. IEEE Trans. Emerging Topics Comput. Intell., 1–14 (2020). https://doi.org/10.1109/TETCI.2019.2923426

  11. P.K. Agarwal, J. Gurjar, A.K. Agarwal, R. Birla, Application of artificial intelligence for development of intelligent transport system in smart cities. Int. J. Transport. Eng. Traffic Syst. 1(1) (2015)

    Google Scholar 

  12. X.Y. Wang, Y.M. Ding, Adaptive real-time predictive compensation control for 6-DOF serial arc welding manipulator. Chinese J. Mech. Eng. 23(3), 361–366 (2010)

    Article  Google Scholar 

  13. R.D. Labati, A. Genovese, E. Munoz, Computational intelligence for industrial and environmental applications, in Presented at the 8th International Conference on Intelligent Systems, (Sofia, Bulgaria, 2016)

    Google Scholar 

  14. Q.K. Al-Shayea, Artificial neural networks in medical diagnosis. Int. J. Comp. Sci. Issues 8(2), 150–154 (2011)

    Google Scholar 

  15. P.K. Kankar, S.C. Sharma, S.P. Harsha, Fault diagnosis of ball bearings using machine learning methods. Expert Syst. Appl. 38(3), 1876–1886 (2011)

    Article  Google Scholar 

  16. J. Schmidhuber, Deep learning in neural networks: An overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  17. S. Kar, S. Das, P.K. Ghosh, Applications of neuro fuzzy systems: A brief review and future outline. Appl. Soft Comput. 15, 243–259 (2014)

    Article  Google Scholar 

  18. G.B. Huang, Q.Y. Zhu, C. Siew, Extreme learning machine: Theory and applications. Neurocomputing 70(1), 489–501 (2006)

    Article  Google Scholar 

  19. O.P. Mahela, A.G. Shaik, N. Gupta, A critical review of detection and classification of power quality events. Renew. Sust. Energ. Rev. 41, 495–505 (2015)

    Article  Google Scholar 

  20. S. Khokhar, A.A.B.M. Zin, A.S.B. Mokhtar, A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renew. Sust. Energ. Rev. 51, 1650–1663 (2015)

    Article  Google Scholar 

  21. R.C. Cavalcante, R.C. Brasileiro, V.L.F. Souza, Computational intelligence and financial markets: A survey and future directions. Expert Syst. Appl. 55, 194–211 (2016)

    Article  Google Scholar 

  22. W. Zhang et al., Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis. Chinese J. Mech. Eng. 30, 782–795 (2017). https://doi.org/10.1007/s10033-017-0150-0

    Article  Google Scholar 

  23. D.H. Ballard, An Introduction to Natural Computation (MIT Press, 2020)

    MATH  Google Scholar 

  24. B. Xue, M. Zhang, W.N. Browne, X. Yao, A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016). https://doi.org/10.1109/TEVC.2015.2504420

    Article  Google Scholar 

  25. M. Mitchell, An Introduction to Genetic Algorithms (MIT Press, 1998)

    Book  Google Scholar 

  26. T. Burczynski, W. Kus, E. Majchrzak, P. Orantek, M. Dziewonski, Evolutionary computation in identification of a tumor, in Presented at the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), (Honolulu, HI, USA, 2002)

    Google Scholar 

  27. Y.-F. Zhu, X.-M. Tang, O.O.S. Intelligence, Overview of swarm intelligence, in Presented at the 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), (2010)

    Google Scholar 

  28. L. Rosenberg et al., Crowds vs swarms, a comparison of intelligence, in Presented at the Swarm/Human Blended Intelligence Workshop (SHBI), (Cleveland, OH, 2016)

    Google Scholar 

  29. L.N.D. Castro, Natural Computing: The Grand Challenges and Two Case Studies. 2020. Accessed 7 July 2020. [Online]. Available: https://www.slideshare.net/lndecastro/2012-natural-computing-the-grand-challenges-and-two-case-studies

  30. I.R. Cohen, Real and artificial immune systems: Computing the state of the body. Nat. Rev. Immunol. 7, 569–574 (2007). https://doi.org/10.1038/nri2102

    Article  Google Scholar 

  31. M. Zyda, Creating a science of games: Introduction. Commun. ACM Spec. Iss. Creat. Sci. Games 50(7), 26–29 (2007)

    Google Scholar 

  32. T. Jitta, Artificial Immune System. 2020. Accessed: 7 July 2020. [Online]. Available: https://www.slideshare.net/TejaswiniJitta/artificial-immune-system-72702061

  33. L.A. Zadeh, Fuzzy logic. Computer 21(4), 83–93 (1988)

    Article  Google Scholar 

  34. S.X. Wu, W. Banzhaf, The use of computational intelligence in intrusion detection systems: A review. Appl. Soft Comput. 10(1), 1–35 (2010)

    Article  Google Scholar 

  35. H. Yakura, S. Shinozaki, R. Nishimura, Y. Oyama, J. Sakuma, Malware analysis of imaged binary samples by convolutional neural network with attention mechanism, in Presented at the Malware Analysis of Imaged Binary Samples by Convolutional Neural Network with Attention Mechanism, (2017)

    Google Scholar 

  36. M. Zheng et al., Steganographer detection based on multiclass dilated residual networks, in ACM International Conference in Multimedia Retrieval, (2018), pp. 300–308

    Google Scholar 

  37. I. Witten, E. Frank, M. Hall, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, Burlington, MA, 2011)

    Google Scholar 

  38. M. Charest, S. Delisle, Ontology-guided intelligent data mining assistance: combining declarative and procedural knowledge, in Presented at the IASTED International Conference, (2006)

    Google Scholar 

  39. S.-H. An, B.-H. Lee, D.-R. Shin, A survey of intelligent transportation systems, in Presented at the 3rd International Conference on Computational Intelligence, (Communication Systems and Networks, Bali, Indonesia, 2011)

    Google Scholar 

  40. R. Rauf, F. Riaz, M.A. Niazi, Computational intelligence in context of autonomous vehicle modeling: a survey, in Presented at the 1st International Conference on Power, (Energy and Smart Grid (ICPESG), Mirpur Azad Kashmir, Pakistan, 2018)

    Google Scholar 

  41. T.-X. Chen, Z.-R. Zhuang, R.-C. Lo, Y.-M. Hong, Outdoor vision-based obstacle avoidance for an autonomous land vehicle using fuzzy logic, in Presented at the 2011 International Conference on System Science and Engineering (ICSSE), (2011)

    Google Scholar 

  42. A. Poursamad, M. Montazeri, Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles. Control. Eng. Pract. 16(7), 861–873 (2008)

    Article  Google Scholar 

  43. W.-C. Wang, C.-C. Tai, S.-J. Wu, Z.-Y. Liu, A hybrid genetic algorithm with fuzzy logic controller for wireless power transmission system of electric vehicles, in Presented at the IEEE International Conference on Industrial Technology (ICIT), (2015)

    Google Scholar 

  44. H. Yin, S. Wong, J. Xu, C. Wong, Urban traffic flow prediction using a fuzzy-neural approach. Transp. Res. Part C Emerg. Technol. 10(2), 85–98 (2002)

    Article  Google Scholar 

  45. C. Quek, M. Pasquier, B.B.S. Lim, POP-TRAFFIC: A novel fuzzy neural approach to road traffic analysis and prediction. IEEE Trans. Intell. Transp. Syst. 7(2), 133–146 (2006)

    Article  Google Scholar 

  46. J. Tang, F. Liu, Y. Zou, W. Zhang, Y. Wang, An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Trans. Intell. Transp. Syst. 18(9), 2340–2350 (2017). https://doi.org/10.1109/TITS.2016.2643005

    Article  Google Scholar 

  47. X. Zhang, E. Onieva, A. Perallos, E. Osaba, V.C. Lee, Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction. Transp. Res. Part C Emerg. Technol. 43, 127–142 (2014)

    Article  Google Scholar 

  48. E.I. Vlahogianni, M.G. Karlaftis, J.C. Golias, Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transp. Res. Part C Emerg. Technol. 13(3), 211–234 (2005)

    Article  Google Scholar 

  49. Y.-C. Chiou, An artificial neural network-based expert system for the appraisal of two-car crash accidents. Accid. Anal. Prev. 38(4), 777–785 (2006)

    Article  Google Scholar 

  50. W.-K. Tseng, S.-S. Liao, An expert system using rbf neural network for estimating vehicle speed based on length of skid mark, in Presented at the International Conference on Natural Computation (ICNC), (2011)

    Google Scholar 

  51. T. Azim, M.A. Jaffar, A.M. Mirza, Fully automated real time fatigue detection of drivers through fuzzy expert systems. Appl. Soft Comput. 18, 25–38 (2014)

    Article  Google Scholar 

  52. M.D. Hina, A. Soukane, A. Ramdane-Cherif, Multimodal fusion and fission techniques for a safe driving environment, in Presented at the 11th ITS European Congress, vol. 6-9, (Glasgow, Scotland, 2016), p. 2016

    Google Scholar 

  53. G. Brioschi, M.D. Hina, A. Soukane, A. Ramdane-Cherif, M. Colombetti, Techniques for cognition of driving context for safe driving application, in Presented at the ICCI*CC 2016, 15th IEEE International Conference on Cognitive Informatics and Cognitive Computing, (Stanford, CA, USA, 2016)

    Google Scholar 

  54. S. Grimm, P. Hitzler, A. Abecker, Knowledge representation and ontologies, in Semantic Web Services: Concepts, Technology and Applications, ed. by R. Studer, S. Grimm, A. Abecker, (Springer, Berlin, Germany, 2007), pp. 51–106

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligen Communicating Systems Laboratory, ECE Paris School of Engineering, Paris, France

    Manolo Dulva Hina & Assia Soukane

  2. LISV Laboratory, Université de Versailles – Paris Saclay, Velizy, France

    Amar Ramdane-Cherif

Authors
  1. Manolo Dulva Hina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Assia Soukane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amar Ramdane-Cherif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manolo Dulva Hina .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studi, Dehradun, India

    Ravi Tomar

  2. INSEEC U. Research Center, ECE Paris School of Engineering, Paris, France

    Manolo Dulva Hina

  3. INSEEC U. Research Center, ECE Paris School of Engineering, Paris, France

    Rafik Zitouni

  4. LISV Laboratory, University of Versailles - Paris-Saclay, Vélizy, France

    Amar Ramdane-Cherif

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hina, M.D., Soukane, A., Ramdane-Cherif, A. (2022). Computational Intelligence in Intelligent Transportation Systems: An Overview. In: Tomar, R., Hina, M.D., Zitouni, R., Ramdane-Cherif, A. (eds) Innovative Trends in Computational Intelligence. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-78284-9_2

Download citation

Publish with us

Policies and ethics

Search

Navigation