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Decision-Making Intelligent System for Passenger of Urban Transports

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Book cover Ubiquitous Computing and Ambient Intelligence (UCAmI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10586))

Abstract

Smart transportation systems have now been implemented in many cities. The implementation of these systems requires having a solid infrastructure and specialized devices. However, the implementation of these systems does not consider the infrastructure of other countries. As result, its implementation can be costly. Specifically, in Mexico, Urban Passenger Transport has few transportation units to meet the demand of the population. Also, these do not provide precise information of arrival times of buses. In this research work, we present a software system that combats the inconveniences of public transportation in Mexico, providing information in real time that will allow the passengers to make informed or correct decisions regarding their journey. The information provided to the passengers will be the availability of seats and the arrival times of the buses.

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References

  1. ITS Canada: Intelligent transportation, ITS Canada (2012). https://www.itscanada.ca/it/

    Google Scholar 

  2. Villanueva, E.Á.: Planeación para la administración de una flotilla de autobuses de transporte público, Instituto Politécnico Nacional (2015)

    Google Scholar 

  3. Notimex: Los problemas de usuarios de transporte público, inseguridad, saturación y largos trayectos (2014). http://www.animalpolitico.com/2014/05/inseguridad-saturacion-y-largos-trayectos-los-problemas-de-usuarios-de-transporte-publico/. Accessed 02 May 2017

  4. Alessio Ishizaka, P.N.: Multi-criteria Decision Analysis: Methods and Software. Wiley, Hoboken (2013)

    Book  Google Scholar 

  5. Chernoff, H., Moses, L.: Elementary Decision Theory. Dover, New York (1987)

    MATH  Google Scholar 

  6. Gammer, N., Cherrett, T., Gutteridge, C.: Disseminating real-time bus arrival information via QRcode tagged bus stops: a case study of user take-up and reaction in Southampton, UK. J. Transp. Geogr. 34, 254–261 (2014)

    Article  Google Scholar 

  7. Yu, B., Lam, W.H.K., Lam, M.: Bus arrival time prediction at bus stop with multiple routes. Transp. Res. C. 19(6), 1157–1170 (2011)

    Article  Google Scholar 

  8. Li, J., Gao, J., Yang, Y., Wei, H.: Bus arrival time prediction based on mixed model. China. Commun. 14, 38–47 (2015)

    Google Scholar 

  9. Zhou, P., Zheng, Y., Li, M.: “How long to wait? predicting bus arrival time with mobile phone based participatory sensing. In: Proceeding of 10th International Conference on Mobile Systems, Applications, and Services - MobiSys 2012, vol. 13, pp. 379–392 (2012)

    Google Scholar 

  10. Wang, L., Zuo, Z., Fu, J.: Bus arrival time prediction using RBF neural networks adjusted by online data. Procedia Soc. Behav. Sci. 138, 67–75 (2014)

    Article  Google Scholar 

  11. Edison, K., Ferris, B., Borning, A., Rutherford, G.S., Layton, D.: Where is my bus ? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Transp. Res. A. 45(8), 839–848 (2011)

    Google Scholar 

  12. Emmanouilidis, C., Koutsiamanis, R.-A.: Mobile guides: taxonomy of architectures, context awareness, technologies and applications. J. Netw. Comput. Appl. 36(1), 103–125 (2013)

    Article  Google Scholar 

  13. Perera, C., Member, S., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things : a survey. IEEE. Commun. Surv. Tutor. 16(1), 414–454 (2014)

    Article  Google Scholar 

  14. Cook, D.J., Augusto, J.C.: Ambient intelligence: technologies, applications, and opportunities. Pervasive. Mob. Comput. 5(4), 277–298 (2009)

    Article  Google Scholar 

  15. Synott, G., Chen, J., Nugent, L., Moore, C.D.: Flexible and customizable visualization of data generated within intelligent environments. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (2012). doi:10.1109/EMBC.2012.6347317

    Google Scholar 

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Acknowledgments

This research work has been partially funded by European Commission and CONACYT, through the SmartSDK project.

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Correspondence to Pedro Wences .

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Wences, P., Martinez, A., Estrada, H., Gonzalez, M. (2017). Decision-Making Intelligent System for Passenger of Urban Transports. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-67585-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67584-8

  • Online ISBN: 978-3-319-67585-5

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