Advertisement

iTRANS: Proactive ITS Based on Drone Technology to Solve Urban Transportation Challenge

  • Luis E. FerrerasEmail author
Chapter
Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

Transportation has become the latest field for disrupting innovation, as were telecommunications or computers in the last decades. We are seeing significant advances in the development of autonomous or semi-autonomous vehicles and an immense surge of shared mobility services, such as bike-sharing and ride-sourcing services like Uber- all promising to improve the life of the average urban commuter. There are significant obstacles to the deployment and/or full utilization of these technologies, however, which could be mitigated with an additional layer of transportation system monitoring. This chapter introduces the use of unmanned aircraft vehicles (UAV), or drones, as the cornerstone of an advanced and proactive new intelligent transportation system (ITS) called, iTRANS. This new approach will reveal important traffic variables that are currently unpredictable, as well as solve most jurisdictional conflicts of interest, and regulatory constraints. Furthermore, iTRANS is designed to be one of the main tools to complete a successful transition from a dysfunctional transportation system to an optimal linear programming one in which transportation supply and demand is proactively managed through advanced ITS software. This sophisticated ITS system, connected to open software platforms, would gather and integrate all available information from the different modes of transportation, allowing real-time traffic management of the entire transportation system, as a whole. In addition, this chapter addresses the current technologies, urban transportation challenges related to autonomous vehicles, and describes the multifaceted approach of iTRANS and how its application would be advantageous in the deployment of autonomous vehicles. In a nutshell, this provides a systematic approach that 21st century engineers could use to create more eco-friendly, affordable, safe and sustainable transportation environments.

Keywords

Unmanned aerial systems (UAS) Intelligent transportation systems (ITS) platform Artificial intelligent (AI) itrans 

References

  1. 1.
    Shladover, S.: Cooperative (rather than autonomous) vehicle-highway automation systems. IEEE Intell. Transp. Syst. Mag. 10–19 (2009)Google Scholar
  2. 2.
    Schrank, D., Eisel, B., Lomax, T., Bak, J.: Urban mobility scorecard. Texas A&M Transportation Institute and INRIX, August 2015. http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-scorecard-2015.pdf, accessed March 1, 2016
  3. 3.
    U.S. Department of Transportation: Federal Highway Administration 2010. 2010 Urban Congestion Trends, Enhancing System Reliability with Operations. http://ops.fhwa.dot.gov/publications/fhwahop11024/index.htm. Accessed 11 Jan 2012
  4. 4.
    Ilas, C.: Electronic sensing technologies for autonomous ground vehicles: a review. In: The 8th International Symposium on Advanced Topics in Electrical Engineering, IEEE, Bucharest, Romania, 23–25 May 2013Google Scholar
  5. 5.
    Amukele, T.K.,; Sokoll, L.J., Pepper, D., Howard, D.P., Street, J.: Can unmanned aerial systems (Drones) be used for the routine transport of chemistry, hematology, and coagulation laboratory specimens?, PLoS ONE (2015)Google Scholar
  6. 6.
    Ian, G.R.S.: The rise of the predator empire: tracing the history of U.S. drones, understanding empire, 2014. https://understandingempire.wordpress.com/2-0-a-brief-history-of-u-s-drones. Accessed 1 Mar 2016
  7. 7.
    Center for the Study of the Drone: Drones in the defense budget, Bard College, 2015. http://dronecenter.bard.edu/drones-in-the-defense-budget. Accessed 1 Mar 2016
  8. 8.
    Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521, 460–466 (2015)CrossRefGoogle Scholar
  9. 9.
    Mueller, M.W., Hehn, M., D’Andrea, R.: A computationally efficient motion primitive for quadrocopter trajectory generation. Institute for Dynamic Systems and Control, ETH Zurich, IEEE (2015)Google Scholar
  10. 10.
    Brooks, C., Roussi, C., Colling, T.: Unpaved roads assessment. Michigan Technological University (MTU), characterization of unpaved road conditions through the use of remote sensing. www.mtri.org/unpaved, 1 Mar 2016
  11. 11.
    Urmson, C., Whittaker, W.: Red, Self-Driving Cars and the Urban Challenge, pp. 66–68. IEEE Computer Society, March–April 2008Google Scholar
  12. 12.
    Srini, V.P.: A Vision for Supporting Autonomous Navigation in Urban Environments, pp. 68–77. IEEE Computer Society, Dec 2006Google Scholar
  13. 13.
    Asaro, P., Calo, R., Walker Smith, B.: Autonomous driving. center for internet and society, Stanford University, http://cyberlaw.stanford.edu/wiki/index.php/Automated_Driving:_Legislative_and_Regulatory_Action. Accessed 1 Mar 2016
  14. 14.
    Driverless Car Market: Top misconceptions of autonomous cars and self-driving vehicles. www.driverless-future.com. Accessed 26 Feb 2016
  15. 15.
    Cheng, X., Yang, L., Shen, X., D2D for intelligent transportation systems: a feasibility study. IEEE Trans. Intell. Transp. Syst. 16(4) (2015)Google Scholar
  16. 16.
    Dokic, J., Muller, B., Meyer, G.: European roadmap smart systems for automated driving. European Technology Platform on Smart System Integration, April 2015Google Scholar
  17. 17.
    Albino, V., Berardi, U., Dangelico, R.M.: Smart cities: definitions, dimensions, performance, and initiatives. J. Urban Technol. (2015)Google Scholar
  18. 18.
    Chun, B.: A study on intelligent traffic system related with smart city. Int. J. Smart Home 9(7), 223–230 (2015)CrossRefGoogle Scholar
  19. 19.
    Farkas, K., Feher, G., Benczur, A., Sidlo, C.: Crowdsending based public transport information service in smart cities. IEEE Commun. Mag. 158–165, Aug 2015Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Parsons Transportation GroupOrlandoUSA

Personalised recommendations