Advertisement

An Efficient Monitoring of Real Time Traffic Clearance for an Emergency Service Vehicle Using IOT

  • P. Gowtham
  • V. P. Arunachalam
  • V. A. Vijayakumar
  • S. Karthik
Article
  • 35 Downloads
Part of the following topical collections:
  1. Special Issue on Emerging Technology for Software Defined Network Enabled Internet of Things

Abstract

In this paper, a real time emergency vehicle tracking through architecture of instinctive emergency recognition system with high-intensity digital camera it is located in a national high way traffic signal. In a recent survey, there are thousands of people losing their lives due to the delay in the emergency services. Resent survey say that more than 4000 heart attack victims can be saved each year if the delay could be minimized and in the present scenario the number of deaths is in lakhs and this number can be effectively reduced by providing timely and accurate emergency service all the way through avoiding the unnecessary time delay near traffic jams during an emergency situation (Tagne et al. in IEEE Trans Intell Transp Syst 17(3):796–809, 2015). This method clarifies the modeling and working of different units of the emergency vehicle identification system such that optimized emergency vehicle tracking algorithm, with the traffic supervision unit. In this article confer the basic components and their function such that internet of things and their dissimilar layers of protocol, raspberry pi and its architecture, with interfacing sensors such as Siren sound detect (REES-52), the Wireless sensor (NRF905se). It is direct and the most efficient route that is asphalt construct to the central server checks for the location of the vehicle and change the traffic signal. When the emergency vehicle is approaching the traffic lights. The system generate in sequence regarding the traffic emergency situations such as ambulance and siren sound (Emergency Services Review: A comparative review of international ambulance service best practice. http://www.aace.org.uk. Accessed 28 July 2017, 2017). The processed information can be used to divert the live traffic as desirable to avoid the problems related to real time road traffic (Sundar et al. in Sens J IEEE 15(2):1109–1113, 2015).

Keywords

Emergency vehicle recognition Vehicle classification Objects tracking GPS device Traffic control IOT based automated traffic signals Mote sensors 

References

  1. 1.
    Tagne, G., Talj, R., Charara, A.: Design and comparison of robust nonlinear controllers for the lateral dynamics of intelligent vehicles. IEEE Trans. Intell. Transp. Syst. 17(3), 796–809 (2015)CrossRefGoogle Scholar
  2. 2.
    Emergency Services Review: A comparative review of international Ambulance Service best practice. http://www.aace.org.uk. Accessed 28 July 2017 (2017)
  3. 3.
    Sundar, R., Hebbar, S., Golla, V.: Implementing intelligent traffic control systemfor congestion control537 ambulance clearance and stolen vehicle detection. Sens. J. IEEE 15(2), 1109–1113 (2015)CrossRefGoogle Scholar
  4. 4.
    Rahman, A.H.A., et al.: Model based detection and tracking of single moving object using laser range finder. In: Fifth International Conference on Intelligent Systems, Modelling and Simulation (ISMS) (2014)Google Scholar
  5. 5.
    Küçükoğlu, İ., Öztürk, N.: An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows. Comput. Ind. Eng. 86, 60–68 (2015)CrossRefGoogle Scholar
  6. 6.
    Tak, S., Woo, S., Yeo, H.: Study on the framework of hybrid collision warning system using loop detectors and vehicle information. Transp. Res. C Emerg. Technol. 73, 202–218 (2016)CrossRefGoogle Scholar
  7. 7.
    Sireesha, E., Rakesh, D.: Intelligent traffic light system to prioritized emergency purpose vehicles based on wireless sensor network. Int. J. Res. Stud. Sci. Eng. Technol. 1, 24–27 (2014)Google Scholar
  8. 8.
    Reshma, R., Ramesh, T.K.: Security incident management in ground transportation system using UAVs. IEEE, 978-1-4799-7849-6/15/$31.00 ©2015 IEEEGoogle Scholar
  9. 9.
    Ruhnau, P., Vogler, A.: Camera system. In: Particular for a Vehicle, and Method for Ascertaining Pieces of Image Information of a Detection Area. Patent No. US 9,720,148 B2, 1 Aug 2017Google Scholar
  10. 10.
    Erol, M.H., Bulut, F.: Real-time application of travelling salesman problem using Google Maps API. In: Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), 2017. IEEE (2017)Google Scholar
  11. 11.
    Cui, H., Liu, W., et al.: School of Automation, Hangzhou Dianzi University, Zhejiang 310018, China. “A hypersonic vehicle tracking algorithm based on the UKF generalized labeled multi-Bernoulli filter”. Accession Number: 16265337, IEEE Conference, 27–29 July (2016)Google Scholar
  12. 12.
    Yang, H., Rakha, H., Ala, M.V.: Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Trans. Intell. Transp. Syst. 18(6), 1–11 (2016)CrossRefGoogle Scholar
  13. 13.
    Kassir, M.M., Palhang, M.: A region based CAM shift tracking with a moving camera. In: Proceeding of the 2nd RSI/ISM International Conference on Robotics and Mechatronics, 15–17 October (2014)Google Scholar
  14. 14.
    Zargayouna, M., Balbo, F., Ndiaye, K.: Generic model for resource allocation in transportation application to urban parking management. Transp. Res. C Emerg. Technol. 71, 538–554 (2016)CrossRefGoogle Scholar
  15. 15.
    Lee, S.J., Tewolde, G., Kwon, J.: Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application. In: 2014 IEEE World Forum on Internet of Things (WiF-IoT), pp. 353–358, 6–8 March (2014)Google Scholar
  16. 16.
    Tarapiah, S., Atalla, S., Alsayid, B.: Smart on-board transportation management system geo-casting featured. In: World Congress on Computer Applications and Information Systems (WCCAIS), pp. 1–6, 17–19 Jan (2014)Google Scholar
  17. 17.
    Fogue, M., et al.: A system for automatic notification and severity estimation of automotive accidents. IEEE Trans. Mob. Comput. 13(5), 948–963 (2014)CrossRefGoogle Scholar
  18. 18.
    Krishna, A.S., Asif Hussain, S.: Smart vehicle security and defending against collaborative attacks by malware. Int. J. Eng. Sci. Comput. (2015).  https://doi.org/10.4010/2015.444 CrossRefGoogle Scholar
  19. 19.
    Yuan, Y., Xiong, Z., Wang, Q.: An incremental framework for video-based traffic sign detection tracking and recognition. IEEE Trans. Intell. Transp. Syst. 18(7), 1918–1929 (2017)CrossRefGoogle Scholar
  20. 20.
    Qin, J., Ye, Y.: The emergency vehicle routing problem with uncertain demand under sustainability environments. Open Access Sustain. 9(2), 288 (2017).  https://doi.org/10.3390/su9020288 CrossRefGoogle Scholar
  21. 21.
    Wang, X., Xu, L., Sun, H., Xin, J.M., Zheng, N.: On-road vehicle detection and tracking using MMW radar and monovision fusion. IEEE Trans. Intell. Transp. Syst. 17(7), 2075–2084 (2016)CrossRefGoogle Scholar
  22. 22.
    Kim, Y.-J., Hong, J.-S.: Urban traffic flow prediction system using a multifactor pattern recognition model. IEEE Trans. Intell. Transp. Syst. 16(5), 2744–2755 (2015)CrossRefGoogle Scholar
  23. 23.
    Kaur, H., Sahni, V., Bala, M.: Survey of reactive and hybrid routing protocols in MANET: a review IJCSIT. Int. J. Comput. Sci. Inf. Technol. 4(3), 498–500 (2013)Google Scholar
  24. 24.
    Isaloo, M., Azimifar, Z.: Anomaly detection on traffic videos based on trajectory simplification. In: IEEE 8th Iranian Conference on Machine Vision and Image Processing (MVIP), pp. 200–203 (2013)Google Scholar
  25. 25.
    Satzoda, R.K., Trivedi, M.M.: Multipart vehicle detection using symmetry-derived analysis and active learning. IEEE Trans. Intell. Transp. Syst. 17(4), 926–937 (2016)CrossRefGoogle Scholar
  26. 26.
    Zhou, P., Zheng, Y., Li, M.: Predicting bus arrival time with mobile phone based participatory sensing. IEEE Trans. Mob. Comput. 13(6), 1228–1241 (2014)CrossRefGoogle Scholar
  27. 27.
    Thupalli, N.K.: Microphone Array System for Speech Enhancement in Laptop’s. Blekinge Tekniska Högskola, Karlskrona (2012)Google Scholar
  28. 28.
    Permananda, B.S., Uma, B.V.: Speech enhancement algorithm to reduce the effect of background noise in mobile phones. Int. J. Wirel. Mobile Netw. 5(1), 177 (2013)CrossRefGoogle Scholar
  29. 29.
    Hebbar, S., Pattar, P., Golla, V.: A mobile zigbee module in a traffic control system. Potentials IEEE 35(1), 19–23 (2016)CrossRefGoogle Scholar
  30. 30.
    Thirukrishna, J.T., Karthick, S., Arunachalam, V.P.: Revamp energy efficiency in homogeneous wireless sensor networks using optimized radio energy algorithms and power-aware distance source routing protocol. Future Gener. Comput. Syst. 81, 331–339 (2018).  https://doi.org/10.1016/j.future.2017.11.042 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • P. Gowtham
    • 1
  • V. P. Arunachalam
    • 2
  • V. A. Vijayakumar
    • 2
  • S. Karthik
    • 2
  1. 1.Department of Electronics and Communication EngineeringJITCoimbatoreIndia
  2. 2.Department of Computer Science EngineeringSNSCTCoimbatoreIndia

Personalised recommendations