Smart traffic management system with real time analysis

  • Authors

    • Sheena Mariam Jacob
    • Shobha Rekh
    • Manoj G
    • J John Paul
    https://doi.org/10.14419/ijet.v7i3.29.19190
  • Cloud, Image Processing, Raspberry Pi, Traffic Congestion, Ultrasonic Sensors
  • Abstract

    This paper aims to overcome traffic congestion caused by ineffective traffic management systems that are outdated and work on a predefined countdown. These traditional systems allot timings irrespective of the actual density in traffic on a specific road thereby causing large red light delays. The system we propose ensures traffic lights respond to real time values of traffic, thereby allowing proper management of time and resources. In order to do this we first calculate the density of traffic which is determined using a combination of ultrasonic sensors and image processing techniques. This information is processed by a Raspberry Pi, which in turn controls the traffic light indicators. In addition to that, the data that is collected is sent to the cloud, and can be used to monitor traffic flow at periodic intervals. In case of sensor system failure, the values stored in the cloud will also be useful in predicting the density of traffic based on long term periodic analysis.

     

     

  • References

    1. [1] Author “Title of the Paperâ€, Journal name, Vol. X, No. X, (200X), pp. XX-XX, available online: http://xxx, last visit:28.02.2013

      [2] Author, â€Title of the Paperâ€, Proceedings of the conference name, Vol. X, No. X, (200X), pp: XX-YY, http://dx.doi.org/10.1109/MMM.2013.2248651.

      [3] Author, Title of the Book, Publisher, (200X), pp: XXX-YYY.

      [4] Cho JH, Chang SA, Kwon HS, Choi YH, KoSH, Moon SD, Yoo SJ, Song KH, Son HS, Kim HS, Lee WC, Cha BY, Son HY & Yoon KH (2006), Long-term effect of the internet-based glucose monitoring system on HbA1c Reduction and glucose stability: a 30-month follow-up study for diabetes management with a ubiquitous medical care system. Diabetes Care 29, 2625–2631.

      [5] Fauci AS, Braunwald E, Kasper DL & Hauser SL (2008), Principles of Harrison’s Internal Medicine, Vol. 9, 17thedn. McGraw-Hill, New York, NY, pp.2275–2304.

      [6] Kim HS & Jeong HS (2007), A nurse short message service by cellular phone in type-2 diabetic patients for six months. Journal of Clinical Nursing 16, 1082–1087.

      [7] Lee JR, Kim SA, Yoo JW & Kang YK (2007), The present status of diabetes education and the role recognition as a diabetes educator of nurses in korea. Diabetes Research and Clinical Practice 77, 199–204.

      [8] McMahon GT, Gomes HE, Hohne SH, Hu TM, Levine BA & Conlin PR (2005), Web-based care management in patients with poorly controlled diabetes. Diabetes Care 28, 1624–1629.

      [9] Thakurdesai PA, Kole PL & Pareek RP (2004), Evaluation of the quality and contents of diabetes mellitus patient education on Internet. Patient Education and Counseling 53, 309–313.

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  • How to Cite

    Mariam Jacob, S., Rekh, S., G, M., & John Paul, J. (2018). Smart traffic management system with real time analysis. International Journal of Engineering & Technology, 7(3.29), 348-351. https://doi.org/10.14419/ijet.v7i3.29.19190

    Received date: 2018-09-07

    Accepted date: 2018-09-07