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Smart Cities Traffic Congestion Monitoring and Control System

Published: 25 May 2020 Publication History

Abstract

The traffic monitoring projects responsible for the current traffic monitoring infrastructure utilized by companies and government agencies tend to be very expensive and require difficult and extensive implementation. The challenge and goal of this paper is to create a smaller scale, low cost method of analyzing, controlling, and predicting traffic conditions. Traffic data including car count, frequency, and direction, is gathered from a USB camera and sent to a microcontroller to be interpreted using computer vision libraries. The traffic data is then transferred and stored onto the cloud to be further analyzed. This paper focuses on two aspects of managing traffic. The first aspect involves the optimization of traffic cycles at an intersection using incoming car counts to minimize the wait time between traffic light cycles. The second aspect involves predicting future traffic flow by training a deep neural network utilizing collected traffic data and machine learning techniques.

References

[1]
A. Atta, S. Abbasand M. Khan, and G. Ahmedand U. Farooq. 2018. An Adaptive Approach: Smart Traffic Congestion Control System. Journal of King Saud University -- Computer and Information Sciences (2018). https://doi.org/10.1016/j.jksuci.2018.10.011
[2]
G. Bradski and A. Kaehler. 2008. Learning OpenCV. O'Reilly Media Inc.
[3]
J. Chung, C Gülçehre, K. Cho, and Y. Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR abs/1412.3555 (2014). arXiv:1412.3555 http://arxiv.org/abs/1412.3555
[4]
D. Gazis. 2002. The Origins of Traffic Theory. Operations Research. 50, 1 (Feb. 2002), 69--77.
[5]
J. Kell, I. Fullerton, and M. Mills. 1990. Traffic Detector Handbook. Technical Report. U.S. Department of Transportation.
[6]
keras. 2017. Keras Documentation: Datasets. https://keras.io/datasets/
[7]
keras. 2017. Keras Documentation: Recurrent Layers. https://keras.io/layers/recurrent/
[8]
L. Mimbela. 2007. A Summary of Vehicle Detection and Surveillance Technologies used in Intelligent Trasnportation Systems. Technical Report. U.S. Department of Transportation.
[9]
W. Zhao, Y. Xing, X. Ban, and C. Guo. 2017. Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine. Scientific Programming (2017).

Cited By

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  • (2024)Adaptive and dynamic smart traffic light system for efficient management of regular and emergency vehicles at city intersectionIET Smart Cities10.1049/smc2.12090Online publication date: 23-Aug-2024
  • (2022)Optimal Extreme Learning Machine based Traffic Congestion Control System in Vehicular Network2022 6th International Conference on Electronics, Communication and Aerospace Technology10.1109/ICECA55336.2022.10009111(597-603)Online publication date: 1-Dec-2022
  • (2021)Applications of Big Data in Large and Small SystemsApplications of Big Data in Large- and Small-Scale Systems10.4018/978-1-7998-6673-2.ch002(20-37)Online publication date: 2021

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  1. Smart Cities Traffic Congestion Monitoring and Control System

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    cover image ACM Conferences
    ACMSE '20: Proceedings of the 2020 ACM Southeast Conference
    April 2020
    337 pages
    ISBN:9781450371056
    DOI:10.1145/3374135
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 25 May 2020

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    Author Tags

    1. Traffic control
    2. machine learning
    3. neural networks
    4. optimization
    5. traffic prediction

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    ACM SE '20
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    ACM SE '20: 2020 ACM Southeast Conference
    April 2 - 4, 2020
    FL, Tampa, USA

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    Overall Acceptance Rate 502 of 1,023 submissions, 49%

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    Cited By

    View all
    • (2024)Adaptive and dynamic smart traffic light system for efficient management of regular and emergency vehicles at city intersectionIET Smart Cities10.1049/smc2.12090Online publication date: 23-Aug-2024
    • (2022)Optimal Extreme Learning Machine based Traffic Congestion Control System in Vehicular Network2022 6th International Conference on Electronics, Communication and Aerospace Technology10.1109/ICECA55336.2022.10009111(597-603)Online publication date: 1-Dec-2022
    • (2021)Applications of Big Data in Large and Small SystemsApplications of Big Data in Large- and Small-Scale Systems10.4018/978-1-7998-6673-2.ch002(20-37)Online publication date: 2021

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