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Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique

Author

Listed:
  • Mohandu Anjaneyulu

    (School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore 632014, India)

  • Mohan Kubendiran

    (School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore 632014, India)

Abstract

A vital problem faced by urban areas, traffic congestion impacts wealth, climate, and air pollution in cities. Sustainable transportation systems (STSs) play a crucial role in traffic congestion prediction for adopting transportation networks to improve the efficiency and capacity of traffic management. In STSs, one of the essential functional areas is the advanced traffic management system, which alleviates traffic congestion by locating traffic bottlenecks to intensify the interpretation of the traffic network. Furthermore, in urban areas, accurate short-term traffic congestion forecasting is critical for designing transport infrastructure and for the real-time optimization of traffic. The main objective of this paper was to devise a method to predict short-term traffic congestion (STTC) every 5 min over 1 h. This paper proposes a hybrid Xception support vector machine (XPSVM) classifier model to predict STTC. Primarily, the Xception classifier uses separable convolution, ReLU, and convolution techniques to predict the feature detection in the dataset. Secondarily, the support vector machine (SVM) classifier operates maximum marginal separations to predict the output more accurately using the weight regularization technique and a fine-tuned binary hyperplane mechanism. The dataset used in this work was taken from Google Maps and comprised snapshots of Bangalore, Karnataka, taken using the Selenium automation tool. The experimental outcome showed that the proposed model forecasted traffic congestion with an accuracy of 97.16%.

Suggested Citation

  • Mohandu Anjaneyulu & Mohan Kubendiran, 2022. "Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:74-:d:1010069
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    References listed on IDEAS

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    1. Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    2. Alisoltani, Negin & Leclercq, Ludovic & Zargayouna, Mahdi, 2021. "Can dynamic ride-sharing reduce traffic congestion?," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 212-246.
    3. Alireza Ermagun & David Levinson, 2018. "Spatiotemporal traffic forecasting: review and proposed directions," Transport Reviews, Taylor & Francis Journals, vol. 38(6), pages 786-814, November.
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