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Traffic Flow Prediction: An Intelligent Scheme for Forecasting Traffic Flow Using Air Pollution Data in Smart Cities with Bagging Ensemble

Author

Listed:
  • Noor Ullah Khan

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan)

  • Munam Ali Shah

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan)

  • Carsten Maple

    (Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK)

  • Ejaz Ahmed

    (Computer Science Department, National University of Computer and Emerging Sciences (NUCES-FAST), Islamabad 44000, Pakistan)

  • Nabeel Asghar

    (Department of Computer Science, Bahauddin Zakariya University, Multan 60000, Pakistan)

Abstract

Traffic flow prediction is the most critical part of any traffic management system in a smart city. It can help a driver to pick the most optimized way to their target destination. Air pollution data are often connected with traffic congestion and there exists plenty of research on the connection between air pollution and traffic congestion using different machine learning approaches. A scheme for efficiently predicting traffic flow using ensemble techniques such as bagging and air pollution has not yet been introduced. Therefore, there is a need for a more accurate traffic flow prediction system for the smart cities. The aim of this research is to forecast traffic flow using pollution data. The contribution is twofold: Firstly, a comparison has been made using different simple regression techniques to find out the best-performing model. Secondly, bagging and stacking ensemble techniques have been used to find out the most accurate model of the two comparisons. The results show that the K-Nearest Neighbors (KNN) bagging ensemble provides far better results than all the other regression models used in this study. The experimental results show that the KNN bagging ensemble model reduces the error rate in predicting the traffic congestion by more than 30%.

Suggested Citation

  • Noor Ullah Khan & Munam Ali Shah & Carsten Maple & Ejaz Ahmed & Nabeel Asghar, 2022. "Traffic Flow Prediction: An Intelligent Scheme for Forecasting Traffic Flow Using Air Pollution Data in Smart Cities with Bagging Ensemble," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4164-:d:784313
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    References listed on IDEAS

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    1. Nimesh, Vikas & Sharma, Debojit & Reddy, V. Mahendra & Goswami, Arkopal Kishore, 2020. "Implication viability assessment of shift to electric vehicles for present power generation scenario of India," Energy, Elsevier, vol. 195(C).
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    Cited by:

    1. Junkai Zhang & Jun Wang & Haoyu Zang & Ning Ma & Martin Skitmore & Ziyi Qu & Greg Skulmoski & Jianli Chen, 2024. "The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(14), pages 1-34, July.
    2. Thembani Moyo & Siphiwe Mbatha & Oluwayemi-Oniya Aderibigbe & Trynos Gumbo & Innocent Musonda, 2022. "Assessing Spatial Variations of Traffic Congestion Using Traffic Index Data in a Developing City: Lessons from Johannesburg, South Africa," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    3. Shenghan Zhou & Chaofan Wei & Chaofei Song & Yu Fu & Rui Luo & Wenbing Chang & Linchao Yang, 2022. "A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    4. Jucheol Moon & Jin Gi Hong & Tae-Won Park, 2022. "A Novel Method for Traffic Estimation and Air Quality Assessment in California," Sustainability, MDPI, vol. 14(15), pages 1-12, July.
    5. Fu, Yang & Ying, Feixiang & Huang, Lingling & Liu, Yang, 2023. "Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM," Renewable Energy, Elsevier, vol. 203(C), pages 455-472.

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