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Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection

Author

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  • Isaac Oyeyemi Olayode

    (Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg P.O. Box 2028, South Africa)

  • Lagouge Kwanda Tartibu

    (Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg P.O. Box 2028, South Africa)

  • Modestus O. Okwu

    (Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg P.O. Box 2028, South Africa)

  • Alessandro Severino

    (Department of Civil Engineering and Architecture, University of Catania, 95123 Catania, Italy)

Abstract

The accurate and effective prediction of the traffic flow of vehicles plays a significant role in the construction and planning of signalized road intersections. The application of artificially intelligent predictive models in the prediction of the performance of traffic flow has yielded positive results. However, much uncertainty still exists in the determination of which artificial intelligence methods effectively resolve traffic congestion issues, especially from the perspective of the traffic flow of vehicles at a four-way signalized road intersection. A hybrid algorithm, an artificial neural network trained by a particle swarm optimization model (ANN-PSO), and a heuristic Artificial Neural Network model (ANN) were compared in the prediction of the flow of traffic of vehicles using the South Africa transportation system as a case study. Two hundred and fifty-nine (259) traffic datasets were obtained from the South African road network using inductive loop detectors, video cameras, and GPS-controlled equipment. For the ANN and ANN-PSO training and testing, 219 traffic data were used for the training, and 40 were used for the testing of the ANN-PSO model, while training (160), testing (40), and validation (59) was used for the ANN. The ANN result presented a logistic sigmoid transfer function with a 13–6–1 model and a testing R 2 of 0.99169 compared to the ANN-PSO result, which showed a testing performance of R 2 0.99710. This result shows that the ANN-PSO model is more efficient and effective than the ANN model in the prediction of the traffic flow of vehicles at a four-way signalized road intersection. Furthermore, the ANN and ANN-PSO models are robust enough to predict traffic flow due to their better testing performance. The modelling approaches proposed in this study will assist transportation engineers and urban planners in designing a traffic control system for traffic lights at four-way signalized road intersections. Finally, the results of this research will assist transportation engineers and traffic controllers in providing traffic flow information and travel guidance for motorists and pedestrians in the optimization of their travel time decision-making.

Suggested Citation

  • Isaac Oyeyemi Olayode & Lagouge Kwanda Tartibu & Modestus O. Okwu & Alessandro Severino, 2021. "Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection," Sustainability, MDPI, vol. 13(19), pages 1-28, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10704-:d:643898
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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. (Sean) Qian, Zhen & Li, Jia & Li, Xiaopeng & Zhang, Michael & Wang, Haizhong, 2017. "Modeling heterogeneous traffic flow: A pragmatic approach," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 183-204.
    3. Kirby, Howard R. & Watson, Susan M. & Dougherty, Mark S., 1997. "Should we use neural networks or statistical models for short-term motorway traffic forecasting?," International Journal of Forecasting, Elsevier, vol. 13(1), pages 43-50, March.
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    2. Naheliya, Bharti & Redhu, Poonam & Kumar, Kranti, 2024. "MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    3. Isaac Oyeyemi Olayode & Alessandro Gaetano Severino & Tiziana Campisi & Lagouge Kwanda Tartibu, 2022. "Comprehensive Literature Review on the Impacts of COVID-19 Pandemic on Public Road Transportation System: Challenges and Solutions," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
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    5. Xiaoping Tian & Changkuan Zou & Yuqing Zhang & Lei Du & Song Wu, 2023. "NA-DGRU: A Dual-GRU Traffic Speed Prediction Model Based on Neighborhood Aggregation and Attention Mechanism," Sustainability, MDPI, vol. 15(4), pages 1-20, February.

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