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Multi-Branch Traffic Flow Prediction Based on Temporal Speed

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Proceedings of the Sixth International Conference of Transportation Research Group of India (CTRG 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 272))

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Abstract

Efficient traffic management is a major issue for developing countries. Traffic flow prediction is an important problem in intelligent transportation system (ITS). Various studies have been reported in the literature for traffic flow prediction in which combined models have been proposed only using traffic flow data. It is evident from the traffic flow theory that speed and flow are inter-related. Therefore, considering speed to predict flow in a model can help in improving the performance of a prediction method. Keeping this in mind, we propose a traffic flow prediction method consisting of two branches. First branch predicts traffic flow using past flow data through long short-term memory (LSTM) neural network. Second branch predicts volume using Gaussian process regression (GPR) based on temporal speed. Finally, prediction from both the branches was combined through weighted average. The mean squared error (MSE), root mean squared error (RMSE), and Pearson’s correlation coefficient (r) were used to evaluate the effectiveness of the proposed model. Based on these measures, it was found that results of our proposed model are promising.

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Acknowledgements

“This study was supported by the University Grants Commission (UGC), New Delhi, India, through the start-up grant research project Modelling and simulation of vehicular traffic flow problems through the grant No. F.30-403/2017(BSR), which is thankfully acknowledged. Authors also convey their gratitude to anonymous reviewers for their valuable suggestions which significantly improved this article.”

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Authors and Affiliations

  1. School of Liberal Studies, Dr. B. R. Ambedkar University Delhi, Delhi, 110006, India

    Nisha & Kranti Kumar

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  1. Nisha
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  2. Kranti Kumar
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Correspondence to Kranti Kumar .

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Editors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India

    Lelitha Devi

  2. Department of Civil and Environmental Engineering, Indian Institute of Technology Tirupati, Tirupati, India

    Gowri Asaithambi

  3. Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

    Shriniwas Arkatkar

  4. Department of Civil Engineering, Indian Institute of Science, Bangalore, Karnataka, India

    Ashish Verma

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“Authors declare that they have no conflict of interest.”

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© 2023 Transportation Research Group of India

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Nisha, Kumar, K. (2023). Multi-Branch Traffic Flow Prediction Based on Temporal Speed. In: Devi, L., Asaithambi, G., Arkatkar, S., Verma, A. (eds) Proceedings of the Sixth International Conference of Transportation Research Group of India . CTRG 2021. Lecture Notes in Civil Engineering, vol 272. Springer, Singapore. https://doi.org/10.1007/978-981-19-3494-0_4

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  • DOI: https://doi.org/10.1007/978-981-19-3494-0_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-3493-3

  • Online ISBN: 978-981-19-3494-0

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