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Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression

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Abstract

In recent decades, there has been substantial population growth, leading to a higher volume of vehicles on the roadways. This has contributed to traffic congestion issues, affecting not just major metropolitan areas but also medium-sized and small cities worldwide. The management of roadway traffic is enhanced by accurate short-term traffic flow forecasts, which makes it a crucial part of intelligent transportation systems. This study utilizes Gaussian process regression (GPR) to predict the road traffic flow under heterogeneous conditions for 5 min in the future using past data. GPR model represents the relationship between data points as a probability distribution over functions, rather than a single deterministic function as in traditional linear regression. This allows GPR to capture both the mean and uncertainty of predictions. All of the comparable models were trained and tested on actual data sets that were gathered through field research. Results of the GPR model were compared with other traditional models like autoregressive moving average model, multi-layer perceptron and cascade forward backpropagation. The performance analysis was done and the GPR model was found to be quite effective followed by other traditional neural networks. Study results confirm that the GPR model can be successfully applied for short-term traffic flow prediction under heterogeneous traffic flow conditions.

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Data availability

Data used in this research were collected by authors under the University Grants Commission (UGC), New Delhi, India, funded research project ‘Modelling and simulation of vehicular traffic flow problems.’ If there are relevant research needs, the data can be obtained by sending an e-mail to the corresponding author.

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Acknowledgements

Authors are thankful to the University Grants Commission (UGC), New Delhi, India, for providing financial support to carry out this study through the start-up grant project Modeling and simulation of vehicular traffic flow problems via the grant No. F.30-403/2017(BSR)

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Authors and Affiliations

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

    Bharti & Kranti Kumar

  2. Department of Mathematics, Maharashi Dayanand University, Rohtak, Haryana, 124001, India

    Bharti Naheliya

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  1. Bharti
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  2. Bharti Naheliya
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First Author: Conceptualization, Methodology, Investigation, Writing - original draft. Second Author: Methodology, Investigation, Writing - original draft. Third Author: Writing - review & editing, Supervision.

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Correspondence to Kranti Kumar.

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Bharti, Naheliya, B. & Kumar, K. Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01902-1

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