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Evolving Prediction Models with Genetic Algorithm to Forecast Vehicle Volume in a Service Station (Best Application Paper)

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Artificial Intelligence XXXVI (SGAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11927))

Abstract

In the service industry, having an efficient resource plan is of utmost importance for operational efficiency. An accurate forecast of demand is crucial in obtaining a resource plan which is efficient. In this paper, we present a real world application of an AI forecasting model for vehicle volumes forecasting in service stations. We improve on a previously proposed approach by intelligently tuning the hyper parameters of the prediction model, taking into account the variability of the vehicle volume data in a service station. In particular, we build a Genetic algorithm based model to find the topology of the neural network and also to tune additional parameters of the prediction model that is related to data filtration, correction and feature selection. We compare our results with the results from ad hoc parameter settings of the model from previous work and show that the combined genetic algorithm and neural network based approach further improves forecasting accuracy which helps service stations better manage their resource requirements.

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Authors and Affiliations

  1. EBTIC, Khalifa University, Abu Dhabi, UAE

    Himadri Sikhar Khargharia, Siddhartha Shakya, Russell Ainslie & Gilbert Owusu

  2. British Telecom, Ipswich, UK

    Himadri Sikhar Khargharia, Siddhartha Shakya, Russell Ainslie & Gilbert Owusu

Authors
  1. Himadri Sikhar Khargharia
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  2. Siddhartha Shakya
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  3. Russell Ainslie
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  4. Gilbert Owusu
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Corresponding author

Correspondence to Himadri Sikhar Khargharia .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Khargharia, H.S., Shakya, S., Ainslie, R., Owusu, G. (2019). Evolving Prediction Models with Genetic Algorithm to Forecast Vehicle Volume in a Service Station (Best Application Paper). In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-34885-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34884-7

  • Online ISBN: 978-3-030-34885-4

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