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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageBritish English
Title of host publicationArtificial Intelligence XXXVI - 39th SGAI International Conference on Artificial Intelligence, AI 2019, Proceedings
EditorsMax Bramer, Miltos Petridis
PublisherSpringer
Pages167-179
Number of pages13
ISBN (Print)9783030348847
DOIs
StatePublished - 2019
Event39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019 - Cambridge, United Kingdom
Duration: 17 Dec 201919 Dec 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11927 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019
Country/TerritoryUnited Kingdom
CityCambridge
Period17/12/1919/12/19

Keywords

  • Forecasting
  • Genetic algorithm
  • Neural network

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