Using Neural Networks for Bicycle Route Planning

This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to des...

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Autores principales:	Jurica Đerek, Marjan Sikora, Luka Kraljević, Mladen Russo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GIS multiple criteria analysis neural networks bicycle routes tourists Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/64571babd7cb4103a1a8c659f01c3e45
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spelling	oai:doaj.org-article:64571babd7cb4103a1a8c659f01c3e452021-11-11T15:08:25ZUsing Neural Networks for Bicycle Route Planning10.3390/app1121100652076-3417https://doaj.org/article/64571babd7cb4103a1a8c659f01c3e452021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10065https://doaj.org/toc/2076-3417This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to design the routes on the novel area. We created two NNs capable of choosing the best route among the given road network by training them on two different areas. The bicycle routes produced by NNs were the same at best and had 75% overlap at the worst compared to those produced by human experts. Furthermore, the mean square error for all of our NN models varied from 0.015 and 0.081. We compared this new approach to the traditional multicriteria GIS (geographic information system) analysis (MA) that requires the human expert to define the bicycle route selection criteria. The benefit of using NN over the MA was that the NN directly transfers the human expertise to a model. In contrast, the MA needs the expert to select multiple criteria and adjust their weights carefully.Jurica ĐerekMarjan SikoraLuka KraljevićMladen RussoMDPI AGarticleGISmultiple criteria analysisneural networksbicycleroutestouristsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10065, p 10065 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GIS multiple criteria analysis neural networks bicycle routes tourists Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	GIS multiple criteria analysis neural networks bicycle routes tourists Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jurica Đerek Marjan Sikora Luka Kraljević Mladen Russo Using Neural Networks for Bicycle Route Planning
description	This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to design the routes on the novel area. We created two NNs capable of choosing the best route among the given road network by training them on two different areas. The bicycle routes produced by NNs were the same at best and had 75% overlap at the worst compared to those produced by human experts. Furthermore, the mean square error for all of our NN models varied from 0.015 and 0.081. We compared this new approach to the traditional multicriteria GIS (geographic information system) analysis (MA) that requires the human expert to define the bicycle route selection criteria. The benefit of using NN over the MA was that the NN directly transfers the human expertise to a model. In contrast, the MA needs the expert to select multiple criteria and adjust their weights carefully.
format	article
author	Jurica Đerek Marjan Sikora Luka Kraljević Mladen Russo
author_facet	Jurica Đerek Marjan Sikora Luka Kraljević Mladen Russo
author_sort	Jurica Đerek
title	Using Neural Networks for Bicycle Route Planning
title_short	Using Neural Networks for Bicycle Route Planning
title_full	Using Neural Networks for Bicycle Route Planning
title_fullStr	Using Neural Networks for Bicycle Route Planning
title_full_unstemmed	Using Neural Networks for Bicycle Route Planning
title_sort	using neural networks for bicycle route planning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/64571babd7cb4103a1a8c659f01c3e45
work_keys_str_mv	AT juricađerek usingneuralnetworksforbicyclerouteplanning AT marjansikora usingneuralnetworksforbicyclerouteplanning AT lukakraljevic usingneuralnetworksforbicyclerouteplanning AT mladenrusso usingneuralnetworksforbicyclerouteplanning
_version_	1718437143969267712

Using Neural Networks for Bicycle Route Planning

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