Cargando…

Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based op...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Jeng-Fung, Lo, Shih-Kuei, Do, Quang Hung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196985/
https://www.ncbi.nlm.nih.gov/pubmed/30402084
http://dx.doi.org/10.1155/2018/5469428
_version_ 1783364665255919616
author Chen, Jeng-Fung
Lo, Shih-Kuei
Do, Quang Hung
author_facet Chen, Jeng-Fung
Lo, Shih-Kuei
Do, Quang Hung
author_sort Chen, Jeng-Fung
collection PubMed
description Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell's method to advance the exploration capability and increase the convergence speed. Our presented approach combines the strengths of fuzzy logic, wavelet transform, neural network, and the heuristic algorithm to detect the trends and patterns of transportation data and thus has been successfully applied to transport forecasting. Other different forecasting methods, including ANN-based model, FWNN-based model, and WNN-based model, are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R). The performance indexes show that the FWNN model achieves lower RMSE and MAPE, as well as higher R, indicating that the FWNN model is a better predictor.
format Online
Article
Text
id pubmed-6196985
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-61969852018-11-06 Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm Chen, Jeng-Fung Lo, Shih-Kuei Do, Quang Hung Comput Intell Neurosci Research Article Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell's method to advance the exploration capability and increase the convergence speed. Our presented approach combines the strengths of fuzzy logic, wavelet transform, neural network, and the heuristic algorithm to detect the trends and patterns of transportation data and thus has been successfully applied to transport forecasting. Other different forecasting methods, including ANN-based model, FWNN-based model, and WNN-based model, are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R). The performance indexes show that the FWNN model achieves lower RMSE and MAPE, as well as higher R, indicating that the FWNN model is a better predictor. Hindawi 2018-10-08 /pmc/articles/PMC6196985/ /pubmed/30402084 http://dx.doi.org/10.1155/2018/5469428 Text en Copyright © 2018 Jeng-Fung Chen et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chen, Jeng-Fung
Lo, Shih-Kuei
Do, Quang Hung
Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm
title Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm
title_full Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm
title_fullStr Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm
title_full_unstemmed Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm
title_short Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm
title_sort forecasting short-term traffic flow by fuzzy wavelet neural network with parameters optimized by biogeography-based optimization algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196985/
https://www.ncbi.nlm.nih.gov/pubmed/30402084
http://dx.doi.org/10.1155/2018/5469428
work_keys_str_mv AT chenjengfung forecastingshorttermtrafficflowbyfuzzywaveletneuralnetworkwithparametersoptimizedbybiogeographybasedoptimizationalgorithm
AT loshihkuei forecastingshorttermtrafficflowbyfuzzywaveletneuralnetworkwithparametersoptimizedbybiogeographybasedoptimizationalgorithm
AT doquanghung forecastingshorttermtrafficflowbyfuzzywaveletneuralnetworkwithparametersoptimizedbybiogeographybasedoptimizationalgorithm