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Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days

by Bharti Sharma, Vinod Kumar Katiyar, Arvind Kumar Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 17
Year of Publication: 2014
Authors: Bharti Sharma, Vinod Kumar Katiyar, Arvind Kumar Gupta
10.5120/18840-0026

Bharti Sharma, Vinod Kumar Katiyar, Arvind Kumar Gupta . Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days. International Journal of Computer Applications. 107, 17 ( December 2014), 1-6. DOI=10.5120/18840-0026

@article{ 10.5120/18840-0026,
author = { Bharti Sharma, Vinod Kumar Katiyar, Arvind Kumar Gupta },
title = { Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 17 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number17/18840-0026/ },
doi = { 10.5120/18840-0026 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:16.873199+05:30
%A Bharti Sharma
%A Vinod Kumar Katiyar
%A Arvind Kumar Gupta
%T Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 17
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a model for traffic volume prediction which can be effectively used for transportation planning, management and security assessment at any time. Fuzzy logic is applied in order to realize effective and efficient traffic prediction. In this paper, 'day' of a week and 'time' of a day are taken as inputs for proposed model and the output will be the predicted the traffic volume. The 'time' is divided into nine triangular membership functions. The second input 'day' is divided into five triangular membership functions and the output forecasted traffic volume has been divided into eight triangular membership functions. The predicted traffic volume when compared with actual traffic volume has MAPE within acceptable level of error. Prediction results show that the proposed fuzzy logic system produces more accurate and stable traffic volume predictions.

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Index Terms

Computer Science
Information Sciences

Keywords

Defuzzification Fuzzy logic MAPE Membership functions Traffic load forecasting

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