Yusof, Yuhanis and Ahmad, Farzana Kabir and Kamaruddin, Siti Sakira and Omar, Mohd Hasbullah and Mohamed, Athraa Jasim (2015) Short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine. Soft Computing in Data Science, 545. pp. 164-173. ISSN 1865-0929

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Abstract

The goal of an active traffic management is to manage congestion based on current and predicted traffic conditions.This can be achieved by utilizing traffic historical data to forecast the traffic flow which later supports travellers for a better journey planning.In this study, a new method that integrates Firefly algorithm (FA) with Least Squares Support Vector Machine (LSSVM) is proposed for short term traffic speed forecasting, which is later termed as FA-LSSVM.In particular, the Firefly algorithm which has the advantage in global search is used to optimize the hyper-parameters of LSSVM for efficient data training. Experimental result indicates that the proposed FA-LSSVM generates lower error rate and a higher accuracy compared to a non-optimized LSSVM.Such a scenario indicates that FA-LSSVM would be a competitor method in the area of time series forecasting.

Item Type:	Article
Additional Information:	Book Subtitle:First International Conference, SCDS 2015, Putrajaya, Malaysia, September 2–3, 2015, Proceedings
Uncontrolled Keywords:	short term forecasting,Least Squares Support Vector Machine, Firefly algorithm, traffic management system
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	09 Sep 2015 09:28
Last Modified:	28 Apr 2016 01:40
URI:	https://repo.uum.edu.my/id/eprint/15449

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