Configuration for Predicting Travel-Time Using Wavelet Packets and Support Vector Regression - Journal of Transportation Technologies

JTTs > Vol.3 No.3, July 2013

Configuration for Predicting Travel-Time Using Wavelet Packets and Support Vector Regression ()

HTML

Download as PDF (Size: 622KB) PP. 220-231

DOI: 10.4236/jtts.2013.33023 4,580 Downloads 7,016 Views Citations

Author(s)

Adeel Yusuf, Vijay K. Madisetti

Affiliation(s)

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.

ABSTRACT

Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed, with a small review of the previous work. A model for the travel-time prediction on freeways based on wavelet packet decomposition and support vector regression (WDSVR) is proposed, which used the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better (with selected wavelets only), when compared with the support vector regression model (without wavelet decomposition) with a prediction horizon of 45 minutes and more. The data used in this paper was taken from the California Department of Transportation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5-minute intervals over a distance of 9.13 miles. The results indicated MAPE ranging from 12.35% to 14.75% against the classical SVR method with MAPE ranging from 12.57% to 15.84% with a prediction horizon of 45 minutes to 1 hour. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is presented with interchangeable prediction methods.

KEYWORDS

Travel-Time Prediction; Wavelet Packets; Support Vector Regression; Advanced Traveler Information System

Share and Cite:

Yusuf, A. and Madisetti, V. (2013) Configuration for Predicting Travel-Time Using Wavelet Packets and Support Vector Regression. Journal of Transportation Technologies, 3, 220-231. doi: 10.4236/jtts.2013.33023.

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journals Menu

Home

About SCIRP

Service

Policies