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.

Highlights

  • Accurate travel-time forecast information has become a fundamental component of all ATIS (Advanced Traffic Information Systems)

  • 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

  • Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better, when compared with the support vector regression model with a prediction horizon of 45 minutes and more

Read more

Summary

Introduction

Accurate travel-time forecast information has become a fundamental component of all ATIS (Advanced Traffic Information Systems). Drivers demand an accurate travel-time calculator that can forecast their commute time in advance. This forecast is even more significant in the morning and evening hours, when the commuters face jammed freeways and they want to avoid the peak-hour congestion. The travel-time is dependent on multiple factors that are related through a complex-dependent relationship with one another. Such factors include weather conditions, driver behavior, and time of the day etc. This complex-dependence makes the traffic data both non-linear and non-stationary. Accurate prediction of travel time becomes a challenging task

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call