Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

Xu Miao,Yajie Zou,Lingtao Wu,Bing Wu

doi:10.1155/2020/3463287

Xu Miao, Yajie Zou + Show 2 more

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https://doi.org/10.1155/2020/3463287

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Abstract

Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.

Highlights

Travel time can effectively measure roadway traffic conditions [1]. us, accurate prediction of freeway travel time is important for traffic management agencies to provide better traffic guidance
Few studies have comprehensively evaluated the effects of different periodic functions on the two types of models under different prediction steps. us, this study focuses on multistep ahead travel time prediction by considering different periodic functions. e periodic characteristics of the travel time are captured by simple average (SA), trigonometric polynomial function (TPF), and double exponential smoothing method (DES) models. e residual part is modeled by the statistical models (ARIMA, space time (ST) model, vector autoregressive (VAR) model) and machine learning models(support vector machine (SVM), back propagation neural network (BPNN), multilinear regression (MLR))
We explored the impacts of different periodic functions on statistical models and machine learning models under different aggregation levels for the input data. e testing period is 15:30 to 19:30 from 1 August to 31 August (21 weekdays)

Summary

Introduction

Travel time can effectively measure roadway traffic conditions [1]. us, accurate prediction of freeway travel time is important for traffic management agencies to provide better traffic guidance. Based on the previous studies, some studies have compared statistical models and machine learning models, and some scholars have proposed the improvement of periodic functions on travel time prediction. Us, this study focuses on multistep ahead travel time prediction by considering different periodic functions.

Results

Conclusion