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

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.

Short-Term Traffic Speed Prediction Based on Fundamental and Cointegration Relationship of Speed–Density in Non-Congested and Congested States

“…Only 20 lags at maximum, less than 2 h for 5 min interval prediction, were introduced. It is observed that the traffic condition has cyclic patterns, and it has been confirmed that considering the cyclic pattern helps improve the prediction accuracy [62], [63]. Fourth, the implementation of multitask learning would improve the prediction accuracy.…”

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

“…Chen et al [21] established an ensemble machine-learning algorithm for the short-term traffic flow prediction via the integration of gradient boosting regression trees and the least absolute shrinkage and selection operator. Besides, Yang et al [23] and Miao et al [24] focus on predicting the traffic speed and travel time of the freeways by periodic function to improve the performance of the machine-learning models.…”

On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

“…The study in [8] combines different statistical methods and machine learning methods with a trigonometric regression function to predict the evolution of the traffic speed. The previous study is extended in [9] by combining different statistical and machine learning methods with different periodic functions. Traffic predictions using data from fixed traffic detectors are restricted to the location of the detectors.…”

“…Three channels correspond to the spatiotemporal evolution of the three fundamental traffic variables estimated using FCD, and the three other channels to the spatiotemporal evolution of the three fundamental traffic variables estimated using data from the induction loops. When the traffic is predicted using FCD, the images have three channels, a width of 72 pixels and a height of 95 pixels since the freeway is divided into 95 sections 9 . The 72 pixels for the width represent the temporal evolution of a traffic variable for the previous 3.6 hours.…”