Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Abstract

Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

“…The accuracy can be further improved [ 31 ]. Considering the influence of the data interval, choosing an appropriate data interval is beneficial to improve the accuracy of the results [ 27 ].…”

“…Time series data inevitably discuss cyclical and non-cyclical issues. The forecast for Freeway Speed focuses on the impact of periodicity on the forecast [ 27 ]. The prediction results of the six models in the article have been significantly improved after considering the cycle effects.…”

Period Division-Based Markov Models for Short-Term Traffic Flow Prediction

“…Additionally, the characteristics of traffic flow have been discussed by some researchers when predicting short-term traffic flow. Zhang et al [31] and Yang et al [40] indicated that there is a periodic trend for traffic flow data. Hosseini et al [4] pointed out that the rules of traffic flow are different on weekdays/weekends.…”

Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data

“…In particular, (in)direct approximation using linear, multiple linear regression models and shallow neural networks are often encountered in practice. RNN and ARMA models are often adopted in research for traffic time series prediction [64,65]. Besides that, for most model types a 'simple' prediction application as well as a more sophisticated data fusion application is derived.…”