Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction

Licheng Qu,Wei Li,Zhaolu Li,Minghao Zhang,Ronghui Zhang

doi:10.1155/2020/8899478

Licheng Qu, Wei Li + Show 3 more

Open Access

https://doi.org/10.1155/2020/8899478

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Abstract

As a typical time series, the length of the data sequence is critical to the accuracy of traffic state prediction. In order to fully explore the causality between traffic data, this study established a temporal backtracking and multistep delay model based on recurrent neural networks (RNNs) to learn and extract the long- and short-term dependencies of the traffic state data. With a real traffic data set, the coordinate descent algorithm was employed to search and determine the optimal backtracking length of traffic sequence, and multistep delay predictions were performed to demonstrate the relationship between delay steps and prediction accuracies. Besides, the performances were compared between three variants of RNNs (LSTM, GRU, and BiLSTM) and 6 frequently used models, which are decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), random forest (RF), gradient boosting decision tree (GBDT), and stacked autoencoder (SAE). The prediction results of 10 consecutive delay steps suggest that the accuracies of RNNs are far superior to those of other models because of the more powerful and accurate pattern representing ability in time series. It is also proved that RNNs can learn and mine longer time dependencies.

Highlights

As an indispensable means of transportation, automobiles have brought unprecedented convenience to our daily life
Despite the increase of the number of delay steps, the time gap between the current time point and the predicted time point causes the interruption of the dependency relationship and makes the error metrics increase to varying degrees
There will be some loss in prediction accuracy, multistep delay prediction can still improve the diversity of prediction results and provide a more reliable application

Summary

Introduction

As an indispensable means of transportation, automobiles have brought unprecedented convenience to our daily life. They have brought many social problems, such as traffic congestion, energy crisis, and air pollution, which have caused widespread concern around the world [1, 2]. As the cornerstone of intelligent transportation systems, traffic flow forecasting is crucial to the development of ITS. Accurate and reliable traffic state prediction can help road users and managers to grasp the real-time traffic status and plan travel routes more reasonably. It reduces environmental pollution and improves the management art of smart cities [6]

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