Article2019

An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning

Yuanli Gu · Wenqi Lu · Xinyue Xu · Lingqiao Qin · Hanyu Zhang

160 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Forecasting Techniques and Applications Air Quality Monitoring and Forecasting

Abstract

Short-term traffic volume prediction, which can assist road users in choosing appropriate routes and reducing travel time cost, is a significant topic of intelligent transportation system. To overcome the error magnification phenomena of traditional combination methods and to improve prediction performance, this paper proposes an improved Bayesian combination model with deep learning (IBCM-DL) for traffic flow prediction. First, an IBCM framework is established based on the new BCM framework proposed by Wang. Then, correlation analysis is used to analyze the relevance between the historical traffic flow and the traffic flow within the current interval. Three sub-predictors including the gated recurrent unit neural network (GRUNN), autoregressive integrated moving average (ARIMA), and radial basis function neural network (RBFNN) are incorporated into the IBCM framework to take advantage of each method. The real-world traffic volume data captured by microwave sensors located on the expressways of Beijing was used to validate the proposed model in multiple scenarios. The overall results illustrate that the IBCM-DL model outperforms the other state-of-the-art methods in terms of accuracy and stability.

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References

1977Book Review: Time Series Analysis: Forecasting and Control 1977Time Series Analysis: Forecasting and Control 2017Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network 2014New Bayesian combination method for short-term traffic flow forecasting 2015A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting 2019Short-term prediction of lane-level traffic speeds: A fusion deep learning model 2017Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method 2010Forecasting urban traffic flow by SVR with continuous ACO

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