Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Xianglong Luo · Danyang Li · Yu Qiang Yang · Shengrui Zhang

300 citations0/0 files storedJournal of Advanced Transportation

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Traffic Prediction and Management Techniques Traffic control and management Hydrological Forecasting Using AI

Abstract

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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References

2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2017LSTM network: a deep learning approach for short‐term traffic forecast 2014Short-term traffic forecasting: Where we are and where we’re going 2014Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning 2017Deep learning for short-term traffic flow prediction 2008Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

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2022Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities 2020Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison

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