LC-RNN: A Deep Learning Model for Traffic Speed Prediction

Zhongjian Lv · Jiajie Xu · Kai Zheng · Hongzhi Yin · Pengpeng Zhao · Xiaofang Zhou

284 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Traffic control and management Air Quality Monitoring and Forecasting

Abstract

Traffic speed prediction is known as an important but challenging problem. In this paper, we propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction than existing solutions. It takes advantage of both RNN and CNN models by a rational integration of them, so as to learn more meaningful time-series patterns that can adapt to the traffic dynamics of surrounding areas. Furthermore, since traffic evolution is restricted by the underlying road network, a network embedded convolution structure is proposed to capture topology aware features. The fusion with other information, including periodicity and context factors, is also considered to further improve accuracy. Extensive experiments on two real datasets demonstrate that our proposed LC-RNN outperforms six well-known existing methods.

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References

2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction 2015Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory 2004Travel time prediction with support vector regression 2013Time Series Forecasting Using Distribution Enhanced Linear Regression

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