Abstract
Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections.
Highlights
Real-time traffic state prediction plays a vital role in traffic management and public service.The ability to timely, accurately and efficiently predict the evolution of traffic can assist travelers and government agencies in reacting to possible congestion ahead of time
We propose a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict future traffic conditions in the overall network
We introduce the ConvLSTM NN and show that it outperforms other models, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), long short-term memory neural networks (LSTMs) and the stacked autoencoder model (SAE)
Summary
Real-time traffic state prediction plays a vital role in traffic management and public service.The ability to timely, accurately and efficiently predict the evolution of traffic can assist travelers and government agencies in reacting to possible congestion ahead of time. Compared with data-driven methods, mathematical or statistical models that are derived from macroscopic and microscopic theories of traffic flow exhibit difficulties addressing unstable traffic conditions and complex road settings due to their strong hypotheses and assumptions [1]. Data-driven methods, including the autoregressive integrated moving average model (ARIMA), support vector machine (SVM), Bayesian network, neural network (NN) and so forth, have achieved promising results due to their greater potential in processing complex nonlinear problems [2]. Among all the data-driven methods, the deep learning approach has been validated for its efficiency since it is capable of exploiting much deeper architectures and processing high-dimensional sets of Sensors 2018, 18, 2287; doi:10.3390/s18072287 www.mdpi.com/journal/sensors
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