A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections
2018
Sensors
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
doi:10.3390/s18072287
pmid:30011942
pmcid:PMC6068706
fatcat:72bos3dgxvbrthsuvb43nededy