Intelligent transportation systems (ITS) have developed rapidly for urban road networks in recent years. Accurate and efficient short-term traffic flow speed prediction is key to the realization of ITS. Traffic flow data usually perform stochastic and nonlinear characteristics, making short-term forecasting of large-scale urban road networks challenging. To extract the spatial and temporal correlations between traffic flows, we propose a novel short-term speed forecasting of large-scale urban road network based on the deep learning algorithm Transformer used in the field of natural language processing. We test the model using a real floating car dataset collected on a large-scale urban road network. It is found that the Transformer model shows high prediction accuracy and efficiency performance and outperforms the benchmark models.
Short-Term Speed Forecasting of Large-Scale Urban Road Network Based on Transformer
21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China
CICTP 2021 ; 609-618
2021-12-14
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Short-term speed predictions exploiting big data on large urban road networks
Online Contents | 2016
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