Abstract
Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.
Original language | English |
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Article number | 4075 |
Number of pages | 20 |
Journal | Mathematics |
Volume | 11 |
Issue number | 19 |
DOIs | |
Publication status | Published - 26 Sept 2023 |
Bibliographical note
Funding Information:This work was supported by the National Natural Science Foundation of China under Grant No. 71971060; Laboratory for Traffic & Transport Planning Digitalization Program, No. 2022JH-F05; Fundamental Research Funds for the Central Universities, China No. 2242022R40025; Start-up Research Fund of Southeast University, China No. RF1028623057.
Publisher Copyright:
© 2023 by the authors.
Keywords
- congestion analysis
- deep learning model
- spatial-temporal model
- traffic flow prediction