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Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences.
- Source :
- Journal of Transportation Engineering. Part A. Systems; Jun2022, Vol. 148 Issue 6, p1-9, 9p
- Publication Year :
- 2022
-
Abstract
- Real-time and accurate short-term traffic flow prediction is important for the operation and management of expressways. This paper presents a hybrid model that can be used to discover the spatiotemporal dependencies of traffic flows and, thus, achieve a more accurate traffic flow forecast. This model stacks a full connection (FC) layer, two-layer long short-term memory (LSTM), and a middle mean pooling layer, denoted by FC-LSTM, to expand the ability of LSTM to capture spatial correlations and too long-term temporal dependencies of traffic flows. Traffic data of 51 toll stations (accounting for 15% of all stations) in the Guizhou expressway network in China in January 2016 were used to evaluate the validation of FC-LSTM. The results showed that the traffic flows at adjacent tollgates were interactional and correlated, that FC-LSTM could capture this spatial dependency and the temporal correlations of traffic flows, and, thus, that it was superior to other baselines with a low prediction error, high precision, and high fitting degree. Moreover, FC-LSTM is interpretable and robust owing to its explicit input and is suitable for traffic flow prediction for most tollgates under the same parameters. [ABSTRACT FROM AUTHOR]
- Subjects :
- TRAFFIC flow
EXPRESS highways
TRAFFIC estimation
FORECASTING
OPERATIONS management
Subjects
Details
- Language :
- English
- ISSN :
- 24732907
- Volume :
- 148
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Journal of Transportation Engineering. Part A. Systems
- Publication Type :
- Academic Journal
- Accession number :
- 156438830
- Full Text :
- https://doi.org/10.1061/JTEPBS.0000660