Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences
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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/24732907
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Supplemental Notes:
- © 2022 American Society of Civil Engineers.
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Authors:
- Shuai, Chunyan
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0000-0001-8282-2697
- Wang, WenCong
- Xu, Geng
- He, Min
- Lee, Jaeyoung
- Publication Date: 2022-6
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 04022026
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Serial:
- Journal of Transportation Engineering, Part A: Systems
- Volume: 148
- Issue Number: 6
- Publisher: American Society of Civil Engineers
- ISSN: 2473-2907
- EISSN: 2473-2893
- Serial URL: http://ascelibrary.org/journal/jtepbs
Subject/Index Terms
- TRT Terms: Expressways; Predictive models; Spatial analysis; Toll facilities; Traffic flow; Traffic models
- Geographic Terms: Guizhou Province (China)
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01845682
- Record Type: Publication
- Files: TRIS, ASCE
- Created Date: May 19 2022 10:41AM