Abstract
Traffic flow prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the governors make reliable traffic control strategies. In this paper, we propose a deep traffic flow prediction architecture P-DBL, which takes advantage of a deep bi-directional long short-term memory (DBL) model and precipitation information. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data and additional precipitation data. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS) and the precipitation dataset from California Data Exchange Center (CDEC). The experiment results demonstrate that the proposed model for traffic flow prediction obtains high accuracy compared with other models.
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Notes
- 1.
Caltrans Performance Measurement System (PeMS), http://pems.dot.ca.gov.
- 2.
California Data Exchange Center (CDEC), http://cdec.water.ca.gov.
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Acknowledgement
This work is supported by “Fundamental Research Funds for the Central Universities” (XDJK2017C027) and “CERNET Innovation Project” (NGII20170516).
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Wang, J., Xu, X., Wang, F., Chen, C., Ren, K. (2018). A Deep Prediction Architecture for Traffic Flow with Precipitation Information. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_31
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