Predicting Traffic Flow with Deep Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3292-0_37
Reference32 articles.
1. Li L, Du B, Wang Y, Qin L, Tan H (2020) Estimation of missing values in heterogeneous traffic data: application of multimodal deep learning model. Knowl-Based Syst 194:105592
2. Li L, Qin L, Qu X, Zhang J, Wang Y, Ran B (2019) Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowl-Based Syst 172:1–14. https://www.sciencedirect.com/science/article/pii/S0950705119300164
3. Edes YJS, Michalopoulos PG, Plum RA (1980) Improved estimation of traffic flow for real-time control characteristics 7(9):28
4. Chan KY, Dillon TS, Singh J, Chang E (2011) Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm. IEEE Trans Intell Transp Syst 13(2):644–654
5. Zhang S, Song Y, Jiang D, Zhou T, Qin J (2019) Noise-identified Kalman filter for short-term traffic flow forecasting. In: 2019 15th International conference on mobile ad-hoc and sensor networks (MSN). IEEE, pp 462–466
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