Future Location and Trajectory Prediction-Reference-Cited by-同舟云学术

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Future Location and Trajectory Prediction

Published:2020 Issue: Volume: Page:215-254
ISSN:
Container-title:Big Data Analytics for Time-Critical Mobility Forecasting
language:
Short-container-title:

Author:

Georgiou Harris,Petrou Petros,Tampakis Panagiotis,Sideridis Stylianos,Chondrodima Eva,Pelekis Nikos,Theodoridis Yannis

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-45164-6_8

Reference43 articles.

1. Agarwal, P.K., Fox, K., Munagala, K., Nath, A., Pan, J., Taylor, E.: Subtrajectory clustering: models and algorithms. In: PODS, pp. 75–87 (2018)

2. Alexandridis, A., Chondrodima, E., Giannopoulos, N., Sarimveis, H.: A fast and efficient method for training categorical radial basis function networks. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2831–2836 (2017). http://doi.org/10.1109/TNNLS.2016.2598722

3. Ankerst, M., Breunig, M., et al.: Optics: ordering points to identify the clustering structure. In: Proc. of SIGMOD 1999 (1999)

4. Ayhan, S., Samet, H.: Aircraft trajectory prediction made easy with predictive analytics. In: Proc. of ACM SIGKDD 2016 (2016)

5. Ayhan, S., Samet, H.: Time series clustering of weather observations in predicting climb phase of aircraft trajectories. In: Proc. of the IWCTS 2016 (2016)

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1. Machine Learning Models for Vessel Route Forecasting: An Experimental Comparison;2022 23rd IEEE International Conference on Mobile Data Management (MDM);2022-06

2. Public Transport Arrival Time Prediction Based on GTFS Data;Machine Learning, Optimization, and Data Science;2022

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