Temporal pattern mining of urban traffic volume data: a pairwise hybrid clustering method
Author:
Affiliation:
1. Department of Infrastructure Engineering, University of Melbourne, Melbourne, Australia
Publisher
Informa UK Limited
Subject
Transportation,Modeling and Simulation,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/21680566.2023.2185496
Reference42 articles.
1. Bartels R. B. A. Barsky and J. C. Beatty. 1987. An Introduction to Splines for Use in Computer Graphics and Geometric Modeling.
2. Bishop, C. M., and N. M. Nasrabadi 2006. Pattern Recognition and Machine Learning. 1st ed., Vol. 4, 738. New York: Springer-Verlag.
3. A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection
4. Traffic flow prediction by an ensemble framework with data denoising and deep learning model
5. A statistical method for estimating predictable differences between daily traffic flow profiles
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1. Urban Resident Travel Survey Method Based on Cellular Signaling Data;ISPRS International Journal of Geo-Information;2023-07-28
2. Traffic Anomaly Detection: Exploiting Temporal Positioning of Flow-Density Samples;IEEE Transactions on Intelligent Transportation Systems;2023
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