A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder

In urban planning and transportation management, the centrality characteristics of urban streets are vital measures to consider. Centrality can help in understanding the structural properties of dense traffic networks that affect both human life and activity in cities. Many cities classify urban str...

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Veröffentlicht in:	ISPRS international journal of geo-information 2020-07, Vol.9 (7), p.456
Hauptverfasser:	Noori, Fatemeh, Kamangir, Hamid, A. King, Scott, Sheta, Alaa, Pashaei, Mohammad, SheikhMohammadZadeh, Abbas
Format:	Artikel
Sprache:	eng
Schlagworte:	Back propagation centrality measures Cities Classification Classifiers Deep learning Design Federal agencies Land use Local movements Machine learning Neural networks Noise reduction Rehabilitation Roads Roads & highways Semantics stacked denoising autoencoder street functionality classification Streets Support vector machines Traffic flow Transportation management Transportation networks Transportation planning Urban areas Urban planning urban transportation network Walkways
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