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

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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Bibliographic Details
Main Authors: Fatemeh Noori, Hamid Kamangir, Scott A. King, Alaa Sheta, Mohammad Pashaei, Abbas SheikhMohammadZadeh
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:ISPRS International Journal of Geo-Information
Subjects:
urban transportation network
street functionality classification
stacked denoising autoencoder
deep learning
centrality measures
machine learning
Online Access:https://www.mdpi.com/2220-9964/9/7/456

Internet

https://www.mdpi.com/2220-9964/9/7/456

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