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Article

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

1
Department of Geomatics, Civil Engineering, Shahid Rajaee Teacher Training University, Lavizan 1678815811, Iran
2
Department of Computing Sciences, Texas A&M university- Corpus Christi, Corpus Christi, TX 78412, USA
3
Computer Science Department, Southern Connecticut State University, New Haven, CT 06515, USA
4
Department of Civil, Geological, and Mining Engineering, Polytechnique Montréal, QC H3T 1J4, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2020, 9(7), 456; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070456
Received: 6 June 2020 / Revised: 13 July 2020 / Accepted: 15 July 2020 / Published: 20 July 2020
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 streets to provide stakeholders with a group of street guidelines for possible new rehabilitation such as sidewalks, curbs, and setbacks. Transportation research always considers street networks as a connection between different urban areas. The street functionality classification defines the role of each element of the urban street network (USN). Some potential factors such as land use mix, accessible service, design goal, and administrators’ policies can affect the movement pattern of urban travelers. In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street segments. In our work, a Stacked Denoising Autoencoder (SDAE) predicts a street’s functionality, then logistic regression is used as a classifier. Our proposed classifier can differentiate between four different classes adopted from the U.S. Department of Transportation (USDT): principal arterial road, minor arterial road, collector road, and local road. The SDAE-based model showed that regular grid configurations with repeated patterns are more influential in forming the functionality of road networks compared to those with less regularity in their spatial structure. View Full-Text
Keywords: urban transportation network; street functionality classification; stacked denoising autoencoder; deep learning; centrality measures; machine learning urban transportation network; street functionality classification; stacked denoising autoencoder; deep learning; centrality measures; machine learning
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MDPI and ACS Style

Noori, F.; Kamangir, H.; A. King, S.; Sheta, A.; Pashaei, M.; SheikhMohammadZadeh, A. A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder. ISPRS Int. J. Geo-Inf. 2020, 9, 456. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070456

AMA Style

Noori F, Kamangir H, A. King S, Sheta A, Pashaei M, SheikhMohammadZadeh A. A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder. ISPRS International Journal of Geo-Information. 2020; 9(7):456. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070456

Chicago/Turabian Style

Noori, Fatemeh, Hamid Kamangir, Scott A. King, Alaa Sheta, Mohammad Pashaei, and Abbas SheikhMohammadZadeh. 2020. "A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder" ISPRS International Journal of Geo-Information 9, no. 7: 456. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070456

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