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A deep learning approach to urban street functionality prediction based on centrality measures and stacked denoising autoencoder

Fatemeh Noori, Hamid Kamangir, Scott A. King, Alaa Sheta, Mohammad Pashaei and Abbas SheikhMohammadZadeh

Article (2020)

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Abstract

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.

Uncontrolled Keywords

urban transportation network; street functionality classification; stacked denoising autoencoder; deep learning; centrality measures; machine learning

Subjects: 1000 Civil engineering > 1000 Civil engineering
1000 Civil engineering > 1003 Transportation engineering
Department: Department of Civil, Geological and Mining Engineering
PolyPublie URL: https://publications.polymtl.ca/9390/
Journal Title: ISPRS International Journal of Geo-Information (vol. 9, no. 7)
Publisher: MDPI
DOI: 10.3390/ijgi9070456
Official URL: https://doi.org/10.3390/ijgi9070456
Date Deposited: 16 Aug 2023 12:27
Last Modified: 11 Apr 2024 08:00
Cite in APA 7: Noori, F., Kamangir, H., King, S. A., Sheta, A., Pashaei, M., & SheikhMohammadZadeh, A. (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(7), 23 pages. https://doi.org/10.3390/ijgi9070456

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