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

Datos Bibliográficos

ID22031562
AutoresFatemeh Noori (Shahid Rajaee Teacher Training University), Hamid Kamangir (0000-0001-9718-7518, Texas A&M University – Corpus Christi, autor de correspondencia), Scott A King (0000-0002-4022-0388, Texas A&M University – Corpus Christi), Alaa Sheta (0000-0002-3727-6276, Southern Connecticut State University), Mohammad Pashaei (0000-0002-1427-6265, Texas A&M University – Corpus Christi), Abbas Sheikh-Mohammad-Zadeh (0000-0003-4799-7680, Polytechnique Montréal), Abbas SheikhMohammadZadeh (Polytechnique Montréal)
Año2020
Volumen9
Número7
Páginas456
Fecha de publicación2020-07-20
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaISPRS International Journal of Geo-Information (JOURNAL)
Identificadores de la revistaISSN: 2220-9964 • E-ISSN: 2220-9964
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9070456
OpenAlexW3042473914
IdiomaEN
Citas recibidas1
Referencias citadas53

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

Autoencoder · Centrality · Deep learning · Geography · Street network · Transport engineering · Automated Road and Building Extraction · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Mathematics · Urban Design and Spatial Analysis · Artificial Intelligence