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

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dc.contributor.authorNoori, Fatemeh
dc.contributor.authorKamangir, Hamid
dc.contributor.authorKing, Scott A.
dc.contributor.authorSheta, Alaa
dc.contributor.authorPashaei, Mohammad
dc.contributor.authorSheikhMohammadZadeh, Abbas
dc.creator.orcidhttps://orcid.org/0000-0002-4022-0388en_US
dc.creator.orcidhttps://orcid.org/0000-0002-3727-6276en_US
dc.creator.orcidhttps://orcid.org/0000-0002-1427-6265en_US
dc.creator.orcidhttps://orcid.org/0000-0002-4022-0388
dc.creator.orcidhttps://orcid.org/0000-0002-3727-6276
dc.creator.orcidhttps://orcid.org/0000-0002-1427-6265
dc.creator.orcidhttps://orcid.org/0000-0002-4022-0388
dc.creator.orcidhttps://orcid.org/0000-0002-3727-6276
dc.creator.orcidhttps://orcid.org/0000-0002-1427-6265https://orcid.org/0000-0002-4022-0388
dc.creator.orcidhttps://orcid.org/0000-0002-3727-6276
dc.creator.orcidhttps://orcid.org/0000-0002-1427-6265
dc.date.accessioned2021-10-26T18:58:33Z
dc.date.available2021-10-26T18:58:33Z
dc.date.issued2020-07-20
dc.description.abstractIn 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.en_US
dc.description.abstractIn 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.
dc.identifier.citationNoori, F., Kamangir, H., A King, S., Sheta, A., Pashaei, M. and 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), p.456.en_US
dc.identifier.citationNoori, F., Kamangir, H., A King, S., Sheta, A., Pashaei, M. and 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), p.456.
dc.identifier.doihttps://doi.org/10.3390/ijgi9070456
dc.identifier.urihttps://hdl.handle.net/1969.6/89856
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherMDPIen_US
dc.publisherMDPI
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjecturban transportation networken_US
dc.subjectstreet functionality classificationen_US
dc.subjectstacked denoising autoencoderen_US
dc.subjectdeep-learningen_US
dc.subjectcentrality measuresen_US
dc.subjectmachine learningen_US
dc.subjecturban transportation network
dc.subjectstreet functionality classification
dc.subjectstacked denoising autoencoder
dc.subjectdeep-learning
dc.subjectcentrality measures
dc.subjectmachine learning
dc.titleA deep learning approach to urban street functionality prediction based on centrality measures and stacked denoising autoencoderen_US
dc.titleA deep learning approach to urban street functionality prediction based on centrality measures and stacked denoising autoencoder
dc.typeArticleen_US
dc.typeArticle

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