{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T21:47:29Z","timestamp":1771624049146,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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\u2019 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/ijgi9070456","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T06:08:17Z","timestamp":1595225297000},"page":"456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder"],"prefix":"10.3390","volume":"9","author":[{"given":"Fatemeh","family":"Noori","sequence":"first","affiliation":[{"name":"Department of Geomatics, Civil Engineering, Shahid Rajaee Teacher Training University, Lavizan 1678815811, Iran"}]},{"given":"Hamid","family":"Kamangir","sequence":"additional","affiliation":[{"name":"Department of Computing Sciences, Texas A&amp;M university- Corpus Christi, Corpus Christi, TX 78412, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-0388","authenticated-orcid":false,"given":"Scott","family":"A. King","sequence":"additional","affiliation":[{"name":"Department of Computing Sciences, Texas A&amp;M university- Corpus Christi, Corpus Christi, TX 78412, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-6276","authenticated-orcid":false,"given":"Alaa","family":"Sheta","sequence":"additional","affiliation":[{"name":"Computer Science Department, Southern Connecticut State University, New Haven, CT 06515, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1427-6265","authenticated-orcid":false,"given":"Mohammad","family":"Pashaei","sequence":"additional","affiliation":[{"name":"Department of Computing Sciences, Texas A&amp;M university- Corpus Christi, Corpus Christi, TX 78412, USA"}]},{"given":"Abbas","family":"SheikhMohammadZadeh","sequence":"additional","affiliation":[{"name":"Department of Civil, Geological, and Mining Engineering, Polytechnique Montr\u00e9al, QC H3T 1J4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1111\/j.1467-9671.2004.00186.x","article-title":"Selection of streets from a network using self-organizing maps","volume":"8","author":"Jiang","year":"2004","journal-title":"Trans. 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