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Opportunities and Prospects of Artificial Intelligence–Based Analysis of Urban Pedestrian Infrastructure

Field of Science:Civil and Structural EngineeringEngineering (miscellaneous)
National field of science (HAC):01.02.02 — Dynamics and strength of machines, instruments and equipment01.02.03 — Mechanics of soils and rocks01.02.04 — Mechanics of deformable solids01.02.05 — Fluid and gas mechanics01.04.01 — Instruments and methods of experimental physics01.04.04 — Physical electronics01.04.10 — Semiconductor physics04.00.10 — Geotechnology (open-pit, underground and construction)04.00.04 — Hydrogeology and engineering geology04.00.09 — Mine surveying04.00.11 — Well drilling and development technology04.00.12 — Construction and operation of oil and gas pipelines, bases and storage facilities04.00.13 — Development and operation of oil and gas fields04.00.14 — Mineral processing04.00.15 — Technology and engineering of geological exploration04.00.16 — Mining machinery04.00.17 — Physical processes in mining05.01.01 — Engineering geometry and computer graphics. 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International journal of road and urban infrastructure engineering

International journal of road and urban infrastructure engineering

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ARTICLE ANNOTATION

quote
This study explores the potential of artificial intelligence (AI) technologies for analyzing urban pedestrian infrastructure. The research focuses on the application of computer vision techniques, machine learning algorithms, and Geographic Information Systems (GIS) to automatically assess the condition of sidewalks, detect obstacles, and evaluate key safety and comfort indicators. The results demonstrate that AI-based approaches enable efficient, objective, and scalable assessment of pedestrian infrastructure. Such methods support evidence-based decision-making in urban transport planning, help identify priority intervention areas, and contribute to the development of more inclusive and sustainable cities.

AUTHORS

Science ID: FNV-0826-0037

Tags

# artificial intelligence# computer vision# sustainable development# urban mobility# GIS# pedestrian infrastructure

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Article Identifiers

ROI:

https://eroi.uz/11.4/A026-491462

DOI:

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References

Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3-6. https://doi.org/10.1177/2399808317751169

Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, Antonio Torralba; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 633-641

Heaton, Jeffrey. (2017). Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618. Genetic Programming and Evolvable Machines. 19. 10.1007/s10710-017-9314-z.

Arianna Salazar Miranda, Zhuangyuan Fan, Fabio Duarte, Carlo Ratti, Desirable streets: Using deviations in pedestrian trajectories to measure the value of the built environment, Computers, Environment and Urban Systems, Volume 86, 2021, 101563, ISSN 0198-9715, https://doi.org/10.1016/j.compenvurbsys.2020.101563.

Fan Zhang, Lun Wu, Di Zhu, Yu Liu, Social sensing from street-level imagery: A case study in learning spatio-temporal urban mobility patterns, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 153, 2019, Pages 48-58, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2019.04.017.

Biljecki, Filip & Ito, Koichi. (2021). Street view imagery in urban analytics and GIS: A review. Landscape and Urban Planning. 215. 104217. 10.1016/j.landurbplan.2021.104217