Artificial intelligence and mobile technology for urban assessment: a documentary study
DOI:
https://doi.org/10.5281/zenodo.22758435Keywords:
artificial intelligence, smartphones, urban assesmentAbstract
The objective of this study was to analyze trends in the use of smartphones and AI algorithms for urban pavement assessment. A documentary and descriptive methodological design was applied, systematizing technical variables from previous studies and evaluating the feasibility of crowdsourcing and citizen crowdsensing strategies. The main findings revealed that the accelerometer is the essential sensor for passive diagnosis, complemented by the gyroscope, which captures swaying movements in complex profiles. A transition was observed, moving from binary statistical roughness classification at 10 Hz using Support Vector Machines (SVMs) to continuous quantitative estimations with deep convolutional neural networks (IRI-Net) and predictive PCI mapping coupled with Geographic Information Systems (GIS). Furthermore, mobile crowdsensing and citizen crowdsourcing platforms demonstrate the potential for massive expansion of monitoring to secondary and peri-urban roads. The findings showed that the convergence between AI and mobile technology currently has sufficient technical maturity to accurately diagnose road infrastructure using commonly used sensors, resulting in a low-cost, highly scalable alternative to mitigate historical information asymmetries in public management.
Downloads
References
Abbondati, F., Verardi, F., Setaro, A., & Oreto, C. (2026). Pavement Roughness as a Multiscale Spatial Process: Insight from Crowdsensed Data. Sustainability, 18(12), 5796. https://doi.org/10.3390/SU18125796
Ahmed, S., Kuldeep, S., Rafa, S, Fazal, J., Hoque, M., Liu, G., & Gandomi, A (2024). Enhancement of traffic forecasting through graph neural network-based information fusion techniques. Information Fusion, 110, 102466. https://doi.org/10.1016/J.INFFUS.2024.102466
Alqaydi, S., Zeiada, W., El Wakil, A., Alnaqbi, A., & Azam, A. (2024). A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring. Eng, 5(4), 3397–3426. https://doi.org/10.3390/ENG5040177
Amândio, M., Parente, M., Neves, J., & Fonseca, P. (2021). Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review. Buildings. 11(12), 579. https://doi.org/10.3390/BUILDINGS11120579
Bermoy, L. (2026). Development and Validation of a Cloud-Based Road Surface Condition Monitoring Framework using Citizen-Sourced Smartphone Sensor Data in the Philippines. International Journal of Engineering and Manufacturing, 16(2), 196–213. https://doi.org/10.5815/IJEM.2026.02.13
Cano-Ortiz, S., Pascual-Muñoz, P., & Castro-Fresno, D. (2022). Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139, 104309. https://doi.org/10.1016/J.AUTCON.2022.104309
de Oliveira, R., Mazzoni, L., Savasini, K., de Almeida, F., & Ho, L. (2026). Experimental Study on the Impact of Driving Mode, Traffic, and Road Infrastructure on the Energy Consumption of Road Transport. Sustainability, 18(4), 2052. https://doi.org/10.3390/SU18042052
Jeong, J., & Jo, H. (2024). Toward Real-World Implementation of Deep Learning for Smartphone-Crowdsourced Pavement Condition Assessment. IEEE Internet of Things Journal, 11(4), 6328–6337. https://doi.org/10.1109/JIOT.2023.3312353
Justo-Silva, R., Ferreira, A., & Flintsch, G. (2021). Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13095248
Li, J., Yin, G., Wang, X., & Yan, W. (2022). Automated decision making in highway pavement preventive maintenance based on deep learning. Automation in Construction, 135, 104111. https://doi.org/10.1016/J.AUTCON.2021.104111
Liu, Y., Qin, S., Li, J., & Jin, T. (2023). Artificial Intelligence and Street Space Optimization in Green Cities: New Evidence from China. Sustainability, 15(23), 16367. https://doi.org/10.3390/SU152316367
Llopis-Castelló, D., Arce-Blanco, C., & Valdecantos-Álvarez, J. (2026). Before-after study of pavement roughness impact on vehicle fuel consumption and emissions. Transportation Research Part D: Transport and Environment, 151. https://doi.org/10.1016/J.TRD.2025.105131
Milad, A., Ali, A., Al-Sulaimi, Z., & Al-Kindi, K. (2025). Utilizing spatial artificial intelligence to develop pavement performance indices: a case study. Scientific Reports, 15(1). https://doi.org/10.1038/S41598-025-23290-7
Reis, M. (2025). Internet of Things and Artificial Intelligence for Secure and Sustainable Green Mobility: A Multimodal Data Fusion Approach to Enhance Efficiency and Security. Multimodal Technologies and Interaction, 9(5), 39. https://doi.org/10.3390/MTI9050039
Rouky, N., Benmoussa, O., Fri, M., Abourraja, M., & Ben-Bouazza, F. (2025). Artificial Intelligence Applications for Smart and Sustainable Mobility as a Service Concept: A Systematic Literature Review. Future Transportation, 5(3), 122. https://doi.org/10.3390/futuretransp5030122
Shulajkovska, M., Noveski, G., Smerkol, M., Grabnar, J., Dovgan, E., & Gams, M. (2023). EU Smart Cities: Towards a New Framework of Urban Digital Transformation. Informatica (Eslovenia), 47(2), 151–158. https://doi.org/10.31449/inf.v47i2.4904
Shulajkovska, M., Smerkol, M., Noveski, G., Bohanec, M., & Gams, M. (2024). Artificial Intelligence-Based Decision Support System for Sustainable Urban Mobility. Electronics, 13(18), 3655. https://doi.org/10.3390/ELECTRONICS13183655
Souza, V., Giusti, R., & Batista, A. (2018). Asfault: A low-cost system to evaluate pavement conditions in real-time using smartphones and machine learning. Pervasive and Mobile Computing, 51, 121–137. https://doi.org/10.1016/J.PMCJ.2018.10.008
Yigitcanlar, T., David, A., Li, W., Fookes, C., Bibri, S., & Ye, X. (2024). Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations. Smart Cities, 7(4), 1576–1625. https://doi.org/10.3390/SMARTCITIES7040064
Yiliguoqi, B., Siriguleng, B., Arong, L., Guo, G., & Wang, J. (2026). Research on the evaluation and analysis of road surface roughness based on smartphone sensors and SVM. Scientific Reports, 16(1), 4409-. https://doi.org/10.1038/s41598-025-34396-3
Yu, J., & Hyland, M. (2025). Interpretable State-Space Model of Urban Dynamics for Human-Machine Collaborative Transportation Planning. Transportation Research Part B: Methodological, 192. https://doi.org/10.1016/j.trb.2024.103134
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal is licensed under the Attribution License (CC BY), allowing the possibility of copying, distributing, displaying, and producing derivative works, as long as the author is acknowledged and cited.
