International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 16, No. 2, 8 Apr. 2026
Cover page and Table of Contents: PDF (size: 1371KB)
Development and Validation of a Cloud-Based Road Surface Condition Monitoring Framework using Citizen-Sourced Smartphone Sensor Data in the Philippines
PDF (1371KB), PP.196-213
Views: 0 Downloads: 0
Author(s)
Lyndon R. Bermoy
1,*
Index Terms
Mobile sensing, Road surface condition monitoring, smartphone accelerometer, cloud-based analytics, geospatial visualization, infrastructure monitoring, machine learning classification
Abstract
This study presents the development and validation of a mobile-to-cloud road surface condition monitoring system using citizen-sourced smartphone sensor data in the Philippines. The proposed framework integrates mobile data acquisition, local preprocessing, secure cloud transmission, supervised classification, and geospatial visualization within a unified architecture. Tri-axial accelerometer and gyroscope signals were collected at 50 Hz, segmented into overlapping windows, and transformed into statistical feature vectors prior to cloud-based inference. Field deployment was conducted across 48 urban and peri-urban road segments, generating 12,485 processed feature windows. A labeled subset of 4,200 windows was used for supervised evaluation. The classification model achieved an overall accuracy of 88.4%, with balanced precision and recall across smooth, moderate, and severe surface condition categories. Confusion matrix analysis showed that misclassifications were primarily concentrated between adjacent condition levels rather than between extreme classes. System-level evaluation demonstrated near-real-time responsiveness, with an average end-to-end latency of 1.8 seconds under stable network conditions. Offline buffering achieved 100% synchronization success following connectivity restoration, ensuring data integrity in environments with intermittent network coverage. The results indicate that smartphone-based vibration sensing, when integrated with cloud analytics and geospatial visualization, provides a scalable and cost-efficient approach for preliminary road surface monitoring. The proposed framework offers a practical complement to conventional inspection methods and supports data-driven infrastructure prioritization in developing urban contexts.
Cite This Paper
Lyndon R. Bermoy, "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 (IJEM), Vol.16, No.2, pp.196-213, 2026. DOI:10.5815/ijem.2026.02.13
Reference
[1]P. Mohan, V. N. Padmanabhan, and R. Ramjee, “Nericell: Rich Monitoring of Road and Traffic Conditions Using Mobile Smartphones,” in Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys), 2008, pp. 323–336. https://doi.org/10.1145/1460412.1460444
[2]J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, and H. Balakrishnan, “The Pothole Patrol: Using a Mobile Sensor Network for Road Surface Monitoring,” in Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services (MobiSys), 2008, pp. 29–39. https://doi.org/10.1145/1378600.1378605
[3]A. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs, and L. Selavo, “Real Time Pothole Detection Using Android Smartphones with Accelerometers,” in 2011 International Conference on Distributed Computing in Sensor Systems Workshops (DCOSS Workshops), 2011, pp. 1–6. https://doi.org/10.1109/DCOSS.2011.5982206
[4]S. Sattar, S. Li, and M. Chapman, “Road Surface Monitoring Using Smartphone Sensors: A Review,” Sensors, vol. 18, no. 11, Art. no. 3845, 2018. https://doi.org/10.3390/s18113845
[5]M. Staniek, “Road Pavement Condition Diagnostics Using Smartphone-Based Data Crowdsourcing in Smart Cities,” Journal of Traffic and Transportation Engineering (English Edition), vol. 8, no. 4, pp. 554–567, 2021. https://doi.org/10.1016/j.jtte.2020.09.004
[6]O. A. Egaji, G. Evans, M. Griffiths, and G. Islas, “Real-Time Machine Learning-Based Approach for Pothole Detection,” Expert Systems with Applications, vol. 184, Art. no. 115562, 2021. https://doi.org/10.1016/j.eswa.2021.115562
[7]D. Arya, H. Maeda, S. K. Ghosh, D. Toshniwal, and Y. Sekimoto, “RDD2020: An Annotated Image Dataset for Automatic Road Damage Detection Using Deep Learning,” Data in Brief, vol. 36, Art. no. 107133, 2021. https://doi.org/10.1016/j.dib.2021.107133
[8]J. M. C. Manalo, A. S. Alon, Y. D. Austria, N. E. Merencilla, M. A. Misola, and R. C. Sandil, “A Transfer Learning-Based System of Pothole Detection in Roads through Deep Convolutional Neural Networks,” in 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022, pp. 1469–1473. https://doi.org/10.1109/DASA54658.2022.9765227
[9]L. V. Fortin, A. R. V. Delos Santos, P. J. B. Cagud, P. R. Castor, and O. E. Llantos, “Eyeway: An Artificial Intelligence Of Things Pothole Detection System With Map Visualization,” Procedia Computer Science, vol. 251, pp. 216–223, 2024. https://doi.org/10.1016/j.procs.2024.11.103
[10]L. R. Bermoy and J. E. Sanchez, “Development and Evaluation of a Photoluminescent–Reflective Cat’s Eye Road Stud for Tropical Urban Road Safety,” International Journal of Science and Research Archive, vol. 18, no. 1, pp. 560–567, 2026. https://doi.org/10.30574/ijsra.2026.18.1.0097
[11]V. Douangphachanh and H. Oneyama, “Estimation of Road Roughness Condition from Smartphones under Realistic Settings,” in 2013 13th International Conference on ITS Telecommunications (ITST), 2013, pp. 433–439. https://doi.org/10.1109/ITST.2013.6685585
[12]H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, and H. Omata, “Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images,” Computer-Aided Civil and Infrastructure Engineering, 2018. https://doi.org/10.1111/mice.12387
[13]F. Balcı and S. Yılmaz, “Faster R-CNN Structure for Computer Vision-Based Road Pavement Distress Detection,” Journal of Polytechnic, vol. 26, no. 2, pp. 701–710, 2023. https://doi.org/10.2339/politeknik.98713
[14]B. Varona and A. Monteserin, “A deep learning approach to automatic road surface monitoring and pothole detection,” Personal and Ubiquitous Computing, vol. 24, pp. 519–534, 2020. https://doi.org/10.1007/s00779-019-01234-z
[15]A. Allouch, A. Koubaa, T. Abbes, and A. Ammar, “Trigger-Based Pothole Detection Using Smartphone and OBD-II,” in 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2020. https://doi.org/10.1109/CONECCT50063.2020.9198602
[16]T. Nguyen, L. Tran, and V. Pham, “Real-Time Pavement Distress Mapping with Smartphone IMU and Cloud-Based Random Forest Classification,” Sensors, vol. 23, no. 8, Art. no. 3992, 2023. https://doi.org/10.3390/s23083992