Leveraging Mobile Mapping System for Pavement Condition Monitoring and GIS Asset Management
Abstract
The Government of Indonesia, mandated PT Hutama Karya to develop and manage Trans Sumatera Toll Road (JTTS), spanning 2,844 km of strategic toll road infrastructure. By 2025, approximately 848 km will be operational, necessitating robust maintenance and monitoring mechanisms to ensure service quality, safety, and asset longevity. This study investigates the application of Mobile Mapping System (MMS) technology for pavement inspection and asset management along a 10 km section of JTTS. The methodology integrates LiDAR-derived point cloud data and 360° imagery to detect and classify pavement damage, supported by spatial segmentation at 5 m intervals. LiDAR processing identified surface deformations by analyzing elevation differentials, while imagery enabled manual digitization of damage features in georeferenced coordinates. Damage attributes were consolidated into shapefiles and integrated into a GIS-based dashboard, enabling visualization of severity levels, asset condition indices, and segment-wise distributions. The dashboard analysis indicates 64,023 segments in good condition and 8,995 in poor condition, offering actionable insights for maintenance prioritization and supporting evidence-based decision-making in toll road asset management.
References
[1] A. Karduni, A. Kermanshah, and S. Derrible. A protocol to convert spatial polyline data to network formats and applications to world urban road networks. Scientific Data 2016, Volume 3, DOI: 10.1038/sdata.2016.46.
[2] A. Mazhindu and H. Madamombe. Design and Implementation of a Web-GIS for the management of road infrastructure in Zimbabwe. South African Journal of Geomatics. 2022, Volume 11. No 11. DOI: 10.4314/sajg.v11i1.5.
[3] O. Al-Bayari. Mobile mapping systems in civil engineering projects (case studies). Appl Geomat. Volume 11, No 11. 2019. Doi: 10.1007/s12518-018-0222-6
[4] B. Wang, Z. Long, X. Chen, C. Feng, M. Zhao, D. Sun, W. Wang, and S. Wang. Research on LiDAR point cloud data transformation method based on weighted altitude difference map. Frontiers in Physics. 2024. Volume 2. DOI: 10.3389/fphy.2024.1387717.
[5] E. Che, J. Jung, and M. J. Olsen. Object recognition, segmentation, and classification of mobile laser scanning point clouds: A state-of-the-art review. Sensors, 2019. Volume 19. No. 4. DOI: 10.3390/s19040810.
[6] F. Li, Z. Zhou, J. Xiao, R. Chen, M. Lehtomäki, S. Elberink, G. Vosselman, J. Hyyppä, Y. Chen, and A. Kukko. Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data. IEEE Transactions on Intelligent Transportation Systems. 2022 Volume 23. DOI: 10.1109/TITS.2022.3157611.
[7] F. Zhang, J. Zhou, R. Liu, Z. Du, and X. Ye. A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability. Sustainability. 2016. Volume. 8, pp. 1-19. DOI: 10.3390/SU8090926.
[8] H. Dong, K. Song, Y. Wang, Y. Yan, and P. Jiang. Automatic Inspection and Evaluation System for Pavement Distress. IEEE Transactions on Intelligent Transportation Systems. 2021. Volume. 23, pp. 12377-12387. DOI: 10.1109/TITS.2021.3113802.
[9] H. Fan, B. Yang, A. Zipf, and A. Rousell. A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data. International Journal of Geographical Information Science. 2016. Volume. 30, pp. 748-764. DOI: 10.1080/13658816.2015.1100732.
[10] H. Guan, J. Li, Y. Yu, M. Chapman, and C. Wang. Automated road information extraction from mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems. 2015. Volume. 16, no. 1, pp. 194-206, DOI: 10.1109/TITS.2014.2328589.
[11] H. Metsaranta. Standardized Level-of-Service Scale for the Condition of the Road Assets - Bases, Present Situation and a Recommendation for a Scale. unpublished report. 2004.
[12] I. Hermawan et al. Evaluation of Precision and Accuracy of Mobile Mapping System (MMS) for Asset Monitoring. 2023.
J. Jhan, N. Kerle, and J. Rau. Integrating UAV and Ground Panoramic Images for Point Cloud Analysis of Damaged Building. IEEE [13] Geoscience and Remote Sensing Letters. 2022. Volume. 19, pp. 1-5. DOI: 10.1109/LGRS.2020.3048150.
[14] M. Mehdi, T. Cherradi, S. Karkouri, and A. Qachar. Applying Geographic Information Systems (GIS) for surface condition indicators modeling of a flexible pavement. E3S Web of Conferences, 2021. DOI: 10.1051/e3sconf/202129804001.
[15] O. Hakimi, H. Liu, O. Abudayyeh, A. Houshyar, M. Almatared, and A. Alhawiti. Data Fusion for Smart Civil Infrastructure Management: A Conceptual Digital Twin Framework. Buildings. 2023. Volume. 13, no. 2725. DOI: 10.3390/buildings13112725
[16] P. Dudhbale, S. Ghodmare, and R. Mane. Innovative Approaches in Road Condition Monitoring: Synergizing Mobile Mapping Systems and High-Resolution Data Analytics. 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). Trichy, India, 2024. pp. 1626-1633, DOI: 10.1109/ICOSEC61587.2024.10722726.
[17] R. Alakus. Developing Spatial Asset Management and Maintenance Processes for Roads and Pavements. unpublished report, Volume. 2. 2009.
[18] S. Alkobaisi, W. Bae, P. Vojtěchovský, and S. Narayanappa. An interactive framework for spatial joins: a statistical approach to data analysis in GIS. GeoInformatica. 2012. Volume 16, pp. 329-355, DOI: 10.1007/s10707-011-0134-7.
[19] S. Radopoulou and I. Brilakis. Automated Detection of Multiple Pavement Defects. Journal of Computing in Civil Engineering, 2017. Volume 31, DOI: 10.1061/(ASCE)CP.1943-5487.0000623.
[20] X. Weng, Y. Huang, and W. Wang. Segment-based pavement crack quantification. Automation in Construction. 2017. Volume 105, p. 102819, DOI: 10.1016/j.autcon.2019.04.014.
[21] Y. Tsai and Z. Wang, A remote sensing and GIS-enabled asset management system (RS-GAMS). Georgia Institute of Technology, 2013.
[22] Y. Wang, Q. Chen, Q. Zhu, L. Liu, C. Li, and D. Zheng. A survey of mobile laser scanning applications and key techniques over urban areas. Remote Sensing, 2019. Volume 11, No. 13, p. 1540, DOI: 10.3390/rs11131540.