Methodology for Calculating the International Roughness Index (IRI) from Mobile LiDAR
DOI:
https://doi.org/10.26439/ciic2025.8669Palabras clave:
International Roughness Index (IRI), mobile LiDAR, pavements, point clouds, road surfaceResumen
Pavement Management Systems integrate advanced decision-making tools for road management. This research addresses the automated approach for analyzing the light detection and ranging (LiDAR) data for road management. The proposed framework integrates machine learning techniques with advanced data-processing methodologies to estimate the International Roughness Index (IRI) using mobile LiDAR measurements. The data were obtained from the publicly available Lille2 and IQmulus point-cloud datasets, captured using the L3D2 mobile mapping systems. Automatic extraction of the rolling surface was performed on these datasets, enabling the subsequent automated generation of pavement profiles. In addition, the layout of edges, axes and profiles was automated. These alignments provided the corresponding Z-coordinates required for IRI computation, enabling faster and more accurate calculations. For both clouds, the differences observed in the global IRI values calculated from averaged reference profiles are small—ranging from 0.15 to 0.29 m/km for Lille2 and from 0.03 to 0.04 m/km for IQmulus. In contrast, the differences between these values and the IRI obtained from profiles generated using the simple method are substantially larger, ranging from 8.98 to 11.59 m/km for Lille2 and from 0.47 to 0.65 m/km for IQmulus. The results of this work will not only contribute to academic development in this field, but also to the practical implementation of more modern, efficient, and accurate systems for road network assessment and maintenance.
Descargas
Referencias
[1] Federal Highway Administration, Asset Management Overview, FHWA-IF-08-008, U.S. Department of Transportation, Office of Asset Management, Washington, D.C., Dec. 2007. [Online]. Available: https://www.fhwa.dot.gov/asset/if08008/assetmgmt_overview.pdf?
[2] American Association of State Highway and Transportation Officials (AASHTO), Transportation Asset Management Guide: A Focus on Implementation, 2nd ed., ch. 1, “Introduction” (secs. 1.1–1.3). Washington, D.C.: AASHTO, Feb. 27, 2020. [Online]. Available: https://www.tamguide.com/wp-content/uploads/2020/02/TAM_GuideIII_ch01-20200227.pdf?
[3] J. R. Quintero González, “Inventarios viales y categorización de la red vial en estudios de Ingeniería de Tránsito y Transporte,” Revista Facultad de Ingeniería, UPTC, vol. 20, no. 30, pp. 65–77, 2011. [Online]. Available: https://dialnet.unirioja.es/descarga/articulo/3758451.pdf
[4] M. W. Sayers, “On the Calculation of International Roughness Index from Longitudinal Road Profile,” Transportation Research Record: Journal of the Transportation Research Board, no. 1501, pp. 1–12, 1995. [Online]. Available: http://onlinepubs.trb.org/Onlinepubs/trr/1995/1501/1501-001.pdf
[5] K. Williams, M. Olsen, G. Roe, and C. Glennie, “Synthesis of Transportation Applications of Mobile LIDAR,” Remote Sensing, vol. 5, no. 9, pp. 4652–4692, Sep. 2013, doi: https://doi.org/10.3390/rs5094652
[6] X. Roynard, J.-E. Deschaud, and F. Goulette, “Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification,” The International Journal of Robotics Research, vol. 37, no. 6, pp. 545–557, Apr. 2018, doi: https://doi.org/10.1177/0278364918767506
[7] B. Vallet, M. Brédif, A. Serna, B. Marcotegui, and N. Paparoditis, “TerraMobilita/iQmulus urban point cloud analysis benchmark,” Computers & Graphics, vol. 49, pp. 126–133, Jun. 2015, doi: https://doi.org/10.1016/j.cag.2015.03.004
[8] F. Goulette, F. Nashashibi, I. Abuhadrous, S. Ammoun, and C. Laurgeau, “An integrated on-board laser range sensing system for on-the-way city and road modeling,” Revue Française de Photogrammétrie et de Télédétection, no. 185, pp. 78-83, 2007.
[9] N. Paparoditis et al., “Stereopolis II: A multi-purpose and multi-sensor 3D mobile mapping system for street visualisation and 3D metrology,” Revue française de photogrammétrie et de télédétection, no. 200, pp. 69–79, Apr. 2014, doi: https://doi.org/10.52638/rfpt.2012.63
[10] H. Edelsbrunner, D. Kirkpatrick, and R. Seidel, “On the shape of a set of points in the plane,” IEEE Transactions on Information Theory, vol. 29, no. 4, pp. 551–559, Jul. 1983, doi: https://doi.org/10.1109/tit.1983.1056714
[11] F. P. Preparata and M. I. Shamos, Computational Geometry: An Introduction, New York / Berlin / Heidelberg: Springer, 1985. doi: https://doi.org/10.1007/978-1-4612-1098-6
[12] T. Křemen, M. Štroner, and P. Třasák, “Determination of Pavement Elevations by the 3D Scanning System and Its Verification,” Geoinformatics FCE CTU, vol. 12, pp. 55–60, Jun. 2014, doi: https://doi.org/10.14311/gi.12.9
[13] M. W. Sayers and S. M. Karamihas, The Little Book of Profiling: Basic Information about Measuring and Interpreting Road Profiles, Ann Arbor, MI: University of Michigan Transportation Research Institute, Sep. 1998. [Online]. Available: https://hdl.handle.net/2027.42/21605
[14] M. J. Olsen et al., Guidelines for the Use of Mobile LiDAR in Transportation Applications, NCHRP Report 748, Transportation Research Board, Washington, D.C., 2013. [Online]. Available: https://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_748.pdf
[15] M. R. De Blasiis, A. Di Benedetto, M. Fiani, and M. Garozzo, “Assessing of the Road Pavement Roughness by Means of LiDAR Technology,” Coatings, vol. 11, no. 1, Art. no. 17, Dec. 2020, doi: https://doi.org/10.3390/coatings11010017
[16] B. Suleymanoglu, M. Soycan, and C. Toth, “3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds,” Sensors, vol. 24, no. 2, Art. no. 503, Jan. 2024, doi: https://doi.org/10.3390/s24020503
[17] Y. Wang et al., “Framework for Geometric Information Extraction and Digital Modeling from LiDAR Data of Road Scenarios,” Remote Sensing, vol. 15, no. 3, Art. no. 576, Jan. 2023, doi: https://doi.org/10.3390/rs15030576
[18] J. Wang, X. Dong, and G. Liu, “Extraction of Urban Road Boundary Points from Mobile Laser Scanning Data Based on Cuboid Voxel,” ISPRS International Journal of Geo-Information, vol. 12, no. 10, Art. no. 426, Oct. 2023, doi: https://doi.org/10.3390/ijgi12100426
[19] Z. Yahya, R. W. Rahmat, F. Khalid, A. Rizaan, and A. Rizal, “A Concave Hull Based Algorithm for Object Shape Reconstruction,” International Journal of Information Technology and Computer Science, vol. 9, no. 3, pp. 1–9, Mar. 2017, doi: https://doi.org/10.5815/ijitcs.2017.03.01
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Actas del Congreso Internacional de Ingeniería Civil (CIIC)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
