An Automated Sound Barrier Inventory Method Using Mobile LiDAR
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 10
Abstract
A sound barrier, also called a noise barrier, plays an irreplaceable role in traffic noise abatement. The Federal Highway Administration’s (FHWA) highway noise regulation requires each state highway agency to maintain a complete inventory of all constructed noise-abatement features. Although key information for most of the newly constructed sound barriers has been inventoried, public transportation agencies are still struggling to keep close track of the in-service barriers because their inventory information is nonexisting, and manual inventory remains time-consuming, labor-intensive, and often dangerous. Therefore, many agencies have shown more interest in exploring the possibility of using light detection and ranging (LiDAR) data for assisting in sound barrier inventory, thanks to the widely available data set and much-improved data quality. This study proposes a LiDAR-based sound barrier inventory method to automatically extract the sound barrier’s location and measure the corresponding geometry. The extraction of a sound barrier is achieved using its unique features after random sample consensus (RANSAC)-based ground extraction and region-growing segmentation. The geometry measurement of the sound barrier is performed by analyzing the detailed dimension of the extracted point cloud, including location, height, length, and lateral offset. The experimental test conducted near Carver, Massachusetts showed the results with a precision rate of 99.9% and a recall rate of 93.8%. Moreover, the outcome of the experimental test has demonstrated the robustness of the proposed method in different complexities of the background and sound barrier types (linear and zigzag). This study has demonstrated the feasibility of using LiDAR for effectively inventorying in-service barriers. Besides the critical application of asset management, the detailed location and geometry information provided by the proposed method can provide valuable insight for other critical applications, such as traffic noise modeling.
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Data Availability Statement
Some data and models that support the findings of this study are available from the corresponding author upon reasonable request.
References
Anderson, G. S., J. C. Ross, C. W. Menge, and L. B. Arnold. 2003. “Absorptive sound barriers: Effects of three potential changes to current design standards of Virginia Department of Transportation.” Transp. Res. Rec. 1859 (1): 45–52. https://doi.org/10.3141/1859-06.
Anzola, J., J. Pascual, G. Tarazona, and R. Gonzalez Crespo. 2018. “A clustering WSN routing protocol based on KD tree algorithm.” Sensors 18 (9): 2899. https://doi.org/10.3390/s18092899.
Balado, J., L. Díaz-Vilariño, P. Arias, and H. González-Jorge. 2018. “Automatic classification of urban ground elements from mobile laser scanning data.” Autom. Constr. 86 (Feb): 226–239. https://doi.org/10.1016/j.autcon.2017.09.004.
Balado, J., J. Martínez-Sánchez, P. Arias, and A. Novo. 2019. “Road environment semantic segmentation with deep learning from MLS point cloud data.” Sensors 19 (16): 3466. https://doi.org/10.3390/s19163466.
Canaz Sevgen, S., and F. Karsli. 2020. “An improved RANSAC algorithm for extracting roof planes from airborne LIDAR data.” Photogramm. Rec. 35 (169): 40–57. https://doi.org/10.1111/phor.12296.
Che, E., M. J. Olsen, C. E. Parrish, and J. Jung. 2019. “Pavement marking retroreflectivity estimation and evaluation using mobile LIDAR data.” Photogramm. Eng. Remote Sens. 85 (8): 573–583. https://doi.org/10.14358/PERS.85.8.573.
Clark, C., and K. Paunovic. 2018. “Who environmental noise guidelines for the European region: A systematic review on environmental noise and quality of life, wellbeing and mental health.” Int. J. Environ. Res. Public Health 15 (11): 2400. https://doi.org/10.3390/ijerph15112400.
Cottrell, B. H. 1982. Guidelines for the design and placement of curb ramps. Charlottesville, VA: Virginia Transportation Research Council.
Ebrahimi, A., and S. Czarnuch. 2021. “Automatic super-surface removal in complex 3D indoor environments using iterative region-based RANSAC.” Sensors 21 (11): 3724. https://doi.org/10.3390/s21113724.
FDOT (Florida DOT). 2014. “Florida Department of Transportation noise abatement barriers–2014.” Accessed September 16, 2021. https://www.fgdl.org/metadata/metadata_archive/fgdc_html/noise_barriers_sep14.fgdc.htm.
FHWA (Federal Highway Administration). 2010. “23 CFR 772.13(f): §772.13 analysis of noise abatement.” Accessed September 16, 2021. https://www.ecfr.gov/current/title-23/chapter-I/subchapter-H/part-772/section-772.13.
FHWA (Federal Highway Administration). 2021. “Summary of noise barriers constructed by December 31, 2019.” Accessed October 6, 2021. https://www.fhwa.dot.gov/environment/noise/noise_barriers/inventory/.
Fine, A., J. Bartak, and C. Systematics. 2012. Applications of geographic information systems (GIS) for highway traffic noise analysis: Case studies of select transportation agencies. Cambridge, MA: John A. Volpe National Transportation Systems Center.
Fischler, M. A., and R. C. Bolles. 1981. “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography.” Commun. ACM 24 (6): 381–395. https://doi.org/10.1145/358669.358692.
Gargoum, S., K. El-Basyouny, J. Sabbagh, and K. Froese. 2017. “Automated highway sign extraction using LIDAR data.” Transp. Res. Rec. 2643 (1): 1–8. https://doi.org/10.3141/2643-01.
Grilli, E., F. Menna, and F. Remondino. 2017. “A review of point clouds segmentation and classification algorithms.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-2/W3: 339–344. https://doi.org/10.5194/isprs-archives-XLII-2-W3-339-2017.
Hata, A., and D. Wolf. 2014. “Road marking detection using LIDAR reflective intensity data and its application to vehicle localization.” In Proc., 17th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), 584–589. New York: IEEE.
Hervieu, A., and B. Soheilian. 2013. “Semi-automatic road/pavement modeling using mobile laser scanning.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. II-3/W3: 31–36. https://doi.org/10.5194/isprsannals-II-3-W3-31-2013.
Hou, Q., and C. Ai. 2020. “A network-level sidewalk inventory method using mobile LIDAR and deep learning.” Transp. Res. Part C Emerging Technol. 119 (Oct): 102772. https://doi.org/10.1016/j.trc.2020.102772.
Hou, Q., M. Cai, and H. Wang. 2017. “Dynamic modeling of traffic noise in both indoor and outdoor environments by using a ray tracing method.” Build. Environ. 121 (Aug): 225–237. https://doi.org/10.1016/j.buildenv.2017.05.031.
Jagannathan, A., and E. L. Miller. 2007. “Three-dimensional surface mesh segmentation using curvedness-based region growing approach.” IEEE Trans. Pattern Anal. Mach. Intell. 29 (12): 2195–2204. https://doi.org/10.1109/TPAMI.2007.1125.
Jung, J., E. Che, M. J. Olsen, and C. Parrish. 2019. “Efficient and robust lane marking extraction from mobile LIDAR point clouds.” ISPRS J. Photogramm. Remote Sens. 147 (Jan): 1–18. https://doi.org/10.1016/j.isprsjprs.2018.11.012.
Kaddoura, I., L. Kröger, and K. Nagel. 2017. “An activity-based and dynamic approach to calculate road traffic noise damages.” Transp. Res. Part D Transp. Environ. 54 (Jul): 335–347. https://doi.org/10.1016/j.trd.2017.06.005.
Kaiser, A., J. A. Ybanez Zepeda, and T. Boubekeur. 2019. A survey of simple geometric primitives detection methods for captured 3D data. New York: Wiley.
Khalilikhah, M., G. Fu, K. Heaslip, and P. Carlson. 2018. “Analysis of in-service traffic sign visual condition: Tree-based model for mobile LIDAR and digital photolog data.” J. Transp. Eng. Part A Syst. 144 (6): 04018017. https://doi.org/10.1061/JTEPBS.0000132.
Lu, X., J. Yao, J. Tu, K. Li, L. Li, and Y. Liu. 2016. “Pairwise linkage for point cloud segmentation.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. III-3: 201–208. https://doi.org/10.5194/isprs-annals-III-3-201-2016.
MassDOT. 2022. “Highway noise abatement inventory.” Accessed April 7, 2022. https://www.mass.gov/doc/noise-abatement-inventory/download.
Powers, D. M. 2020. “Evaluation: From precision, recall and f-measure to ROC, informedness, markedness and correlation.” Preprint, submitted December 15, 2015. http://arXivpreprintarXiv:2010.16061.
Qi, C. R., H. Su, K. Mo, and L. J. Guibas. 2017a. “Pointnet: Deep learning on point sets for 3D classification and segmentation.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 652–660. New York: IEEE.
Qi, C. R., L. Yi, H. Su, and L. J. Guibas. 2017b. “Pointnet++: Deep hierarchical feature learning on point sets in a metric space.” Preprint, submitted December 4, 2007. http://arXivpreprintarXiv:1706.02413.
Rusu, R. 2009. Semantic 3D object maps for everyday manipulation in human living environments. Muenchen, Germany: Technische Universitaet Muenchen.
Rusu, R. B., and S. Cousins. 2011. “3D is here: Point cloud library (PCL).” In Proc., IEEE Int. Conf. on Robotics and Automation, 1–4. New York: IEEE.
Rusu, R. B., Z. C. Marton, N. Blodow, M. E. Dolha, and M. Beetz. 2008. “Functional object mapping of kitchen environments.” In Proc., IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 3525–3532. New York: IEEE.
Singh, D., N. Kumari, and P. Sharma. 2018. “A review of adverse effects of road traffic noise on human health.” Fluctuation Noise Lett. 17 (1): 1830001. https://doi.org/10.1142/S021947751830001X.
Tarsha-Kurdi, F., T. Landes, and P. Grussenmeyer. 2007. “Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from LIDAR data.” In Vol. 36 of Proc., ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, 407–412. Nice, France: ISPRS.
Town of Carver, M. 2022. “Section 7 transportation.” Accessed April 7, 2022. https://www.carverma.gov/sites/g/files/vyhlif4221/f/uploads/transportation.pdf.
Vo, A.-V., L. Truong-Hong, D. F. Laefer, and M. Bertolotto. 2015. “Octree-based region growing for point cloud segmentation.” ISPRS J. Photogramm. Remote Sens. 104 (Jun): 88–100. https://doi.org/10.1016/j.isprsjprs.2015.01.011.
Xiu, H., P. Vinayaraj, K.-S. Kim, R. Nakamura, and W. Yan. 2018. “3D semantic segmentation for high-resolution aerial survey derived point clouds using deep learning.” In Proc., 26th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, 588–591. New York: Association for Computing Machinery.
Xu, B., W. Jiang, J. Shan, J. Zhang, and L. Li. 2016. “Investigation on the weighted RANSAC approaches for building roof plane segmentation from LIDAR point clouds.” Remote Sens. 8 (1): 5. https://doi.org/10.3390/rs8010005.
Xu, D., F. Li, and H. Wei. 2019. “3D point cloud plane segmentation method based on RANSAC and support vector machine.” In Proc., 14th IEEE Conf. on Industrial Electronics and Applications (ICIEA), 943–948. New York: IEEE.
Xu, S., R. Wang, H. Wang, and R. Yang. 2020. “Plane segmentation based on the optimal-vector-field in LIDAR point clouds.” In IEEE transactions on pattern analysis and machine intelligence. New York: IEEE.
Yamauchi, H., S. Lee, Y. Lee, Y. Ohtake, A. Belyaev, and H.-P. Seidel. 2005. “Feature sensitive mesh segmentation with mean shift.” In Proc., Int. Conf. on Shape Modeling and Applications 2005 (SMI 05), 236–243. New York: IEEE.
Zhang, X., G. Li, Y. Xiong, and F. He. 2008. “3d mesh segmentation using mean-shifted curvature.” In Proc., Int. Conf. on Geometric Modeling and Processing, 465–474. New York: Springer.
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Received: Jan 5, 2022
Accepted: May 13, 2022
Published online: Jul 28, 2022
Published in print: Oct 1, 2022
Discussion open until: Dec 28, 2022
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