Technical Papers
Apr 13, 2024

An Automated Pavement Marking Retroreflectivity Condition Assessment Method Using Mobile LiDAR and Video Log Images

Publication: Journal of Infrastructure Systems
Volume 30, Issue 2

Abstract

Pavement markings are a key transportation asset and traffic control device that facilitate safe and predictable driving. The effectiveness of pavement markings is dependent on their condition, particularly during nighttime and adverse weather. The Federal Highway Administration (FHWA) has developed regulations to guide minimum pavement marking retroreflectivity levels, which poses a potential challenge to public agencies because the current practice of visual inspection is labor intensive and the results can be subjective. To address the identified challenges and needs of public agencies, the objective of this research is twofold: (1) to serve as a proof of concept for the use of mobile light detection and ranging (LiDAR) to locate and assess the pavement markings for selected testing sections by developing and evaluating new automated LiDAR processing algorithms, and (2) to investigate the feasibility of identifying the deterioration trend of the retroreflectivity condition. This study developed a complete pavement marking inventory with retroreflectivity conditions for the 14 selected testing sections and also compared historical and current data to inform the deterioration trends of three types of marking materials, including polyurea, epoxy, and thermoplastic. The findings of this study will guide future phases with a larger selection of testing sections, material types, and roadway characteristics. The outcomes of the series of studies will help better define the benefit-to-cost ratio for different marking materials and eventually lead to the development of public agencies’ pavement marking standards.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some data, models, or code supporting this study’s findings, e.g., normalized retroreflectivity values and the correlation between handheld retroreflectometer measurements and LiDAR intensity values, are available from the corresponding author upon reasonable request.

Acknowledgments

This research was undertaken as part of the Massachusetts DOT Research Program with funding from FHWA State Planning and Research (SPR) funds. The authors are solely responsible for the accuracy of the facts and data, the validity of the study, and the views presented herein.

References

Ai, C. 2013. A sensing methodology for an intelligent traffic sign inventory and condition assessment using GPS/GIS, computer vision and mobile LiDAR technologies. Atlanta: Georgia Institute of Technology.
Ai, C., and E. Hennessy. 2022. A pavement marking inventory and retroreflectivity condition assessment method using mobile LiDAR. Rep. No. 22-030. Boston: Massachusetts DOT.
Ai, C., and Y. J. Tsai. 2016. “An automated sign retroreflectivity condition evaluation methodology using mobile LIDAR and computer vision.” Transp. Res. Part C Emerging Technol. 63 (Feb): 96–113. https://doi.org/10.1016/j.trc.2015.12.002.
ASTM. 2018. Standard test method for measurement of retroreflective pavement marking materials with cen-prescribed geometry using a portable retroreflectometer. ASTM E1710-18. West Conshohocken, PA: ASTM.
Benz, R., A. Pike, S. Kuchangi, and Q. Brackett. 2009. Serviceable pavement marking retroreflectivity levels. Rep. No. 0-5656-1. College Station, TX: Texas DOT.
Carlson, P., J. Miles, A. Pike, and E. Park. 2007. Evaluation of wet-weather and contrast pavement marking applications. Rep. No. 0-5008-2. College Station, TX: Texas DOT.
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.
Craig, W. N., III, W. E. Sitzabee, W. J. Rasdorf, and J. E. Hummer. 2007. “Statistical validation of the effect of lateral line location on pavement marking retroreflectivity degradation.” Public Works Manage. Policy 12 (2): 431–450. https://doi.org/10.1177/1087724X07308773.
FHWA (Federal Highway Administration). 2009. “Manual on uniform traffic control devices for streets and highways.” Accessed January 16, 2023. https://mutcd.fhwa.dot.gov/index.htm.
FHWA (Federal Highway Administration). 2022a. “National standards for traffic control devices; the Manual on Uniform Traffic Control Devices for Streets and Highways; Maintaining pavement marking retroreflectivity.” Accessed January 16, 2023. https://www.transportation.gov/bipartisan-infrastructure-law/regulations/2022-16781.
FHWA (Federal Highway Administration). 2022b. “Nighttime visibility general information.” Accessed January 16, 2023. https://highways.dot.gov/safety/other/visibility/nighttime-visibility-general-information.
Guan, H., J. Li, Y. Yu, C. Wang, M. Chapman, and B. Yang. 2014. “Using mobile laser scanning data for automated extraction of road markings.” ISPRS J. Photogramm. Remote Sens. 87 (Jan): 93–107. https://doi.org/10.1016/j.isprsjprs.2013.11.005.
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., and C. Ai. 2022. “An automated sound barrier inventory method using mobile LiDAR.” J. Transp. Eng., Part A: Syst. 148 (10): 04022078. https://doi.org/10.1061/JTEPBS.0000732.
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.
Kamermann, G. 1993. Laser radar-active electrooptical systems, the infrared & electro-optical systems handbook. Washington, DC: SPIE Optical Engineering Press.
Kashani, A. G., M. J. Olsen, C. E. Parrish, and N. Wilson. 2015. “A review of LIDAR radiometric processing: From ad hoc intensity correction to rigorous radiometric calibration.” Sensors 15 (11): 28099–28128. https://doi.org/10.3390/s151128099.
Kirk, A., E. Hunt, and E. Brooks. 2001. Factors affecting sign retroreflectivity. Rep. No. OR-RD-01-09. Salem, OR: Oregon DOT.
Lundkvist, S.-O., and U. Isacsson. 2007. “Prediction of road marking performance.” J. Transp. Eng. 133 (6): 341–346. https://doi.org/10.1061/(ASCE)0733-947X(2007)133:6(341).
Mahlberg, J. A., Y.-T. Cheng, D. M. Bullock, and A. Habib. 2021. “Leveraging lidar intensity to evaluate roadway pavement markings.” Future Transp. 1 (3): 720–736. https://doi.org/10.3390/futuretransp1030039.
Migletz, J., J. L. Graham, K. M. Bauer, and D. W. Harwood. 1999. “Field surveys of pavement-marking retroreflectivity.” Transp. Res. Rec. 1657 (1): 71–78. https://doi.org/10.3141/1657-10.
Olsen, M., C. Parrish, E. Che, J. Jung, and J. Greenwood. 2018. Lidar for maintenance of pavement reflective markings and retroreflective signs. Rep. No. OR-RD-19-01. Salem, OR: Oregon DOT.
Otsu, N. 1979. “A threshold selection method from gray-level histograms.” IEEE Trans. Syst. Man Cybern. 9 (1): 62–66. https://doi.org/10.1109/TSMC.1979.4310076.
Pfeifer, N., P. Dorninger, A. Haring, and H. Fan. 2007. Investigating terrestrial laser scanning intensity data: Quality and functional relations. Cambridge, MA: TU Wien Academic Press.
Pike, A., and T. Barrette. 2020. Pavement markings-wet retroreflectivity standards. Rep. No. MN 2020-09. St. Paul, MN: Minnesota DOT.
Pike, A. M., H. G. Hawkins Jr., and P. J. Carlson. 2007. “Evaluating the retroreflectivity of pavement marking materials under continuous wetting conditions.” Transp. Res. Rec. 2015 (1): 81–90. https://doi.org/10.3141/2015-10.
Reynolds, T., and H. Hawkins Jr. 2010. The impact of snowplowing and traffic on marking retroreflectivity in new hampshire. Washington, DC: Transportation Research Board.
Schnell, T., F. Aktan, and Y.-C. Lee. 2003. “Nighttime visibility and retroreflectance of pavement markings in dry, wet, and rainy conditions.” Transp. Res. Rec. 1824 (1): 144–155. https://doi.org/10.3141/1824-16.
Smadi, O., R. R. Souleyrette, D. J. Ormand, and N. Hawkins. 2008. “Pavement marking retroreflectivity: Analysis of safety effectiveness.” Transp. Res. Rec. 2056 (1): 17–24. https://doi.org/10.3141/2056-03.
Tsai, Y. J., and Z. Wang. 2019. Validating change of sign and pavement conditions and evaluating sign retroreflectivity condition assessment on Georgia’s interstate highways using 3D sensing technology. Rep. No. FHWA-GA-20-1732. Atlanta: Georgia DOT.
Varghese, C., and U. Shankar. 2007. Passenger vehicle occupant fatalities by day and night–a contrast. Washington, DC: National Highway Traffic Safety Administration.
Yang, B., L. Fang, Q. Li, and J. Li. 2012. “Automated extraction of road markings from mobile lidar point clouds.” Photogramm. Eng. Remote Sens. 78 (4): 331–338. https://doi.org/10.14358/PERS.78.4.331.

Information & Authors

Information

Published In

Go to Journal of Infrastructure Systems
Journal of Infrastructure Systems
Volume 30Issue 2June 2024

History

Received: Jul 13, 2023
Accepted: Feb 3, 2024
Published online: Apr 13, 2024
Published in print: Jun 1, 2024
Discussion open until: Sep 13, 2024

Permissions

Request permissions for this article.

Authors

Affiliations

Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Massachusetts, Amherst, MA 01003. ORCID: https://orcid.org/0000-0002-1599-6782
Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Massachusetts, Amherst, MA 01003 (corresponding author). ORCID: https://orcid.org/0000-0002-3536-9348. Email: [email protected]
Neil Boudreau
Assistant Administrator for Traffic and Safety, Highway Division, Massachusetts Dept. of Transportation, Boston, MA 02116.

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share