Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road surface roughness and is a critical input to asset management. In Indiana, the IRI statistic contributes to roughly half of the pavement quality index computation used for asset management. Most agencies inventory IRI once a year, however, pavement conditions vary much more frequently. The objective of this paper is to develop a framework using crowdsourced connected vehicle data to identify and detect temporal changes in IRI. Over 3 billion connected vehicle records in Indiana were analyzed across 30 months between 2022 and 2024 to understand the spatiotemporal variations in roughness. Annual comparisons across all major interstates in Indiana showed the miles of interstates classified as “Good” decreased from 1896 to 1661 miles between 2022 and 2024. The miles of interstate classified as “Needs Maintenance” increased from 82 to 120 miles. A detailed case study showing monthly and daily changes of estimated IRI on I-65 are presented along with supporting dashcam images. Although the crowdsourced IRI estimates are not as robust as traditional specialized pavement profilers, they can be obtained on a monthly, weekly, or even daily basis. The paper concludes by suggesting a combination of frequent crowdsourced IRI and commercially available dashcam imagery of roadway can provide an agile and responsive mechanism for agencies to implement pavement asset management programs that can complement existing annual programs.
Peraka, N.S.P. and Biligiri, K.P. (2020) Pavement Asset Management Systems and Technologies: A Review. Automation in Construction , 119, Article ID: 103336. https://doi.org/10.1016/j.autcon.2020.103336
Department of Transportation (2024) Budget of the United States Government, Fiscal Year 2023. https://www.govinfo.gov/content/pkg/BUDGET-2023-BUD/pdf/BUDGET-2023-BUD-17.pdf
Federal Highway Administration (2024) Chart VMT-421C—Highway Statistics 2022. https://www.fhwa.dot.gov/policyinformation/statistics/2022/vmt421c.cfm
Múčka, P. (2016) International Roughness Index Specifications around the World. Road Materials and Pavement Design , 18, 929-965. https://doi.org/10.1080/14680629.2016.1197144
Aleadelat, W. and Ksaibati, K. (2017) Estimation of Pavement Serviceability Index Through Android-Based Smartphone Application for Local Roads. Transportation Research Record , 2639, 129-135. https://doi.org/10.3141/2639-16
Du, Y., Liu, C., Wu, D. and Li, S. (2016) Application of Vehicle Mounted Accelerometers to Measure Pavement Roughness. International Journal of Distributed Sensor Networks , 12, Article ID: 8413146. https://doi.org/10.1155/2016/8413146
Federal Highway Administration (2024) Pavements—Inertial Profiler—Pavement (IP). https://infotechnology.fhwa.dot.gov/inertial-profiler-road-pavement/
Loprencipe, G., de Almeida Filho, F.G.V., de Oliveira, R.H. and Bruno, S. (2021) Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks. Sensors , 21, Article 3127. https://doi.org/10.3390/s21093127
Bashar, M.Z. and Torres-Machi, C. (2022) Deep Learning for Estimating Pavement Roughness Using Synthetic Aperture Radar Data. Automation in Construction , 142, Article ID: 104504. https://doi.org/10.1016/j.autcon.2022.104504
Abohamer, H., Elseifi, M., Dhakal, N., Zhang, Z. and Fillastre, C.N. (2021) Development of a Deep Convolutional Neural Network for the Prediction of Pavement Roughness from 3D Images. Journal of Transportation Engineering , Part B : Pavements , 147, Article ID: 04021048. https://doi.org/10.1061/jpeodx.0000310
Cereceda, D., Medel-Vera, C., Ortiz, M. and Tramon, J. (2022) Roughness and Condition Prediction Models for Airfield Pavements Using Digital Image Processing. Automation in Construction , 139, Article ID: 104325. https://doi.org/10.1016/j.autcon.2022.104325
Alhasan, A., White, D.J. and De Brabanter, K. (2015) Spatial Pavement Roughness from Stationary Laser Scanning. International Journal of Pavement Engineering , 18, 83-96. https://doi.org/10.1080/10298436.2015.1065403
Gao, Q., Fan, L., Wei, S., Li, Y., Du, Y. and Liu, C. (2023) Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data. Applied Sciences , 13, Article 8080. https://doi.org/10.3390/app13148080
De Blasiis, M.R., Di Benedetto, A., Fiani, M. and Garozzo, M. (2020) Assessing of the Road Pavement Roughness by Means of Lidar Technology. Coatings , 11, Article 17. https://doi.org/10.3390/coatings11010017
Che, E. and Simpson, C. (2023) Assessing the Feasibility of Utilizing a UAS-Based Point Cloud in Pavement Smoothness/Roughness Measurement. Pacific Northwest Transportation Consortium. http://hdl.handle.net/1773/49798
Nappo, N., Mavrouli, O., Nex, F., van Westen, C., Gambillara, R. and Michetti, A.M. (2021) Use of UAV-Based Photogrammetry Products for Semi-Automatic Detection and Classification of Asphalt Road Damage in Landslide-Affected Areas. Engineering Geology , 294, Article ID: 106363. https://doi.org/10.1016/j.enggeo.2021.106363
Prosser-Contreras, M., Atencio, E., Muñoz La Rivera, F. and Herrera, R.F. (2020) Use of Unmanned Aerial Vehicles (UAVs) and Photogrammetry to Obtain the International Roughness Index (IRI) on Roads. Applied Sciences , 10, Article 8788. https://doi.org/10.3390/app10248788
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T. and Omata, H. (2018) Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images. Computer - Aided Civil and Infrastructure Engineering , 33, 1127-1141. https://doi.org/10.1111/mice.12387
Obunguta, F., Matsushima, K. and Susaki, J. (2024) Probabilistic Management of Pavement Defects with Image Processing Techniques. Journal of Civil Engineering and Management , 30, 114-132. https://doi.org/10.3846/jcem.2024.20401
Aleadelat, W., Ksaibati, K., Wright, C.H.G. and Saha, P. (2018) Evaluation of Pavement Roughness Using an Android-Based Smartphone. Journal of Transportation Engineering , Part B : Pavements , 144, Article ID: 04018033. https://doi.org/10.1061/jpeodx.0000058
Aleadelat, W. and Ksaibati, K. (2017) Estimation of Pavement Serviceability Index through Android-Based Smartphone Application for Local Roads. Transportation Research Record : Journal of the Transportation Research Board , 2639, 129-135. https://doi.org/10.3141/2639-16
Li, X. and Goldberg, D.W. (2018) Toward a Mobile Crowdsensing System for Road Surface Assessment. Computers , Environment and Urban Systems , 69, 51-62. https://doi.org/10.1016/j.compenvurbsys.2017.12.005
Ahmed, H.U., Hu, L., Yang, X., Bridgelall, R. and Huang, Y. (2021) Effects of Smartphone Sensor Variability in Road Roughness Evaluation. International Journal of Pavement Engineering , 23, 4404-4409. https://doi.org/10.1080/10298436.2021.1946059
Jeong, J. and Jo, H. (2024) Toward Real-World Implementation of Deep Learning for Smartphone-Crowdsourced Pavement Condition Assessment. IEEE Internet of Things Journal , 11, 6328-6337. https://doi.org/10.1109/jiot.2023.3312353
Dennis, E.P., Hong, Q., Wallace, R., Tansil, W. and Smith, M. (2014) Pavement Condition Monitoring with Crowdsourced Connected Vehicle Data. Transportation Research Record : Journal of the Transportation Research Board , 2460, 31-38. https://doi.org/10.3141/2460-04
Bridgelall, R., Hough, J. and Tolliver, D. (2017) Characterising Pavement Roughness at Non-Uniform Speeds Using Connected Vehicles. International Journal of Pavement Engineering , 20, 958-964. https://doi.org/10.1080/10298436.2017.1366768
Kargah-Ostadi, N., Vasylevskyi, K., Ablets, A. and Drach, A. (2023) Reconciling Pavement Condition Data from Connected Vehicles with the International Roughness Index from Standard Monitoring Equipment Using Physics-Integrated Machine Learning. Transportation Research Record : Journal of the Transportation Research Board , 2678, 416-429. https://doi.org/10.1177/03611981231174406
Samie, M., Golroo, A., Tavakoli, D. and Fahmani, M. (2023) Potential Applications of Connected Vehicles in Pavement Condition Evaluation: A Brief Review. Road Materials and Pavement Design , 25, 889-913. https://doi.org/10.1080/14680629.2023.2237597
Mahlberg, J.A., Li, H., Zachrisson, B., Mathew, J.K. and Bullock, D.M. (2024) Applications of Using Connected Vehicle Data for Pavement Quality Analysis. Frontiers in Future Transportation , 4, Article 1239744. https://doi.org/10.3389/ffutr.2023.1239744
Zhang, Z., Sun, C., Bridgelall, R. and Sun, M. (2018) Application of a Machine Learning Method to Evaluate Road Roughness from Connected Vehicles. Journal of Transportation Engineering , Part B : Pavements , 144, Article ID: 04018043. https://doi.org/10.1061/jpeodx.0000074
Chen, C., Song, Y., Wang, Y.D., Hu, X. and Liu, J. (2023) A Machine Learning-Based Approach to Assess Impacts of Autonomous Vehicles on Pavement Roughness. Philosophical Transactions of the Royal Society A : Mathematical , Physical and Engineering Sciences , 381, Article ID: 20220176. https://doi.org/10.1098/rsta.2022.0176
Magnusson, P. and Svantesson, T. (2024) Road Condition Monitoring. US10953887B2. https://patents.google.com/patent/US10953887B2/en
Mathew, J.K., Li, H., Landvater, H. and Bullock, D.M. (2022) Using Connected Vehicle Trajectory Data to Evaluate the Impact of Automated Work Zone Speed Enforcement. Sensors , 22, Article 2885. https://doi.org/10.3390/s22082885
Kim, M.Y. and Prozzi, J.A. (2021) Segmentation of Highway Networks for Maintenance Operations. Center for Highway Pavement Preservation, CHPP Report-UTA#8-2021. https://www.chpp.egr.msu.edu/wp-content/uploads/2021/10/31.-CHPP-Report-UTA8-2021.pdf
Federal Highway Administration (2024) Office of Highway Policy Information. https://www.fhwa.dot.gov/policyinformation/pubs/hf/pl11028/chapter7.cfm