A review on the impacts of connected vehicles on pavement management systems

References

• AASHTO, 2012. Pavement management guide (2nd ed). Washington, DC: American Association of Highway and Transportation Officials.
   Google Scholar
• Abdelkader, G., Elgazzar, K., and Khamis, A, 2021. Connected vehicles: technology review, state of the art, challenges and opportunities. Sensors, 21 (22), 7712–7742.
   PubMed Web of Science ®Google Scholar
• Al-Kaisy, A., and Ewan, L, 2017. Prioritization scheme for proposed road weather information system sites: Montana case study. Frontiers in Built Environment, 3, 45.
   Google Scholar
• Alavi, A. H., et al., 2016. Continuous health monitoring of pavement systems using smart sensing technology. Construction and Building Materials, 114, 719–736.
   Web of Science ®Google Scholar
• Anderson, A.R., et al., 2012. Quality of mobile air temperature and atmospheric pressure observations from the 2010 development test environment experiment. Journal of Applied Meteorology and Climatology, 51 (4), 691–701.
   Web of Science ®Google Scholar
• Andrášik, R., and Bíl, M, 2016. Efficient road geometry identification from digital vector data. Journal of Geographical Systems, 18, 249–264.
   Web of Science ®Google Scholar
• Andrews, S., Cops, M., and Vehicle Infrastructure Integration Consortium, 2009. Vehicle infrastructure integration proof of concept: results and findings summary-vehicle (No. FHWA-JPO-09-043). Washington, D.C.: Dept. of Transportation. Research and Innovative Technology Administration.
   Google Scholar
• Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT), 2022. Version 9.1 [online]. Available from: https://www.arc-it.net/html/servicepackages/sp151.html#tab-3 [Accessed 20 June 2022].
   Google Scholar
• Arezoumand, S., et al., 2021. Automatic pavement rutting measurement by fusing a high speed-shot camera and a linear laser. Construction and Building Materials, 283, 122668.
   Web of Science ®Google Scholar
• ASCE, 2021. A comprehensive assessment of America’s infrastructure. Virginia: ASCE.
   Google Scholar
• ASTM, 2009. Standard specification for highway weigh-in-motion (WIM) systems with user requirements and test methods (ASTM Standard E1318-09). West Conshohocken, PA: ASTM International.
   Google Scholar
• Bark, S., 2020. How we made the pavements width map [online]. Buckinghamshire: Esri UK & Ireland. Available from: https://resource.esriuk.com/blog/how-we-made-the-pavements-width-map/ [Accessed 25 May 2023].
   Google Scholar
• Biswas, S., Mišić, J., and Mišić, V, 2012. An identity-based authentication scheme for safety messages in WAVE-enabled VANETs. International Journal of Parallel, Emergent and Distributed Systems, 27 (6), 541–562.
   Google Scholar
• Bridgelall, R, 2014. Connected vehicle approach for pavement roughness evaluation. Journal of Infrastructure Systems, 20 (1), 04013001.
   Web of Science ®Google Scholar
• Bridgelall, R, 2015. Precision bounds of pavement distress localization with connected vehicle sensors. Journal of Infrastructure Systems, 21 (3), 04014045.
   Web of Science ®Google Scholar
• Bridgelall, R., et al., 2016a. Error sensitivity of the connected vehicle approach to pavement performance evaluations. International Journal of Pavement Engineering, 19 (1), 82–87.
   Web of Science ®Google Scholar
• Bridgelall, R., et al., 2016b. Use of connected vehicles to characterize ride quality. Transportation Research Record, 2589, 119–126.
   Google Scholar
• Bridgelall, R., et al., 2016c. Precision enhancement of pavement roughness localization with connected vehicles. Measurement Science and Technology, 27 (2), 025012.
   Web of Science ®Google Scholar
• Bridgelall, R., et al., 2017b. Wavelength sensitivity of roughness measurements using connected vehicles. International Journal of Pavement Engineering, 20 (5), 566–572.
   Web of Science ®Google Scholar
• Bridgelall, R., et al., 2019. Enhancement of signals from connected vehicles to detect roadway and railway anomalies. Measurement Science and Technology, 31 (3), 035105.
   Web of Science ®Google Scholar
• Bridgelall, R., et al., 2022. Detecting sources of ride roughness by ensemble-connected vehicle signals. International Journal of Pavement Engineering, 23, 1–11.
   Google Scholar
• Bridgelall, R., Hough, J., and Tolliver, D, 2017a. Characterising pavement roughness at non-uniform speeds using connected vehicles. International Journal of Pavement Engineering, 20 (8), 958–964.
   Web of Science ®Google Scholar
• Bridgelall, R., and Tolliver, D, 2016. Accuracy enhancement of roadway anomaly localization using connected vehicles. International Journal of Pavement Engineering, 19 (1), 75–81.
   Web of Science ®Google Scholar
• Bridgelall, R., and Tolliver, D, 2020. Accuracy enhancement of anomaly localization with participatory sensing vehicles. Sensors, 20 (2), 409–423.
   PubMed Web of Science ®Google Scholar
• Bulut, R., et al., 2013. Evaluation of the enhanced integrated climatic model for modulus-based construction specification for Oklahoma pavements (No. OTCREOS11.1-10-F). Oklahoma: Oklahoma Transportation Center.
   Google Scholar
• Bustamante-Bello, R., et al., 2022. Visualizing street pavement anomalies through fog computing v2i networks and machine learning. Sensors, 22 (2), 456–475.
   PubMed Web of Science ®Google Scholar
• Cantisani, G., and Del Serrone, G, 2020. Procedure for the identification of existing roads alignment from georeferenced points database. Infrastructures, 6 (1), 2–17.
   Google Scholar
• Chapman, M., et al., 2010. Diagnosing road weather conditions with vehicle probe data: results from Detroit IntelliDrive field study. Transportation Research Record, 2169 (1), 116–127.
   Google Scholar
• Chen, S., et al., 2016. LTE-V: A TD-LTE-based V2X solution for future vehicular network. IEEE Internet of Things Journal, 3 (6), 997–1005.
   Web of Science ®Google Scholar
• Chen, J., et al., 2021. Crowd-Sensing road surface quality using connected vehicle data. Transportation Research Record, 2675 (11), 729–739.
   Web of Science ®Google Scholar
• Chen, Y. and Krumm, J., 2010. Probabilistic modeling of traffic lanes from GPS traces. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, 2–5 November, San Jose, CA: ACM, 81–88.
   Google Scholar
• Chen, C., Seo, H., and Zhao, Y, 2022. A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis. International Journal of Pavement Engineering, 23 (12), 4372–4384.
   Web of Science ®Google Scholar
• Crawford, B., 2012. Pioneering sensors collect weather data from moving vehicles [online]. Kent: ITS international. Available from: https://www.itsinternational.com/its9/feature/pioneering-sensors-collect-weather-data-moving-vehicles [Accessed 25 May 2023].
   Google Scholar
• Delgadillo, R., Wahr, C., and Alarcon, J.P, 2011. Toward implementation of the mechanistic–empirical pavement design guide in Latin America. Transportation Research Record: Journal of the Transportation Research Board, 2226, 142–148.
   Web of Science ®Google Scholar
• Dennis, E.P., et al., 2014. Pavement condition monitoring with crowdsourced connected vehicle data. Transportation Research Record, 2460 (1), 31–38.
   Google Scholar
• Di Mascio, P., et al., 2012. Procedure to determine the geometry of road alignment using GPS data. Procedia-Social and Behavioral Sciences, 53, 1202–1215.
   Google Scholar
• Donnges, C., Edmonds, G., and Johannessen, B, 2007. Rural road maintenance: sustaining the benefits of improved access. Bangkok: International Labour Office.
   Google Scholar
• Doycheva, K., Koch, C., and König, M., 2019. Computer vision and deep learning for real-time pavement distress detection. In: Advances in informatics and computing in civil and construction engineering: proceedings of the 35th CIB W78 conference: IT in design, construction, and management, 1–3 October, Illinois: Springer International Publishing, 601–607.
   Google Scholar
• Drobot, S., et al., 2011. The vehicle data translator V3.0 system description (No. FHWA-JPO-11-127). Washington, D.C.: Joint Program Office for Intelligent Transportation Systems.
   Google Scholar
• El-Wakeel, A.S., et al., 2018. Towards a practical crowdsensing system for road surface conditions monitoring. IEEE Internet of Things Journal, 5 (6), 4672–4685.
   Web of Science ®Google Scholar
• Elhashash, M., Albanwan, H., and Qin, R, 2022. A review of mobile mapping systems: from sensors to applications. Sensors, 22 (11), 4262–4287.
   PubMed Web of Science ®Google Scholar
• Elsagheer Mohamed, S.A., and AlShalfan, K.A., 2021. Intelligent traffic management system based on the internet of vehicles (IoV). Journal of Advanced Transportation, 2021, 1–23.
   Web of Science ®Google Scholar
• EUROSTAT, 2022. 77% of inland freight transported by road in 2020 [online]. Luxembourg: Eurostat. Available from: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20220425-2 [Accessed 7 May 2023].
   Google Scholar
• Ewan, L., Al-Kaisy, A., and Hossain, F, 2016. Safety effects of road geometry and roadside features on low-volume roads in Oregon. Transportation Research Record, 2580 (1), 47–55.
   Google Scholar
• FHWA, 2018. Weigh-in-motion pocket guide – part 1: WIM technology, data acquisition, and procurement guide (No. FHWA-PL-18-015). Washington, D.C.: Department of Transportation, Office of Highway Policy Information.
   Google Scholar
• Flintsch, G.W., and Smith, B.L, 2015. Infrastructure pavement assessment & management applications enabled by the connected vehicles environment–proof-of-concept. Virginia: Research and Innovative Technology Administration.
   Google Scholar
• Ganji, M.R., et al., 2021. Asphalt pavement macrotexture monitoring in cracked surfaces by using an acoustical low-cost continuous method. Automation in Construction, 132, 103932.
   Web of Science ®Google Scholar
• Gao, S., etal, 2021. Automatic urban road network extraction from massive GPS trajectories of taxis. In: M. Werner and Y. Chiang, eds. Handbook of Big geospatial data. Cham: Springer International Publishing, 261–283.
   Google Scholar
• Garilli, E., et al., 2021. Automatic detection of stone pavement's pattern based on UAV photogrammetry. Automation in Construction, 122, 103477.
   Web of Science ®Google Scholar
• Gopalakrishnan, K, 2018. Deep learning in data-driven pavement image analysis and automated distress detection: A review. Data, 3 (3), 28–46.
   Web of Science ®Google Scholar
• Gruyer, D., et al., 2021. Are connected and automated vehicles the silver bullet for future transportation challenges? Benefits and weaknesses on safety, consumption, and traffic congestion. Frontiers in Sustainable Cities, 2, 607054.
   Google Scholar
• Guerrero-Ibáñez, J., Zeadally, S., and Contreras-Castillo, J, 2018. Sensor technologies for intelligent transportation systems. Sensors, 18 (4), 1212–1235.
   PubMed Web of Science ®Google Scholar
• Guo, K., et al., 2022. A pavement distresses identification method optimized for YOLOv5s. Scientific Reports, 12 (1), 3542.
   PubMed Web of Science ®Google Scholar
• Guo, Q., Li, L., and Ban, X.J, 2019. Urban traffic signal control with connected and automated vehicles: a survey. Transportation Research Part C: Emerging Technologies, 101, 313–334.
   Web of Science ®Google Scholar
• Heppner, P., et al., 2011. Passenger bus industry weather information application (No. FHWA-JPO-11-123). Washington, D.C.: Joint Program Office for Intelligent Transportation Systems.
   Google Scholar
• Hoang, N.D, 2019. Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach. Advanced Engineering Informatics, 40, 110–120.
   Web of Science ®Google Scholar
• Hoang, N.D., Nguyen, Q.L., and Tran, V.D, 2018. Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Automation in Construction, 94, 203–213.
   Web of Science ®Google Scholar
• Hou, Y., et al., 2021. The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis. Engineering, 7 (6), 845–856.
   Web of Science ®Google Scholar
• Hu, J., Huang, M.C., and Yu, X.B, 2023. Deep learning based on connected vehicles for icing pavement detection. AI in Civil Engineering, 2 (1), 1–14.
   Google Scholar
• Inzerillo, L., Di Mino, G., and Roberts, R., 2018. Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Automation in Construction, 96, 457–469.
   Web of Science ®Google Scholar
• Irschik, D. and Stork, W., 2014. Road surface classification for extended floating car data. In: 2014 IEEE international conference on vehicular electronics and safety. 16–17 December, Hyderabad: IEEE, 78–83.
   Google Scholar
• Jacob, B., O'Brien, E.J., and Newton, W, 2000. Assessment of the accuracy and classification of weigh-in-motion systems. Part 2: European specification. International Journal of Heavy Vehicle Systems, 7 (2-3), 153–168.
   Web of Science ®Google Scholar
• Jadaan, K., Zeater, S., and Abukhalil, Y, 2017. Connected vehicles: an innovative transport technology. Procedia Engineering, 187, 641–648.
   Google Scholar
• Jana, S., et al., 2022. Transfer learning based deep convolutional neural network model for pavement crack detection from images. International Journal of Nonlinear Analysis and Applications, 13 (1), 1209–1223.
   Web of Science ®Google Scholar
• Jang, J., et al., 2017. Framework of data acquisition and integration for the detection of pavement distress via multiple vehicles. Journal of Computing in Civil Engineering, 31 (2), 04016052.
   Web of Science ®Google Scholar
• Jima, D., and Sipos, T, 2022. The impact of road geometric formation on traffic crash and its severity level. Sustainability, 14 (14), 8475–8499.
   Web of Science ®Google Scholar
• Karmańska, A, 2021. The benefits of connected vehicles within organizations. Procedia Computer Science, 192, 4721–4731.
   Google Scholar
• Khalifeh, V., Golroo, A., and Ovaici, K, 2018. Application of an inexpensive sensor in calculating the international roughness index. Journal of Computing in Civil Engineering, 32 (4), 04018022.
   Web of Science ®Google Scholar
• Kheirati, A., and Golroo, A, 2020. Low-cost infrared-based pavement roughness data acquisition for low volume roads. Automation in Construction, 119, 103363.
   Web of Science ®Google Scholar
• Kheirati, A., and Golroo, A, 2022. Machine learning for developing a pavement condition index. Automation in Construction, 139, 104296.
   Web of Science ®Google Scholar
• Kheradmandi, N., and Mehranfar, V, 2022. A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Construction and Building Materials, 321, 126162.
   Web of Science ®Google Scholar
• Kyriakou, C., Christodoulou, S.E., and Dimitriou, L, 2021. Do vehicles sense, detect and locate speed bumps? Transportation Research Procedia, 52, 203–210.
   Google Scholar
• Li, Q., et al., 2011. Mechanistic-empirical pavement design guide (MEPDG): a bird’s-eye view. Journal of Modern Transportation, 19, 114–133.
   Google Scholar
• Li, D., et al., 2021. Pixel-level recognition of pavement distresses based on U-Net. Advances in Materials Science and Engineering, 2021, 1–11.
   Web of Science ®Google Scholar
• Linton, M.A., and Fu, L, 2016. Connected vehicle solution for winter road surface condition monitoring. Transportation Research Record, 2551 (1), 62–72.
   Google Scholar
• Lopes, P.C., et al., 2021. Study of environmental data from vehicle’s sensors and its applicability to complement climate mapping from automatic meteorological stations and assess COVID-19 impact. AI Perspectives, 3 (1), 1–7.
   Google Scholar
• Lu, N., et al., 2014. Connected vehicles: solutions and challenges. IEEE Internet of Things Journal, 1 (4), 289–299.
   Web of Science ®Google Scholar
• Luo, W., Li, L., and Wang, K.C, 2016. Automated pavement horizontal curve measurement methods based on inertial measurement unit and 3D profiling data. Journal of Traffic and Transportation Engineering (English Edition), 3 (2), 137–145.
   Google Scholar
• Mahlberg, J.A., et al., 2021. Prioritizing roadway pavement marking maintenance using lane keep assist sensor data. Sensors, 21 (18), 6014–6029.
   PubMed Web of Science ®Google Scholar
• Mahlberg, J.A., et al., 2022a. Pavement quality evaluation using connected vehicle data. Sensors, 22 (23), 9109–9126.
   PubMed Web of Science ®Google Scholar
• Mahlberg, J.A., et al., 2022b. Measuring roadway lane widths using connected vehicle sensor data. Sensors, 22 (19), 7187–7201.
   PubMed Web of Science ®Google Scholar
• Mahmoudzadeh, A., et al., 2019. Estimating pavement roughness by fusing color and depth data obtained from an inexpensive RGB-D sensor. Sensors, 19 (7), 1655.
   PubMed Web of Science ®Google Scholar
• Mahoney, B., et al., 2010. Vehicles as mobile weather observation systems. Bulletin of the American Meteorological Society, 91 (9), 1179–1182.
   Web of Science ®Google Scholar
• Majidifard, H., Adu-Gyamfi, Y., and Buttlar, W.G, 2020. Deep machine learning approach to develop a new asphalt pavement condition index. Construction and Building Materials, 247, 118513.
   Web of Science ®Google Scholar
• Mei, Q., and Gül, M, 2020. A cost effective solution for pavement crack inspection using cameras and deep neural networks. Construction and Building Materials, 256, 119397.
   Web of Science ®Google Scholar
• Najafi, S., Flintsch, G.W., and Khaleghian, S, 2016. Fuzzy logic inference-based pavement friction management and real-time slippery warning systems: A proof of concept study. Accident Analysis & Prevention, 90, 41–49.
   PubMed Web of Science ®Google Scholar
• Nautiyal, A., and Sharma, S, 2021. Condition based maintenance planning of low volume rural roads using GIS. Journal of Cleaner Production, 312, 127649.
   Web of Science ®Google Scholar
• NCHRP, 2004. Guide for mechanistic-empirical design of new and rehabilitated pavement structures. Champaign: National Research Council.
   Google Scholar
• Pan, Y., et al., 2018. Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (10), 3701–3712.
   Web of Science ®Google Scholar
• Panahandeh, G., Ek, E., and Mohammadiha, N., 2017. Road friction estimation for connected vehicles using supervised machine learning. In: 2017 IEEE intelligent vehicles symposium (IV). 11–14 June, California: IEEE: 1262–1267.
   Google Scholar
• Peraka, N.S.P., and Biligiri, K.P, 2020. Pavement asset management systems and technologies: a review. Automation in Construction, 119, 103336.
   Web of Science ®Google Scholar
• Peraka, N.S.P., Biligiri, K.P., and Kalidindi, S.N, 2021. Development of a multi-distress detection system for asphalt pavements: transfer learning-based approach. Transportation Research Record, 2675 (10), 538–553.
   Web of Science ®Google Scholar
• Qin, J., et al., 2022. Incremental road network update method with trajectory data and UAV remote sensing imagery. ISPRS International Journal of Geo-Information, 11 (10), 502–522.
   Web of Science ®Google Scholar
• Radopoulou, S.C., and Brilakis, I, 2015. Patch detection for pavement assessment. Automation in Construction, 53, 95–104.
   Web of Science ®Google Scholar
• Ranyal, E., Sadhu, A., and Jain, K, 2022. Road condition monitoring using smart sensing and artificial intelligence: a review. Sensors, 22 (8), 3044–3070.
   PubMed Web of Science ®Google Scholar
• Riid, A., et al., 2019. Pavement distress detection with deep learning using the orthoframes acquired by a mobile mapping system. Applied Sciences, 9 (22), 4829–4850.
   Google Scholar
• Roberts, R., Inzerillo, L., and Di Mino, G., 2020. Using UAV based 3D modelling to provide smart monitoring of road pavement conditions. Information, 11 (12), 568–591.
   Google Scholar
• Rolt, J., et al., 2020. Rural road note 01: A guide on the application of pavement design methods for low volume rural roads – first edition. London: ReCAP for UK Aid.
   Google Scholar
• Ruan, S., Long, C., Bao, J., Li, C., Yu, Z., Li, R., Liang, Y., He, T. and Zheng, Y., 2020. Learning to generate maps from trajectories. In: Proceedings of the AAAI conference on artificial intelligence. 7–12 February 2020. New York: AAAI, 890–897.
   Google Scholar
• Sauerwein, P.M., and Smith, B.L, 2011. Investigation of the implementation of a probe-vehicle based pavement roughness estimation system (No. UVA-2010-01). Virginia: Transportation Research Council.
   Google Scholar
• Shi, X, 2020. More than smart pavements: connected infrastructure paves the way for enhanced winter safety and mobility on highways. Journal of Infrastructure Preservation and Resilience, 1 (1), 1–12.
   Google Scholar
• Siems-Anderson, A.R., et al., 2019. An adaptive big data weather system for surface transportation. Transportation Research Interdisciplinary Perspectives, 3, 100071.
   Google Scholar
• Siems-Anderson, A., et al., 2020. Impacts of assimilating observations from connected vehicles into a numerical weather prediction model. Transportation Research Interdisciplinary Perspectives, 8, 100253.
   Google Scholar
• Sujon, M., and Dai, F, 2021. Application of weigh-in-motion technologies for pavement and bridge response monitoring: state-of-the-art review. Automation in Construction, 130, 103844.
   Web of Science ®Google Scholar
• Tang, L., et al., 2015. Lane-level road information mining from vehicle GPS trajectories based on naïve Bayesian classification. ISPRS International Journal of Geo-Information, 4 (4), 2660–2680.
   Web of Science ®Google Scholar
• Wang, Y., et al., 2023. Framework for geometric information extraction and digital modeling from LiDAR data of road scenarios. Remote Sensing, 15 (3), 576–596.
   Web of Science ®Google Scholar
• Wang, H., Jiang, L., and Xiang, P, 2018b. Improving the durability of the optical fiber sensor based on strain transfer analysis. Optical Fiber Technology, 42, 97–104.
   Web of Science ®Google Scholar
• Wang, H., Xiang, P., and Jiang, L, 2018a. Optical fiber sensor based in-field structural performance monitoring of multilayered asphalt pavement. Journal of Lightwave Technology, 36 (17), 3624–3632.
   Web of Science ®Google Scholar
• Wang, H., Xiang, P., and Jiang, L, 2019. Strain transfer theory of industrialized optical fiber-based sensors in civil engineering: a review on measurement accuracy, design and calibration. Sensors and Actuators A: Physical, 285, 414–426.
   Web of Science ®Google Scholar
• Wang, H.P., Xiang, P., and Jiang, L.Z, 2020. Optical fiber sensing technology for full-scale condition monitoring of pavement layers. Road Materials and Pavement Design, 21 (5), 1258–1273.
   Web of Science ®Google Scholar
• Wright, J., et al., 2014. National connected vehicle field infrastructure footprint analysis (No. FHWA-JPO-14-125). Washington, D.C.: Department of Transportation, Intelligent Transportation Systems Joint Program Office.
   Google Scholar
• Xiang, P., and Wang, H, 2018. Optical fibre-based sensors for distributed strain monitoring of asphalt pavements. International Journal of Pavement Engineering, 19 (9), 842–850.
   Web of Science ®Google Scholar
• Ye, Z., et al., 2022. A distributed pavement monitoring system based on internet of things. Journal of Traffic and Transportation Engineering (English Edition), 9 (2), 305–317.
   Google Scholar
• Yin, Y., et al., 2020. The influence of road geometry on vehicle rollover and skidding. International Journal of Environmental Research and Public Health, 17 (5), 1648–1664.
   PubMed Web of Science ®Google Scholar
• Yu, Q., Fang, Y., and Wix, R, 2022. Pavement roughness index estimation and anomaly detection using smartphones. Automation in Construction, 141, 104409.
   Web of Science ®Google Scholar
• Zeng, X., Balke, K., and Songchitruksa, P, 2012. Potential connected vehicle applications to enhance mobility, safety, and environmental security (No. SWUTC/12/161103-1). Texas: Texas Transportation Institute, The Texas A&M University System.
   Google Scholar
• Zeng, H., Park, H., and Smith, B.L, 2019. Impact of vehicle dynamic systems on a connected vehicle-enabled pavement roughness estimation. Journal of Infrastructure Systems, 25 (1), 04018046.
   Web of Science ®Google Scholar
• Zhang, Z., et al., 2018a. Application of a machine learning method to evaluate road roughness from connected vehicles. Journal of Transportation Engineering, Part B: Pavements, 144 (4), 04018043.
   Web of Science ®Google Scholar
• Zhang, Z., et al., 2018b. Road profile reconstruction using connected vehicle responses and wavelet analysis. Journal of Terramechanics, 80, 21–30.
   Web of Science ®Google Scholar
• Zhang, H., et al., 2021. Formulating a GIS-based geometric design quality assessment model for mountain highways. Accident Analysis & Prevention, 157, 106172.
   PubMed Web of Science ®Google Scholar
• Zhang, C., et al., 2022. Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada. International Journal of Transportation Science and Technology, 11 (2), 298–309.
   Google Scholar
• Zhu, J., et al., 2022. Pavement distress detection using convolutional neural networks with images captured via UAV. Automation in Construction, 133, 103991.
   Web of Science ®Google Scholar