A Framework for Remote Road Furniture Monitoring System Using Smart IoT Dashcams and Digital Twin
Publication: Computing in Civil Engineering 2023
ABSTRACT
Monitoring the condition of road furniture, such as traffic lights, signposts, and guardrails, is critical for road safety and driving comfort. However, due to limited resources and the vast size of road infrastructure, comprehensive monitoring can be challenging. To address this issue, this study proposes a framework that automatically identifies damaged road furniture and displays their locations on the user interface, allowing inspectors to remotely monitor road furniture conditions. The framework leverages a road furniture digital twin, containing real-time information about road infrastructure, landmarks, and GPS coordinates. The framework was tested on the NDSU campus road, successfully detecting traffic poles with real-time information displayed on the user interface, reducing the need for manual inspections, and enabling proactive maintenance to improve road safety and driving comfort. The proposed framework is expected to facilitate the real-time remote monitoring of road furniture, minimizing the time, cost, and effort required for inspections.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Azfar, T., J. Weidner, A. Raheem, R. Ke, and R. C. Long. (2022). “Efficient Procedure of Building University Campus Models for Digital Twin Simulation.” IEEE Journal of Radio Frequency Identification, 6: 2022.
Bhatti, G., H. Mohan, and R. Raja Singh. (2021). “Towards the Future of Smart Electric Vehicles: Digital twin technology.” Renewable and Sustainable Energy Reviews, 141: 110801. Pergamon.
Cao, M. T., Q. V. Tran, N. M. Nguyen, and K. T. Chang. (2020). “Survey on Performance of Deep Learning Models for Detecting Road Damages Using Multiple Dashcam Image Resources.” Advanced Engineering Informatics, 46. Elsevier Ltd. https://doi.org/10.1016/J.AEI.2020.101182.
Chen, K., M. Eskandari Torbaghan, M. Chu, L. Zhang, and A. Garcia-Hernández. (2021). “Identifying the Most Suitable Machine Learning Approach for a Road Digital Twin.” Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, 174 (3): 88–101. Thomas Telford Ltd.
Di Benedetto, A., M. Fiani, and M. Marsella. (2019). “Remote Sensing Technologies for Linear Infrastructure Monitoring.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 461–468.
Ferré-Bigorra, J., M. Casals, and M. Gangolells. (2022). “The Adoption of Urban Digital Twins.” Cities, 131: 103905. Pergamon. https://doi.org/10.1016/J.CITIES.2022.103905.
Haresh, S., S. Kumar, M. Z. Zia, and Q. H. Tran. (2020). “Towards Anomaly Detection in Dashcam Videos.” IEEE Intelligent Vehicles Symposium, Proceedings, 1407–1414. Institute of Electrical and Electronics Engineers Inc.
Jo, Y., and S. Ryu. (2015). “Pothole Detection System Using a Black-box Camera.” Sensors 2015, Vol. 15, Pages 29316-29331, 15 (11): 29316–29331. Multidisciplinary Digital Publishing Institute.
Kim, Y., K. Song, and K. Kang. (2022). “Framework for Machine Learning-Based Pavement Marking Inspection and Geohash-Based Monitoring.” International Conference on Transportation and Development 2022: Application of Emerging Technologies - Selected Papers from the Proceedings of the International Conference on Transportation and Development 2022, 1: 123–132. American Society of Civil Engineers.
Lehtomäki, M., A. Jaakkola, J. Hyyppä, A. Kukko, and H. Kaartinen. (2010). “Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data.” Remote Sensing 2010, Vol. 2, Pages 641-664, 2 (3): 641–664. Molecular Diversity Preservation International.
Li, F., S. O. Elberink, and G. Vosselman. (2018). “Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations.” Remote Sensing 2018, Vol. 10, Page 531, 10 (4): 531. Multidisciplinary Digital Publishing Institute.
El Marai, O., T. Taleb, and J. Song. (2020). Roads infrastructure digital twin: A step toward smarter cities realization. IEEE Network, 35(2), 136–143.
Sofia, H., E. Anas, and O. Faïz. (2020, May). “Mobile Mapping, Machine Learning and Digital Twin for Road Infrastructure Monitoring and Maintenance: Case study of mohammed VI bridge in Morocco.” In 2020 IEEE International conference of Moroccan Geomatics (Morgeo) (pp. 1–6). IEEE.
Vieira, J., J. P. Martins, N. M. de Almeida, H. Patrício, and J. G. Morgado. (2022). “Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins.” Sustainability 2022, Vol. 14, Page 7060, 14 (12): 7060. Multidisciplinary Digital Publishing Institute.
Wehner, C., F. Powlesland, B. Altakrouri, and U. Schmid. (2022). Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13343 LNAI: 621–632. Springer Science and Business Media Deutschland GmbH.
Yu, G., S. Zhang, M. Hu, and Y. Ken Wang. (2020). “Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking.” Advances in Civil Engineering, 2020, 1–21.
Zekany, S. A., R. G. Dreslinski, and T. F. Wenisch. (2019). “Classifying Ego-Vehicle Road Maneuvers from Dashcam Video; Classifying Ego-Vehicle Road Maneuvers from Dashcam Video.” In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 1204–1210). IEEE.
Information & Authors
Information
Published In
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Architectural engineering
- Building systems
- Construction engineering
- Construction management
- Highway and road conditions
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Inspection
- Intelligent transportation systems
- Light (artificial)
- Traffic engineering
- Traffic management
- Traffic safety
- Traffic signals
- Transportation engineering
- Transportation management
Authors
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.