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Designing and Building an Intelligent Pavement Management System for Urban Road Networks

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  • Maryam Moradi

    (Faculty of Construction Engineering, École de Technologie Supérieure, 1100 Notre-Dame St. W, Montreal, QC H3C 1K3, Canada)

  • Gabriel J. Assaf

    (Faculty of Construction Engineering, École de Technologie Supérieure, 1100 Notre-Dame St. W, Montreal, QC H3C 1K3, Canada)

Abstract

Pavement maintenance plays a significant role in megacities. Managing complaints and scheduling road reviews are the two maintenance concerns under the intelligent pavement management system (PMS) plan. In contrast, if the damages are not treated immediately, they will increase over time. By leveraging accurate data from sensors, smart PMS will improve management capability, support sustainability, and drive economic growth in the road network. This research aimed to elaborate on the different modules of an intelligent city pavement network to advance to a sustainable city. First, a 3D mobile light detection and ranging (LiDAR) sensor, accompanied by a camera, was applied as the data collection tool. Although 3D mobile LiDAR data have gained popularity, they lack precise detection of pavement distresses, including cracks. As a result, utilizing RGB imaging may help to detect distresses properly. Two approaches were integrated alongside conducting the data analysis in this paper: (1) ArcGIS pro, developed by Esri Inc., which includes noise removal, digital elevation model (DEM) generation, and pavement and building footprint extraction; (2) the Mechanistic-Empirical Pavement Design Guide (AASHTOWare PMED), which was used to assess site specifications such as traffic, weather, subbase, and current pavement conditions in an effort to design the most appropriate pavement for each road section. For the 3D visualization module, CityEngine (a software from Esri) was used to provide the 3D city model. After implementing the research methodology, we drew the following conclusions: (1) using the AASHTOWare PMED method to make decisions about road maintenance and rehabilitation(M&R) actions can significantly speed up the decision-making process, essentially saving time and money and shortening the project’s duration; and (2) if the road conditions are similar, the smart geographical information system (GIS)-based PMS can make consistent decisions about road M&R strategies, i.e., the interference from human factors is less significant.

Suggested Citation

  • Maryam Moradi & Gabriel J. Assaf, 2023. "Designing and Building an Intelligent Pavement Management System for Urban Road Networks," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1157-:d:1028551
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    References listed on IDEAS

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    1. Yanting Zhang & Zhe Zhu & Wei Ning & Amir M. Fathollahi-Fard, 2022. "An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
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    Cited by:

    1. Yulin Chang & Yijie Wang & Chao Sun & Peng Zhang & Wenqian Xu, 2023. "Day-to-Day Dynamic Traffic Flow Assignment Model under Mixed Travel Modes Considering Customized Buses," Sustainability, MDPI, vol. 15(6), pages 1-22, March.

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