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<p>This study aims to formulate effective maintenance strategies for urban roadways by integrating traditional approaches, advanced analytical tools, and contemporary technologies. Among the conventional methods utilized is the Maintenance Prioritization Index (MPI), while the Highway Development and Management Model (HDM-4) represents the modern analytical tool applied. HDM-4 uses comprehensive road network data to assist in route prioritization, particularly through economic assessments such as the Net Present Value to Cost (NPV/Cost) ratio for different pavement segments. Additionally, artificial intelligence (AI)-based techniques were employed using real-time pavement distress data to develop a heat map-based prioritization system for urban roads. The HDM-4 tool was also used to evaluate route conditions both in the present and projected future scenarios. Each method’s performance and applicability for prioritization were independently assessed. The research was conducted on selected urban corridors in Hyderabad, including Ayodhya Crossroad (DA), Ayodhya Crossroad to Dundigal (AD), Miyapur to VNR (MV), VNR to Miyapur (VM), VNR to Gandimaissama (VG), and Gandimaissama to VNR (GV). Furthermore, the relationship between the International Roughness Index (IRI) and the HDM-4 tool’s outputs was analyzed. Findings suggest that AI-based methods offer superior strategic insights for urban road maintenance compared to the MPI approach, while HDM-4 remains a valuable tool for prioritizing routes based on both current and anticipated pavement conditions.</p>

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Assessment of pavement prioritization method for effective maintenance of urban roads

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • D. Harinder,
  • D. Tejasri,
  • Yugendar Poojari,
  • Venkatesh Noolu

摘要

This study aims to formulate effective maintenance strategies for urban roadways by integrating traditional approaches, advanced analytical tools, and contemporary technologies. Among the conventional methods utilized is the Maintenance Prioritization Index (MPI), while the Highway Development and Management Model (HDM-4) represents the modern analytical tool applied. HDM-4 uses comprehensive road network data to assist in route prioritization, particularly through economic assessments such as the Net Present Value to Cost (NPV/Cost) ratio for different pavement segments. Additionally, artificial intelligence (AI)-based techniques were employed using real-time pavement distress data to develop a heat map-based prioritization system for urban roads. The HDM-4 tool was also used to evaluate route conditions both in the present and projected future scenarios. Each method’s performance and applicability for prioritization were independently assessed. The research was conducted on selected urban corridors in Hyderabad, including Ayodhya Crossroad (DA), Ayodhya Crossroad to Dundigal (AD), Miyapur to VNR (MV), VNR to Miyapur (VM), VNR to Gandimaissama (VG), and Gandimaissama to VNR (GV). Furthermore, the relationship between the International Roughness Index (IRI) and the HDM-4 tool’s outputs was analyzed. Findings suggest that AI-based methods offer superior strategic insights for urban road maintenance compared to the MPI approach, while HDM-4 remains a valuable tool for prioritizing routes based on both current and anticipated pavement conditions.