A performance-based Pavement Management System for the road network of Montreal city—a conceptual framework

Amin, Md; Amador-Jiménez, Luis · 2014 · Crossref

DOI: 10.1201/b17219-36

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Summary

This paper presents a conceptual framework for a dynamic, performance-based Pavement Management System (PMS) designed for the road network of Montreal City. The research is motivated by the advanced deterioration of Montreal’s arterial roads, most of which were constructed in the 1950s, and the city’s substantial capital investment of over $1.6 billion in its 2012–2014 Three-year Capital Work Program. The authors argue that current rehabilitation practices, such as mill and asphalt overlay, are inadequate for addressing fatigue and cracking, and that existing PMS tools fail to account for broader objectives like mobility, safety, accessibility, and social costs. Consequently, there is a risk that significant infrastructure investments may be wasted without a robust management system that optimizes trade-offs across these multiple criteria. The proposed framework integrates life-cycle cost analysis (LCCA) with dynamic programming and multi-criteria decision-making. It utilizes pavement performance prediction models, specifically Markov chain probabilistic models, to estimate future pavement conditions based on transition probability matrices. To account for dynamic traffic loads, the framework incorporates an integrated land use and transportation (ILUT) modeling approach using four-step transportation modeling (trip production, distribution, modal split, and assignment) to predict Equivalent Single Axle Loads (ESALs). The optimization process employs a backward recursion method to minimize both agency costs (maintenance and construction) and user costs (vehicle operating, travel time, and accident costs) under budget constraints. The authors critique traditional Markov Decision Process (MDP) approaches for their inability to handle budget constraints and reliance on steady-state probabilities, as well as project-based systems for their exponential complexity. To address these limitations, the framework categorizes roads into groups with distinct performance curves rather than optimizing individual sections in isolation. The study identifies that effective PMS must move beyond simple cost-benefit analysis to include socio-economic development parameters, environmental factors such as air quality indices, and ride comfort metrics. By incorporating discrete choice models and multi-criteria analysis techniques like the Analytical Hierarchy Process (AHP), the framework aims to balance agency expenditures with user benefits and societal impacts. The dynamic nature of the system allows it to manage the continuous aggregate behavior of the transportation system, solving optimization problems at any time interval rather than relying on static, long-term steady-state assumptions. The significance of this work lies in its proposal for a more holistic and realistic approach to pavement management. By integrating dynamic traffic predictions, user costs, and multi-criteria objectives, the framework offers a method to justify revenue requests and optimize maintenance schedules more effectively than traditional tools. It addresses the gap in current literature regarding the economic impact of multiple strategies on safety, congestion, and social cost, providing a defensible procedure for preparing budgets and ensuring that infrastructure investments yield optimal long-term performance and societal benefits.

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