A Standardized Grade Point Average (sGPA) Framework for Urban Pavement Systems: A Scalable Index for Infrastructure Economics and GIS-Based City Evaluation

Introduction

Urban infrastructure systems are foundational to productivity, accessibility, and long-run economic growth. However, existing pavement condition measures suffer from three key limitations:

• Non-aggregability (segment-level scores do not scale well)

• Low interpretability for policy makers

• Weak integration into economic utility functions

To address these issues, we introduce the standardized Grade Point Average (sGPA), a transformation of PCI into a bounded, intuitive 4.3-scale system.

The objective is to unify:

• Engineering condition measurement

• Spatial aggregation

• Economic valuation into a single framework.

Literature Review

Pavement Condition Measurement

The dominant framework is the ASTM D6433 Pavement Condition

Index, which evaluates distress type, severity, and density on a 0–100 scale.

Alternative systems include:

• IRI (International Roughness Index)

• PASER visual ratings

• municipal asset management scoring systems

However, none provide:

• Cross-city comparability

• Bounded scalar interpretation

• Direct economic embedding

Urban Infrastructure Economics

Urban models typically link infrastructure to:

• Transport cost reduction

• Agglomeration effects

• Property value gradients

But infrastructure inputs are rarely normalized into a single interpretable scalar variable, limiting empirical comparability.

Methodology

PCI-to-sGPA Transformation

We define:

                                     sGPA_i=f(PCI_i)

where fis a monotonic piecewise mapping:

                                     sGPA_i∈[0,4.3]

Calibration:

• 97–100 → 4.3

• 93–96 → 4.0

• 90–92 → 3.7

• 87–89 → 3.3

• 83–86 → 3.0

• 80–82 → 2.7

• 77–79 → 2.3

• 73–76 → 2.0

• 70–72 → 1.7

• 65–69 → 1.3

• 60–64 → 1.0

• 55–59 → 0.7

• <55 → 0.0

This preserves ordinal structure while enabling cardinal interpretation.

City-Level Aggregation

We define city infrastructure quality as:

Where:

• Li: lane-kilometres of segment i

This yields a length-weighted expected infrastructure grade.

Functional Weighting Extension

To incorporate network hierarchy:

Where:

• Arterials: W=1.5

• Collectors: W=1.2

• Local roads: W=1.0

Econometric Framework

We embed sGPA into a standard urban outcome model:

• Where Ymay represent:

• Property values

• Congestion delay

• Vehicle operating costs

• Productivity per km²

Expected sign:

                             β₁>0

Nonlinear Urban Welfare Function

We propose diminishing returns in infrastructure quality:

NB=aln(sGPA)-b(sGPA)2

Implications:

• Increasing returns at low infrastructure levels

• saturation beyond optimal sGPA*

• convex investment trade-offs

• Optimal condition:

GIS Implementation

Each road segment contains:

Variable	Meaning
PCI	distress score
sGPA	transformed grade
L	segment length
W	functional weight
Weighted score	sGPA⋅L⋅W

This enables:

• Continuous heat maps of infrastructure quality

• Spatial inequality decomposition

• Corridor prioritization models

Empirical Design (Multi-City Framework)

A comparative design is proposed:

City	Type
Toronto	high-density monocentric
Los Angeles	polycentric auto-dependent
Saint John	mid-sized port-industrial

Each yields:

sGPA__city," " sGPA__core," " sGPA__suburbs

allowing decomposition of infrastructure inequality.

Conceptual Case: Saint John

In Saint John, infrastructure exhibits:

• Historic core concentration

• Industrial corridor loading

• Suburban expansion gradients

Thus:

becomes a spatial inequality metric of urban capital stock.

Results (Simulated Calibration Table)

City	Estimated sGPA	Interpretation
Toronto	3.1–3.4	stable
Los Angeles	2.6–3.0	mixed condition
Saint John	2.1–2.5	maintenance deficit

Conclusion

This paper introduces a unified infrastructure metric (sGPA) that converts pavement condition data into a standardized 4.3-point system. The framework bridges engineering measurement, GIS spatial analysis, and urban economic modeling, enabling consistent cross-city evaluation of infrastructure quality and investment needs [1-21].

Discussion

The sGPA framework contributes:

• Measurement innovation: Transforms engineering PCI into bounded scalar index

• Economic integration: Direct inclusion in regression and welfare models

• Policy usability: “City GPA” is intuitive for governments and public reporting

• Spatial economics linkage: Compatible with GIS-based inequality and clustering models

Limitations

• Requires calibration across jurisdictions

• Sensitive to weighting scheme selection

• Does not capture underground utilities or structural base failure unless included in PCI inputs

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