Travel demand modeling to simulate traffic loads for pavement deterioration curves: dealing with aggregate data at urban and regional scales

Amin, Md. Shohel Reza; Amador-Jiménez, Luis E. · 2015 · Crossref

DOI: 10.1139/cjce-2015-0295

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Summary

This study addresses the limitations of traditional pavement management systems (PMS), which rely on historical traffic data or simple growth rates to estimate pavement deterioration. The authors argue that traffic volume and distribution are dynamically influenced by land use, economy, and travel behavior, necessitating a more integrated approach. The research aims to simulate traffic loads on regional and urban road networks using travel demand models to generate more accurate pavement deterioration curves over a 50-year period (2013–2062). The methodology employs two distinct integration of land use and transportation (ILUT) models. At the regional scale, the TRANUS model integrates spatial input-output and transportation systems to simulate interprovincial freight movement on Highways 1, 2, 7, 15, 16, 102, and 104 connecting the Atlantic Provinces of Canada. This model estimates trade flows based on production and consumption factors. At the urban scale, the Urban Transportation Planning System (UTPS) simulates traffic on arterial and local roads in Montreal. This involves discrete choice models for trip generation, a doubly-constrained gravity model for trip distribution, and a multinomial logit model for mode choice, followed by deterministic user equilibrium assignment to estimate Annual Average Daily Traffic (AADT). Pavement performance is modeled using a regression equation for International Roughness Index (IRI) progression, where Equivalent Single Axle Loads (ESALs) are calculated from simulated AADT and observed truck distributions. The results indicate significant variations in traffic loads and pavement deterioration across the studied networks. For regional highways, accumulated ESALs from 2013 to 2062 range from 2.91 million on Highway 104 to 127.70 million on Highway 2. The simulated truck traffic accounts for a substantial portion of total traffic, contributing up to 91% on Highway 2. In Montreal, the study projects specific IRI values by 2062: 35.71 for flexible-arterial roads, 43.33 for rigid-arterial roads, 31.62 for flexible-local roads, and 30.67 for rigid-local roads. Comparative analysis reveals that the impact of simulated traffic is most pronounced on Highway 2 and Highway 1 at the regional level, and on rigid-arterial and flexible roads in Montreal. The significance of this work lies in its improvement of traditional pavement performance modeling by incorporating dynamic, simulated traffic loads derived from integrated economic and land-use models. By linking travel demand directly to pavement deterioration, the study provides a more robust framework for prioritizing maintenance treatments, estimating long-range investment requirements, and conducting life-cycle economic evaluations. This approach allows for better anticipation of how shifts in land use and economic policies affect infrastructure longevity.

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