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MN WI MI OH IL IN USDOT Region V Regional University Transportation Center Final Report NEXTRANS Project No 083PY04 Using Regional Freight Traffic Assignment Modeling to Quantify the Variability of Pavement Damage for Highway Cost Allocation and Revenue Analysis By Jackeline Murillo-Hoyos Anwaar Ahmed Graduate Students School of Civil Engineering Purdue University anwaar@purdue.edu and Samuel Labi, Principal Investigator Associate Professor School of Civil Engineering Purdue University labi@purdue.edu Report Submission Date: December 31, 2014 DISCLAIMER Funding for this research was provided by the NEXTRANS Center, Purdue University under Grant No. DTRT07-G-005 of the U. Department of Transportation, Research and Innovative Technology Administration (RITA), University Transportation Centers Program. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein.

This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program, in the interest of information exchange. Government assumes no liability for the contents or use thereof. MN WI MI OH IL IN USDOT Region V Regional University Transportation Center Final Report TECHNICAL SUMMARY NEXTRANS Project No 083PY04 Final Report, December 31 Using Regional Freight Traffic Assignment Modeling to Quantify the Variability of Pavement Damage for Highway Cost Allocation and Revenue Analysis Introduction While indicative of a vibrant economy, large volumes of freight traffic have been associated with accelerated wear of pavements particularly. In seeking to adopt operational policies that reduce undue deterioration of their infrastructure, state highway agencies in the United States strive to quantify the damage caused by vehicle loads so that it is possible to update loading polices and to implement fee structures that are commensurate with the pavement damage.

An INDOT-commissioned research study, SPR 3502, provided a methodology to estimate the pavement damage costs. That study reported these costs on the basis of systemwide average levels of traffic loading. In reality, however, traffic loading and climatic severity at specific road segments can differ significantly from what their systemwide averages suggest. This Nextrans study therefore investigated the issue of pavement damage cost estimation from a purely disaggregate level in order to establish potentially more reliable estimates of pavement damage costs.

It is envisaged that doing so would not only increase the efficiency and effectiveness but also would enhance equity in the highway cost allocation and revenue generation. To address the issue at a disaggregate level, the study first established more reliable projections of highway freight traffic volumes at each individual pavement segment on the highway network using the results from a freight assignment and volume prediction tool. Next, for each road segment the expected axle loadings on the basis of the projected traffic volumes, were calculated and the expected pavement damage costs were determined from the expected level of truck volume (and thus, estimated loading). Further, the study quantified the deviation, for each pavement segment, of the damage cost using disaggregate and aggregate approaches.

Findings To address the issue at a disaggregate level, the study first established more reliable projections of highway freight traffic volumes at each individual pavement segment on the highway network using the results from a freight assignment and volume prediction tool. Next, for each road segment, the expected axle loadings on the basis of the projected traffic volumes, were NEXTRANS Project No 020PY01Technical Summary - Page 1 calculated. Then the expected pavement damage costs were determined from the expected loadings. Further, the study quantified the deviation, for each pavement segment, of the damage cost using disaggregate and aggregate approaches.

Recommendations The research product can be used to estimate the cost of pavement damage for individual pavements section on a state highway network. This can be done using the expected axle loadings on the basis of the projected traffic volumes. The deviation of pavement damage costs at each pavement segment relative to the aggregate damage cost reported all pavements, can be quantified. Thus, the dangers of using aggregate estimates for pavement damage cost, can be demonstrated.

Contacts For more information: Samuel Labi NEXTRANS Center Principal Investigator Purdue University - Discovery Park Civil Engineering 2700 Kent B-100 Purdue University West Lafayette, IN 47906 labi@purdue.edu nextrans@purdue.edu (765) 496-9729 (765) 807-3123 Fax www.edu/dp/nextrans i ACKNOWLEDGMENTS The authors hereby acknowledge the funding support provided by NEXTRANS, the USDOT Region V Regional University Transportation Center, located in West Lafayette, Indiana. The authors are particularly grateful to Professor Srinivas Peeta, the Director of NEXTRANS Center, Dr. Rick Evans, the Managing Director, and Ms. Nija Phelps, the secretary, for their immense support and assistance, in many ways, throughout the conduction of this research study.

We are also grateful to graduate students Matthew Volovski, Nathee Athigakunagorn, and Tariq Usman Saeed for their assistance in processing parts of the data. ii CONTENTS Page CHAPTER 1 INTRODUCTION ……………………………….1 Problem Statement and Study Objective ……………….2 Organization of this Report …………………………………. 4 CHAPTER 2 LITERATURE REVIEW ………….2 Past Studies Motivated By Pavement Cost Allocation (PCA) … 5 2.3 Studies Motivated By Pavement Damage Cost Estimation …… 7 2.4 Summary and Discussion ………………………………………. 13 CHAPTER 3 ESTIMATION OF TRAFFIC LOADING………………………….3 Traffic Growth Factor ………………………………………….3 Traffic Loading Estimation …………………………………….

23 CHAPTER 4 LIFE-CYCLE ACTIVITY SCHEDULES FOR PAVEMENT RECONSTRUCTION, REHABILITATION AND MAINTENANCE ………………………………………………….2 Formulating MR&R Activity Schedules ……………………… 25 4.3 The Effect of Discounting over Pavement Life Cycle ………. 30 CHAPTER 5 COSTS AND SERVICE LIVES OF MR&R TREATMENTS.2 Pavement Families for this Study ……………………………… 31 5.3 The Cost of MR&R Activity Schedules ………………………. 31 iii CHAPTER 6 ESTIMATING THE COSTS OF PAVEMENT DAMAGE …….1 Introduction and Overview …………………………………….2 Data Collection and Collation ………………………………….4 Model Results and Discussion ………………………………….5 Estimation of Marginal Pavement M&R Cost ………………….6 Application of the Model ………………………………………. 45 CHAPTER 7 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS….

62 iv LIST OF TABLES Table Page Table 2.1: Past HCA Studies – Methods & Cost Allocators .2: A Synthesis of PDC Estimation Studies based on Indirect Approach … 15 Table 2.3: A Synthesis of PDC Estimation Studies based on Empirical Approach 16 Table 2.4: Studies Using Miscellaneous Methods for PDC Estimation ………….1: AADT and Truck AADT – Summary Statistics …………………….2: Average Truck Class Percentages on the Highway Functional Classes.4: ESAL Factors for Different Highway Functional Classes ….1: Standard Treatments at a Typical Highway Agency ……………….2: Pavement Performance Standards at a Typical Highway Agency …….1: Service Lives of Typical Standard Treatments …………………….2 Flexible Pavement Treatment Costs ……….3 Rigid Pavement Treatment Costs ……….1 Summary Statistics of Key Traffic and Climatic Variables .2 Estimation Results of Random- And Fixed-Parameter Linear Models .1 Basic Data on the Pavement Segments……….2 Pavement Segments: Estimated Traffic Percentages by Vehicle Class .3 Pavement Segments: Estimated Traffic Volume by Vehicle Class .4 Pavement Segments: Estimated Annuals ESALs by Vehicle Class.… 47 v LIST OF FIGURES Figure Page Figure 1.1: Overall Study Framework.1: Contexts of Pavement Damage Cost Estimation ……………………… 5 Figure 6.1: Random Parameter Distribution – Total Traffic (ESALs) 42 Figure 6.2: Random Parameter Distribution – Total Precipitation 43 Figure 6.3: Random Parameter Distribution – Microsurfacing Treatment 44 Figure 6.4: Scatter Plot of Cost per lane-mile of Pavement Damage by Road Class 51 Figure 6.5: Scatter Plot of Pavement Damage Cost Estimates by Road Class …… 52 Figure 6.6: Probability Distribution of Pavement Damage Cost Estimates …….1 Introduction In past practice and research, the charging of road users for their “consumption” of the highway infrastructure has mostly been analyzed on the basis of data on aggregate measures of consumption. However, this is expected to become increasingly based on disaggregate data. It is interesting to observe the gradual evolution of the level of individual responsibility for their highway use: several decades ago, users generally and indirectly paid for highway use irrespective of their weight. This was followed by an era where charges were established for users on the basis of the collective responsibility of the users in each group (also referred to as vehicle classes).

For example, all trucks of a certain size or weight paid a certain fee. Across the user groups, fees were gradated on the basis of size or weight, but within in group, each user paid the same amount. In the current era, the group-based charging policy seems to be waning, as there seems to be greater demand from stakeholders for each individual vehicle even within each group, to pay according to the amount of damage it inflicts individually on the facility. The underlying cause of these shifts is not certain but is often surmised to have roots in the changing voter attitudes in the country.

Notwithstanding these evolutions of user-based charging, the fact remains that highway agencies worldwide that have stewardship of billions of dollars’ worth of taxpayer-owned and infrastructure continue to seek policies that prevent accelerated deterioration of their pavements through excess loading and other factors. As such, highway agencies pursue knowledge of the infrastructure damage caused by heavy vehicles so that the true costs of overweight vehicle operations in terms of pavement and bridge damage repair as well as the costs of enforcing permitting regulations can be ascertained and the existing license or overweight fees can be updated. Over the past 3 decades, several states have carried out studies related to the estimation of pavement damage cost or as part of highway cost allocation, in a bid to restructure the existing user charges. 2 These studies can be categorized as those that provided and implemented a framework to: (i) assess the increase in pavement or bridge costs for every ton increase in payload or the decrease in pavement costs for every increase in the number of axles, for any given truck class; (ii) provide a framework to identify the operational degradation costs (safety and mobility impairment) related to the use of trucks; (iii) provide a framework to identify the wider systemwide benefits associated with truck operations (specifically, the traffic volume reduction because fewer trips are required due to carry the same amount of goods) and the concomitant overall benefits in terms of lower exposure to crashes, reduced emissions, reduced congestion, reduced energy use, and so on; (iv) estimate all revenue sources and respective amounts, associated with the use of trucks; (v) investigate the inequities of each vehicle class (i., different axle configurations and gross vehicle weights) in terms of their revenue generated vs.

the infrastructure damage (physical and/or operational) they inflict; (vi) establish an equitable license or permit fee structure by each heavy vehicle class that would not adversely affect the productivity of the trucking industry; The results of such studies have been used for a variety of highway management functions or to drive highway use policies including fuel tax rate adjustment.2 Problem Statement and Study Objective It has been shown in previous studies that the current road-user charging systems do not recover the full cost caused by heavy vehicles; thus most vehicles are paying less than their fair share of highway repair expenditures (HVCRS, 1984; FHWA, 1997, 2000; RAC, 2002). Also, there is spatial inequity: in other words, for trucks of the same vehicle class, the use an average damage cost value for both a high-trafficked road and a low-trafficked road would underestimate the total damage cost for the former and overestimate the total damage cost for the latter. Ahmed et al (2013) estimated pavement damage costs in order to update the existing fee structure. However, an issue that remains with the past and current studies that are related to cost allocation is that the infrastructure damage is estimated on the basis of systemwide average levels of traffic loading and climatic severity.

In reality, however, traffic loading and climatic severity at specific road segments can differ significantly from what their systemwide averages suggest. 3 Also, annual field counts at specific segments may not be sufficient for the purpose of fee structure determination at specific highway segments because they only reflect current conditions and even with growth adjustment factors, may fail to provide reliable future projections.