In 2011, almost 71% of the petroleum used in the U.S. was used in the transportation sector, accounting for 27% of U.S. energy demand. Despite a 50% increase in the overall fuel economy of passenger vehicles between the years 1973 and 2000, the average fuel economy of heavy vehicles increased less than 20% to only 6.2 miles per gallon. In this same time period, the annual mileage driven by heavy trucks increased by 64%. Meanwhile, consumers, governments and state agencies are all seeking ways to become more sustainable.
Improving vehicle fuel economy ensures that fewer natural resources are required to produce fuel, vehicles traveling on the roadway infrastructure produce lower volumes of greenhouse gases, and consumers who traverse the highway network reduce their environmental impact while also saving money.
While this might seem like a simple concept, understanding the factors that affect vehicle fuel economy is complex. Rolling resistance, among other factors, leads to energy losses at the PVI interface. It is defined as “the mechanical energy converted into heat by a tire moving for a unit distance of roadway.” An effective way of quantitatively measuring rolling resistance of pavements is by using the International Roughness Index (IRI) to measure roughness. There is evidence from the MIRIAM study that rolling resistance is a direct function of the IRI. Reducing the roughness of pavements as measured by IRI can reduce their rolling resistance and lead to improvements in fuel efficiency.
While decision-makers at the municipal and state levels do consider pavement-roughness performance in prioritizing construction, maintenance and rehabilitation decisions, often they do not have easy, user-friendly access to network and context-specific pavement IRI data. A recent analysis of IRI data using the Federal Highway Administration (FHWA) Long Term Pavement Performance (LTPP) database indicates that there are some significant differences in how the IRI of different pavement types, under different circumstances and as defined by region, traffic loading and base preparation, change over time. Within specific contexts, there usually is a trade-off between the overall IRI of a pavement and how fast the IRI changes over time. Typically, while asphalt pavements tend to have an overall lower IRI than concrete pavements, over time they tend to become rougher than concrete pavements at a faster pace. The IRI Explorer is a web-based user-friendly platform that can empower decision-makers to make context-specific queries about their networks to support in their decision-making.
Comparable and similar
The IRI Explorer (http://iriexplorer.com) is a web-based tool that can be used to compare the IRI performance of comparable pavements under similar conditions. Decision-makers can use the tool to access network-specific IRI performance data to support decision-making regarding IRI performance within specific networks.
The IRI Explorer is designed for exploring and analyzing IRI data in any source of pavement performance database. It allows decision-makers and researchers with an interest in pavement performance or sustainability to query the LTPP data about the effect of pavement selection on IRI, and to see the resulting analysis in an easy-to-read graph while still employing statistical rigor on the back end. Currently it provides an analysis interface to gain insights into the factors that have historically governed and/or influenced the change in pavement IRI over time. While the LTPP data is available through various other web portals, IRI Explorer allows decision-makers and researchers to compare basic statistics for specific subsets of their choice. The subsets can be selected based on region, climate, traffic loading, type of base and pavement type. Regional networks also can be explored to get an understanding of how IRI performance varies within a region of interest. Additionally, the IRI Explorer combines the LTPP data with an emissions estimator tool to find greenhouse-gas (GHG) emissions during the use phase of a road (from IRI), as well as the construction phase.
The Sections tab is for looking at the IRI performance of a single pavement section over time. The Networks tab is best for examining the performance of broad categories of pavement types within a regionalized network. Users looking to compare two pavement types will want to go to the Pavement Comparison page. This tab also has many filters to let the user select pavements by sub type, kESALs, climate region, admin region, state, LTPP study, and functional road class. The Emissions Estimator is for estimating the equivalent CO2 emissions of a roadway over the life of that roadway, including construction events. Users looking for a discussion of some of the interesting trends seen in the LTPP database can read up on the Overview of Trends page. The State Data tab houses pavement data gathered by certain individual states. Access to this data is limited to users who are part of the agencies that funded the data inclusion.
Figure 1 illustrates the Sections tab, showing the IRI readings over time (the black line), and the timing of any construction events (vertical blue lines). Each point on the IRI line is the average of the readings from a profile measurement event. The table below the graph lists layer types and thicknesses for the selected section. A menu to print the graph or save it in a number of image formats is accessed by clicking the three bars in the upper right of the graph.
The Networks tab is illustrated in Figure 2. This tab shows every pavement section within approximately 200 miles of a central point (shown on the map as a red marker). Note that not all sections within the LTPP database have mapping coordinates, and therefore don’t appear in this tab. The graph shows the IRI readings vs. time for every section in the network initially. In the network information table, pavement types can be toggled to be the only ones shown by selecting “toggle others” to the left of the pavement type name as shown below. Alternately, individual pavement types can be toggled on or off by clicking directly on the name. Also, clicking on a section in the network graph or the name of the section in the section lists below the graph will take you from the Networks tab directly to the Sections tab graph and information on the section you selected. As in the Sections tab, the Networks graph may be printed or saved to a number of image formats by clicking the three bars in the upper right of the graph to access the Print/Save menu.
The Comparison tab allows users to select and compare data sub-sets using filters such as pavement sub-type (including variations of asphalt and concrete pavements), kESALs, climate region, admin region, state, LTPP study, and functional road class and creates a graph (Figure 3) and a corresponding table that displays information about the initial IRI, change in IRI, average time to first intervention, and whether the two pavements being compared are statistically different in each of the three variables. Additionally, toggling the Show Advanced Plot box will add a cumulative distribution to the graph of the times to first intervention.
Statistical significance is determined using Welch’s two-tailed T-test for equivalence of the mean of two populations. If the test fails that means there is not sufficient evidence in the selected set to say that either pavement outperformed the other for that metric. Values that pass the test (statistically considered different) are bolded in the accompanying table.
Encouraging words
The most important contribution of the IRI Explorer is to provide a platform that allows easy access and statistically significant comparison of pavement performance pertaining to roughness data. Researchers and decision-makers can use it to analyze and compare roughness data and transparently share the results with stakeholders to support informed decision-making. Most importantly it draws attention to the need for a context-specific analysis of pavement performance when prioritizing the reduction of environmental emissions associated with increased rolling resistance as pavements deteriorate over time.
At the same time, it is important to note that given the wide variability in the available datasets, it is critical to use caution when analyzing the data and/or generalizing the outcomes from studies involving limited datasets. Finally, it also is important to acknowledge that there are rarely any free lunches, e.g., pavement types that tend to have lower average IRIs tend to have shorter times to intervention and pavement types with longer times to intervention tend to have higher IRIs. Hence, in order to build smoother and low-emission pavements, decision-makers are encouraged to use tools like IRI Explorer to consider network-wide context-specific assessments. AT
