Inventory Based Rating System Pilot Data Collection and Implementation Report

Transcription

1 Inventory Based Rating System Pilot Data Collection and Implementation Report December 8, 2015 Authors: Tim Colling, PhD., PE John Kiefer, PE Pete Torola, PE Department of Civil and Environmental Engineering Michigan Technological University 1400 Townsend Drive Houghton, MI 49931

2 Table of Contents Executive Summary Acknowledgement Introduction Limitations of Existing Unpaved Road Assessment Systems Premise of Inventory-Based Rating System Objective and Scope Methods Selection of Data Collection Locations Pre-Field Work Training Data Collection Methodology Inventory Based Rating Data Collection Gravel Thickness Data Collection Combined PASER and IBR Collection User Feedback Results Feedback from Users Data Summaries from Collection Antrim County Baraga County Huron County Kalamazoo County Van Buren County Benchmarking Rating Productivity Combined PASER/IBR Collection Benchmarking System Wide IBR Collection Estimates Repeatability of Measurement Conclusions Recommendation for Modification of the Original System Modifications to the Drainage Adequacy Measurement Rating Guidance Modifications to the Structural Adequacy Measurement Rating Guidance Concerns Over the Intent of Good-Fair-Poor Designations Repeatability/Reliability of the System Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 1

3 6.3 Recommendation for Implementation (Policy for Collection) References Appendix A: User Feedback and Comments Appendix B: Draft TAMC Policy for Collection of Roadway Condition Data on Federal-Aid Eligible Roads & Streets Appendix C: Draft Policy for Collection of Roadway Condition Data on (Paved and Unpaved) Non- Federal Aid Eligible Roads & Streets Appendix C: Training Handout Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 2

4 Table of Figures Figure 1: Qualitative classification of counties based on unpaved road network type Figure 2: Roadsoft data set for Antrim County showing unpaved roads Figure 3: Laptop Data Collector showing Huron County IBR collection data Figure 4: IBR Surface Width data as a percentage of roads rated during the pilot Figure 5: IBR Drainage Adequacy data as a percentage of roads rated during the pilot Figure 6: IBR Structural Adequacy data as a percentage of roads rated during the pilot Figure 7: Combined IBR number as a percentage of roads rated during the pilot Figure 8: Converted 10-point scale IBR numbers collected during the pilot Figure 9: Antrim County pilot data collection Figure 10: Antrim County unpaved road showing poor Surface Width, poor Drainage Adequacy, and poor Structural Adequacy Figure 11: Baraga County pilot data collection Figure 12: Thickly vegetated roadsides in Baraga County Figure 13: Huron County pilot data collection Figure 14: Huron County unpaved road showing good Surface Width and good Drainage Adequacy Figure 15: Kalamazoo County pilot data collection Figure 16: Kalamazoo County unpaved road showing fair Surface Width and poor Drainage Adequacy Figure 17: Van Buren County pilot data collection Figure 18: Van Buren County unpaved road with fair Surface Width and fair Drainage Adequacy.. 26 Figure 19: IBR element point difference (rater minus ground truth) Figure 20: IBR number difference for all pilot segments and PASER results for paved road segments as a basis of comparison for data collection repeatability Figure 21: Validation of the institutional knowledge on gravel thickness Table of Tables Table 1: IBR data collection statistics by county Table 2: IBR data and joint IBR and PASER data collection statistics Table 3: System wide IBR data collection estimates Table 4: U.S. Climatic Region III recommended aggregate base thickness Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 3

5 Executive Summary To comply with State statutes on reporting conditions of the Michigan public road network, the Michigan Transportation Asset Management Council (TAMC) has adopted condition assessment systems for use by local road agencies. The Pavement Surface Evaluation and Rating (PASER) system for asphalt, concrete and sealcoat roads, and the National Bridge Inventory (NBI) rating system for public bridges have both proven to be cost-effective network-level metrics for reporting to the Michigan Legislature, and provides usable data on a local and regional level. The rating systems for unpaved roads, however, lacked in accuracy and effectiveness. Most of the rating systems currently in use for unpaved roads are derived from paved road assessment systems, which focus on surface condition as the primary factor in assessing road quality. Surface quality is a much less reliable rating factor for unpaved roads because it can change rapidly and is only one road inventory feature, which includes road width, drainage, and other features. To meet the need of a condition assessment system for unpaved roads, the Inventory-Based Rating (IBR) system was created and implemented as a pilot project in five Michigan counties, assessing roads based on Surface Width, Drainage Adequacy and Structural Adequacy. These inventory features were selected because of their impact on road users and the significant cost to create and maintain them. The system defines a baseline condition for each inventory feature, which indicates a good rating in this good-fair-poor rating system. The five counties were selected based on classification of their road networks; their selection depended upon overall function, management and maintenance of unpaved roads. The Center for Technology & Training (CTT) staff trained those who participated in the pilot data collection and participants collected data on Surface Width, Drainage Adequacy and Structural Adequacy over one to two days. The CTT performed a second series of data collection events, which included gravel thickness measurements at randomly selected locations. The CTT also collected user feedback and comments on the IBR system were collected from the participants during the study. The data collected showed the repeatability of the IBR system to be very high, and the data collectors were ultimately very successful in providing reliable results. The pilot also provided productivity benchmarking, which can be used to forecast the time commitment for collecting the IBR system data. The user feedback was very positive and helpful, and modifications to improve the Drainage Adequacy and Structural Adequacy rating guidance have been implemented. The CTT recommends an update to the Roadsoft Laptop Data Collector (LDC) for future IBR data collection, and a plan has been recommended to collect IBR data over one to two years followed by three- to four-year re-rating cycles and ratings as needed after projects that change the IBR features. It is recommended is that IBR data be collected and reimbursed on the same cycle as PASER data Training for the IBR data collection should be required which is consistent with PASER data collection. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 4

6 1.0 Acknowledgement The Michigan Transportation Asset Management Council (TAMC) and Michigan Technological University wish to acknowledge the contribution of the transportation professionals that assisted in the data collection necessary for this report and the refinements they suggested in an effort to develop a new unpaved road rating method that meets the needs of Michigan s transportation agencies. Staff assisting in the field collection of data for this report were: Kevin Arndt Huron County Road Commission Megan Arndt Kalamazoo Area Transportation Study Ken Barnett Van Buren County Road Commission Travis Bartholomew Road Commission of Kalamazoo County Bill DeYoung Road Commission of Kalamazoo County Craig Ericksen Van Buren County Road Commission Bob Fitzgerald Antrim County Road Commission Bruce Holdwick Huron County Road Commission Joel Hoort Van Buren County Road Commission Gautam Mani Southwest Michigan Planning Commission Doug Mills, PE Baraga County Road Commission Fred Nagler, PE Kalamazoo Area Transportation Study Lynne Parker East Michigan Council of Governments Mark Piotter, PE Huron County Road Commission Linnea Rader Van Buren County Road Commission Jeffrey Rautiola, PE Michigan Department of Transportation Steve Rouser Western Upper Peninsula Planning & Development Region Scott Swanson Baraga County Road Commission Burt Thompson, PE Antrim County Road Commission 2.0 Introduction The TAMC is charged by State statute with reporting the condition of the Michigan public road network to the Michigan Legislature [1]. To this end, the TAMC has successfully adopted existing condition assessment systems for paved roads and bridges in Michigan. The Pavement Surface Evaluation and Rating (PASER) [2,3,4] system for asphalt, concrete, and sealcoat roads has been used by road owning agencies in the State of Michigan since the 1990 s and suits the needs of the TAMC as a cost-effective network-level metric for reporting to the Legislature. PASER data on paved roads have been collected and reported to the Michigan Legislature by the TAMC since 2002 [1]. Similarly, the TAMC has adopted the use of the National Bridge Inventory (NBI) rating system to report the condition of public bridges in the State. The TAMC, however, has not identified a suitable condition assessment system for unpaved roads that meets their requirements of providing a cost-effective, stable, network-level measure in a beneficial way for local road managers. Condition assessment systems can serve two general purposes: providing project-level guidance that infers a type or class of treatment necessary for a given asset, and providing a network-level metric to evaluate the overall system performance. The best assessment systems serve both purposes. Pavement condition assessment systems that provide project-level guidance can be used Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 5

7 to determine when specific maintenance activity on a specific section of road should be conducted. The PASER system, for example, was developed so that road owners could infer specific classes of treatments that would be appropriate based on a road s current rating [2,3,4]. Condition assessment systems that provide a network-level measure can be used as a metric to assess the overall health of the road network. Network-level measures are important for efficient road management because they provide an easy method for determining the level of investment necessary to maintain or work toward a condition target. For example, the TAMC uses the percentage of paved road miles that have a PASER number of 4 or below as its major network-level metric for the paved road system [1]. 2.1 Limitations of Existing Unpaved Road Assessment Systems Many condition assessment systems exist for unpaved roads with measurement methods ranging from basic to complicated [5]. However, none of these existing systems have been found to provide a reliable, stable, network-level assessment that is cost effective to collect on the scale necessary for the TAMC s statewide purposes. Most of the unpaved road condition assessment systems that are widely used evolved from paved road assessment systems. As a result, they use the extent and severity of surface distresses as their primary factor in assessing road quality. Focusing on surface distress as a measure of quality works well for paved roads because surface distresses change slowly and require a significant effort to repair, making the distresses relatively static over the course of a year [2,3,4]. This slow rate of change allows a single rating event every one to two years to provide a sufficient level of data for management purposes on paved roads. However, unlike paved roads, unpaved roads can have significant changes in surface condition over a matter of days or weeks [6], thus limiting the effectiveness of surface distress-driven condition assessment systems both at a network- and project-selection level. Rapid changes in the surface condition of unpaved roads cause condition data to become quickly outdated and, therefore, require frequent data collection cycles in order to remain valid for project-level guidance, making these types of systems difficult to adapt cost-effectively. Similarly, rating systems based on surface conditions are difficult to apply as a network-level measure since the condition of any road in the system may be highly variable during the year as distresses appear and short-term maintenance activity such as grading are completed. Significant surface condition changes in the network may take place in the span of a single week. This results in a network-level metric that can vary greatly from week to week depending on when ratings were collected. Surface condition is a primary factor that impacts use of a paved road by motorists and is directly related to the life of the most expensive layer of the pavement, which is surfacing, that typically drives major improvement work in a paved road network. Unpaved roads, however, can have many other factors that influence their functionality that are not related to surface condition. Unpaved roads are highly variable in their design, construction, use, and upkeep when compared with paved roads. Many unpaved roads do not contain basic inventory elements common to most paved roads, which makes the exclusive focus on surface condition problematic. These inventory items include sufficient ditches and culverts, minimum lane widths, shoulders, and sufficient structural gravel to support loads. Differences in inventory elements can adversely influence the use of the road and may have more of an impact on users than poor surface conditions. For example, road users may consider potholes or ruts on an unpaved road a secondary inconvenience if the unpaved Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 6

8 road is only nine-feet wide and the limited surface width will not allow the operation of two-way vehicle traffic at any significant speed. In this case, surface condition may be irrelevant to users. Similarly, an unpaved road without proper drainage is likely to perform poorly for any traffic volume regardless of the current surface condition [6]. Poor unpaved road surface condition does not always relate to the life of the surfacing layer and more typically may be rectified by low-cost grading. As such, unpaved road surface condition is not automatically indicative of a loss in value of the road as is the case with paved roads. 2.2 Premise of Inventory-Based Rating System The report titled Inventory Based Assessment Systems for Unpaved Roads [7] outlines the Inventory-Based Rating (IBR) system. This report described the premise behind the selection of basic road inventory features that influence road users and have a significant cost to create or maintain. These features are the basic large-scale elements that a good quality road possesses. The IBR system functions by defining a baseline condition for each of the inventory features in the system. This baseline condition for inventory features used characteristics that are considered acceptable for the majority of road users with guidance from design standards. The baseline condition does not imply a mandate to correct roads that do not meet baseline conditions but, rather, creates a reference to compare with other roads. The baseline condition is, in effect, a zeropoint in the system. Roads are assessed based on how they compare to the baseline condition in each of the inventory feature categories. Not meeting any of the baseline condition features will result in a road being rated lower than those that meet all baseline conditions. Inventory features used in this system (Surface Width, Drainage Adequacy and Structural Adequacy) do not change rapidly, require a significant construction or maintenance effort to improve, and are apparent enough to be evaluated from a moving vehicle without the need for fine measurement. Once initial ratings are established, this system only requires updates when a construction or rehabilitation activity occurs, or when a lack of maintenance causes loss or degradation of a road feature. Monitoring inventory features over time at a network level provides a measure that can track the impact of investments on the unpaved road network. The three inventory features used by the IBR system are assessed using the following criteria: Surface Width Surface width is assessed by estimating the approximate width of the traveled portion of the road, which includes travel lanes and any shoulder that is suitable for travel. Good Surface width of 22 feet or greater Fair Surface width between 16 to 21 feet Poor Surface width of 15 feet or less Drainage Adequacy Drainage adequacy is assessed by determining the presence or absence of a secondary ditch (high shoulder) that has the capacity to retain surface water and by estimating the difference in elevation between the ditch flow line or level of standing water in the ditch and the top of the edge of the shoulder. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 7

9 Good Two feet or more of difference in elevation between the ditch flow line or any standing water (whichever is less) and the top edge of the shoulder; no secondary ditches are present Fair Between two and 0.5 feet of difference in elevation between the ditch flow line or any standing water (whichever is less) and the edge of the shoulder, with or without the presence of secondary ditches; or, two feet or more vertical separation between the ditch flow line or any standing water (whichever is less) and the top edge of the shoulder where secondary ditches are present Poor Less than 0.5 feet of difference in elevation between the ditch flow line or any standing water (whichever is less) and the edge of the shoulder; secondary ditches may or may not be present Structural Adequacy Structural adequacy is assessed by the presence or lack of structural distresses (rutting or large potholes) during the previous year that required emergency maintenance to maintain serviceability. If the data are unknown, the assessment can be conducted using an estimate of gravel thickness if known. It is not the intent of this inventory feature to require involved testing or exploration of existing conditions. Ratings are to be made based on local institutional knowledge. Good Structural rutting or major (three feet or larger) potholes did not develop throughout the year and emergency maintenance was not required leaving the road as serviceable throughout the year (when plowed). Alternately, the road can be assessed by estimating the thickness of good quality gravel (crushed and dense graded). An estimated thickness of eight inches or more of good gravel would qualify a road for this level. Fair Limited structural rutting and/or some major (three feet or larger) potholes during the spring or very wet periods. The road is passable throughout the year, but emergency maintenance grading was necessary to maintain it during the wet periods. Placement of four inches of good quality gravel would be recommended as a fix. Alternately, the road can be assessed by determining the gravel thickness is four to seven inches, thus some additional gravel material could be added. Poor Structural rutting and/or major (three feet or larger) potholes are apparent during much of the year. The road is passable throughout the year, but frequent emergency maintenance is required. Placement of five to eight inches of good quality gravel would be recommended as a fix. Alternately, the road can be assessed by determining the gravel thickness is less than four inches, thus significant additional gravel material should be added. See Appendix C: Training Handout for further detail on assessment criteria. 3.0 Objective and Scope The objective of this study was to collect data on various types of unpaved roads under real world conditions to assess the repeatability and accuracy of the IBR system given the variety of users and the variety of road networks across the state of Michigan. The study provided benchmarking information on data collection speeds, identified areas where more guidance on the system was Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 8

10 necessary, and where the rating system needed to be refined. The pilot project provided an opportunity to determine training and guidance needs and to get direct feedback from transportation professionals who will be responsible for collecting and using the data locally. The data collected for this project provided local agencies and the TAMC with a first-hand look at the type of information they need to implement full-scale collection and provide a means for assessing the value of these data. Data collected in this pilot can be used to estimate the scope, cost, and other planning factors necessary for potential statewide rollout of the IBR collection. 4.0 Methods 4.1 Selection of Data Collection Locations Unpaved roads in Michigan vary greatly from county to county in how they are used, constructed, and maintained. The project team defined three general classifications of road networks based on overall function, management, and maintenance of unpaved roads in each county. The classifications used were: Low Volume Terminal Branch Networks Unpaved roads in Low Volume Terminal Branch Networks were primarily used as the ends of the road system where traffic volumes were low and primarily consist of areas where the road accessed few properties. In many cases, year-round access or use of the road is often not necessary. Counties in the Upper Peninsula and northern Lower Michigan generally fall into this category. Agricultural Grid Networks Unpaved roads in Agricultural Grid Networks were primarily used to support agriculture that is a large part of the local economy by providing regular access to farm fields. These types of road networks seasonally support higher volumes of traffic and need to support larger truck loads to meet the needs of users. Generally speaking, access is maintained all year because these types of networks serve residents and agriculture. Agricultural Grid Networks typically provided users with a higher amount of mobility and access than Low Volume Terminal Branch Networks. Suburban Residential Networks Unpaved roads in Suburban Residential Networks were primarily used as local access to rural residential properties located near urban centers. These types of roads provide access all year to primarily passenger vehicle traffic. These types of road networks were near urban centers and were primarily located in the population belt between Grand Rapids and Detroit. Figure 1 illustrates the project team s qualitative assessment of Michigan counties into these types of networks. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 9

11 Baraga Antrim Huron Van Buren Low Volume Terminal Branch Networks Agricultural Grid Networks Suburban Residential Networks Kalamazoo Figure 1: Qualitative classification of counties based on unpaved road network type. Volunteer pilot counties outlined in bold. The original goal of the project was to collect a total of 1,000 miles of unpaved road rating data using the IBR system in at least four counties made up of at least one from each of the three network classifications spread geographically throughout the state. This selected sample size could provide enough data to make accurate predictions for statewide data collection rates, check the validity of the system, and make improvements to the training materials that will be needed for statewide implementation. A selection of five counties distributed across each of the three network types were solicited for cooperation in collecting data for this study. Cooperation in the study was purely voluntary, and county road commission and regional planning staff participating in the study did so at their own expense. The following counties volunteered for data collection during this study: Antrim, Baraga, Kalamazoo, Huron, and Van Buren. Figure 1 illustrates the locations of the volunteer counties. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 10

12 4.2 Pre-Field Work Training Engineers from CTT trained agency employees from the five volunteer counties and planning agency representatives who participated in the pilot data collection prior to doing the actual ratings of the unpaved roads. The training consisted of a two-hour PowerPoint presentation with in-class rating exercises and a two-page quick reference handout. All of the training materials used during the pilot are included in Appendix C. Group rating exercises, which were conducted as part of the training program, helped to provide experience using the system prior to field rating. The agencies also received the Inventory Based Assessment Systems for Unpaved Roads report for their review prior to conducting the training. 4.3 Data Collection Methodology Three discrete data collection events comprised this pilot. The first set of data collection trips collected IBR data for each of the five volunteer counties. The IBR data consisting of Surface Width, Drainage Adequacy, and Structural Adequacy for each roadway segment were graded into one of three ranges (good, fair, or poor) according to the IBR system as well as rating productivity measures (see report section 2.2 Premise of Inventory-Based Rating System for more detail on the criteria used for these ratings). The IBR data collection was completed over the course of one to two days for each volunteer county. This series of collection events recorded the team consensus IBR data as well as blind IBR data on selected road segments. The second series of data collection events followed shortly after the completion of the IBR collection and included collecting gravel thickness measurements at randomly selected locations. CTT staff returned to each county for half a day to determine the depth of gravel on roads previously rated using the IBR system. Gravel depth measurements were used to determine the accuracy of local agency staff knowledge about road structure. The third data collection event was limited to Baraga County. This collection event gathered both IBR data for unpaved roads and PASER data for paved roads were collected in a joint effort. These data were used to determine the relative efficiency of data collection when combined with other rating events. The following people participated in the data collection: Kevin Arndt Huron County Road Commission Megan Arndt Kalamazoo Area Transportation Study Ken Barnett Van Buren County Road Commission Travis Bartholomew Road Commission of Kalamazoo County Tim Colling PhD., PE Michigan Technological University Anne Dahlquist Michigan Technological University Bill DeYoung Road Commission of Kalamazoo County Craig Ericksen Van Buren County Road Commission Bob Fitzgerald Antrim County Road Commission Bruce Holdwick Huron County Road Commission Joel Hoort Van Buren County Road Commission John Kiefer, PE Michigan Technological University Gautam Mani Southwest Michigan Planning Commission Doug Mills, PE Baraga County Road Commission Fred Nagler, PE Kalamazoo Area Transportation Study Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 11

13 Lynne Parker East Michigan Council of Governments Luke Peterson Michigan Technological University Mark Piotter, PE Huron County Road Commission Linnea Rader Van Buren County Road Commission Jeffrey Rautiola, PE Michigan Department of Transportation Steve Rouser Western Upper Peninsula Planning & Development Region Scott Swanson Baraga County Road Commission Burt Thompson, PE Antrim County Road Commission Sean Thorpe Michigan Technological University Pete Torola, PE Michigan Technological University Inventory Based Rating Data Collection The asset management program Roadsoft and the Roadsoft Laptop Data Collector (LDC) were the tools used for the pilot in order to collect IBR condition data quickly and accurately. Roadsoft is a GIS-based asset management program used by agencies in Michigan for storing, managing, and analyzing roadway assets and associated data. Roadsoft and the LDC use a unified base map for the State of Michigan (Framework base map), allowing data stored in Roadsoft to be related to other agencies including regional and state level planning and engineering groups. The pilot used Roadsoft and the LDC because they are likely the tools that would be used in full-scale collection. Figure 2: Roadsoft data set for Antrim County showing unpaved roads in yellow (gravel) and orange (earth). Each volunteer agency provided a copy of their Roadsoft database to the project team prior to the data collection event as displayed in Figure 2. The Roadsoft databases provided an initial inventory of the unpaved roads of the agencies road networks that was used for planning purposes to assess Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 12

14 routing and the size of collection areas. The project team worked with the individual volunteer agencies management, engineering staff, and foremen to plan each of the data collection events. The goal of data collection planning was to identify portions of the unpaved road networks that were representative of the rest of the county and to provide data in areas that would likely be useful for agency management of unpaved roads. Each volunteer agency had already defined the unpaved road network from historic data gathering events. For the most part, data collection areas were subdivided by township since they provided a meaningful reporting block and were indicative of individual township policy for constructing and maintaining unpaved roads. All data collected during the pilot were input directly into the LDC with GPS coordinates linking the rating location to the correct road segment to minimize the chance of transcription or location errors. The Roadsoft LDC is a software package used to collect data in the field for Roadsoft as shown in Figure 3. The LDC connects with a recreational-grade GPS unit to provide spatial locations of the collected data. Figure 3: Laptop Data Collector showing Huron County IBR collection data. Field collection of IBR data were completed with a minimum of three raters for safety, with individuals taking responsibility for driving, data entry into the LDC, and navigation. In many cases, two to four local agency and regional planning agency staff participated as the rating teams. CTT staff took the responsibility of entering data into the LDC and did not direct or guide the generation of ratings from the team to minimize their influence on raw data collection. Data collection occurred on a continuous basis from the moving collection vehicle. Where necessary, stops were made to investigate hard-to-see or hidden features. Group consensus determined the IBR number with each member participating in the rating. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 13

15 Initial hours of data collection at each county employed frequent physical checks of road width and ditch depth with a 25-foot hand tape measure to help calibrate the raters to field condition. Physical checks were also made in instances where raters could not come to consensus on the correct rating. As data collection progressed, these physical checks became fewer in frequency. Physical checks of measurements also verified consensus ratings after blind rating exercises (see section Gravel Thickness Data Collection). At random intervals (every 20 to 60 minutes) during the IBR data collection, the teams would collect blind ratings for a specific segment of road. During blind ratings, each person individually rated and recorded the IBR data for Surface Width, Drainage Adequacy, and Structural Adequacy for the current road segment without discussion or measurement. Blind ratings were completed only with what team members could individually see from their position in the vehicle. Team members could not exchange information or talk during blind ratings. After all team members had recorded their blind ratings on individual recording sheets, the entire rating group discussed what they thought the rating should be until a consensus rating was reached and both Surface Width and Drainage Adequacy were measured with the tape measure to verify the accuracy of the consensus measurement. CTT staff recorded the consensus rating in the LDC. The local agency representative had the final say on the Structural Adequacy portion of the consensus rating since gravel thickness could not be directly measured in the field during data collection. Rating productivity data were collected manually by recording start, end, and break times during the day and logging vehicle miles traveled and miles of road rated at several points in the day during active collection. The LDC s tools supplied the rated road mileage data. The productivity calculation included time to travel to and from data collection areas; however, lunch breaks and time spent driving to meet the rating team were not included. Rating productivity measures are representative of the overall average collection rate for the IBR data collection teams without collecting paved road condition or other data Gravel Thickness Data Collection Following the field collection of the IBR data, CTT staff returned to the pilot counties to collect gravel depth measurements at random locations that had been previously rated. Gravel depth measurements were collected by using a core drill or demolition hammer to expose the bottom of the gravel layer in a shallow pit or hole to verify the actual depth of gravel on the road. Gravel thickness was measured at the center of the travel lane on one randomly selected side of the road. Thickness measurements were collected in each county to determine how closely agency staff using only local knowledge could estimate gravel thickness for the Structural Adequacy category. CTT staff collected at least nine gravel depth samples in each county on roads with various Structural Adequacy ratings. CTT staff verified with local agency maintenance staff that no significant additions or removals of gravel thickness occurred between initial rating and collection of the gravel thickness measurements Combined PASER and IBR Collection The third and final data collection event occurred only in Baraga County. During the first of the two days of this collection event, the rating team collected IBR data on unpaved roads and PASER data on paved roads as the team drove the road network to simulate a joint collection effort. Only PASER data were collected on the final day of this collection effort to provide a comparison for PASER data productivity with and without the IBR data. These data will be used to determine the impact of adding IBR data collection efforts to PASER data collection. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 14

16 4.3.4 User Feedback CTT staff collected user feedback and comments on the IBR system from the participants during the study. They were collected at the training, during rating, and during a post-collection conference call meeting. This feedback helped to refine the rating system, correct training deficiencies, and identified areas that needed more thorough explanation during training. 5.0 Results 5.1 Feedback from Users There were 72 comments received from participants during training, data collection, and the postcollection conference call meeting. After repeat comments were removed, there remained 63 unique comments, which consisted of 13 comments on the software used, 37 comments on the IBR system, three comments on the training materials, and 10 miscellaneous comments. These comments are shown in Appendix A: User Feedback and Comments. 5.2 Data Summaries from Collection The project team collected IBR data from each of the five volunteer counties over the course of nine days of field collection. Each county had one to two data collection days with the exception of Baraga County, which had a third collection day where paved PASER data and unpaved IBR data were collected together. With the exception of the one day of combined collection in Baraga County, the team strictly collected unpaved road IBR data. Figures 4 through 7 summarize the IBR data collected during the pilot collection for each volunteer agency. The figures show the percentage of miles rated for individual IBR elements (Surface Width, Drainage Adequacy, and Structural Adequacy) as well as the aggregate IBR number. The IBR data profiles shown in these figures provide a characterization of how unpaved roads were used and maintained at each agency as part of the transportation system. Figure 8 illustrates the aggregate IBR number for the entire pilot on a 10-point scale similar to PASER data. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 15

17 Width Condition of Rated Network 100% 90% 24% 21% 20% 13% 80% 70% 60% 50% 40% 30% 20% 10% 0% 44% 23% 52% 92% 83% 77% 43% 54% 27% 7% 13% 3% 4% 1% Antrim Baraga Huron Kalamazoo Van Buren Pilot Total Good Fair Poor Figure 4: IBR Surface Width data as a percentage of roads rated during the pilot. 100% 90% 3% Drainage Condition of Rated Network 3% 5% 17% 80% 70% 60% 50% 40% 30% 20% 10% 0% 29% 38% 38% 45% 41% 93% 27% 68% 57% 52% 43% 35% 5% Antrim Baraga Huron 2% Kalamazoo Van Buren Pilot Total Good Fair Poor Figure 5: IBR Drainage Adequacy data as a percentage of roads rated during the pilot. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 16

18 100% 90% 80% Structure Condition of Rated Network 1% 0% 10% 9% 8% 12% 25% 70% 41% 60% 50% 40% 88% 90% 86% 97% 39% Good Fair Poor 30% 20% 46% 36% 10% 0% 6% 2% 1% Antrim Baraga Huron Kalamazoo Van Buren Pilot Total Figure 6: IBR Structural Adequacy data as a percentage of roads rated during the pilot. 100% 90% 0% 14% Percent of Each Agencies Roads Rated 7% 11% 7% 80% 47% 43% 70% 43% 60% 50% 40% 85% 87% 87% 23% Good Fair Poor 30% 20% 10% 0% 51% 46% 34% 10% 3% 2% Antrim Baraga Huron Kalamazoo Van Buren Pilot Total Figure 7: Combined IBR number as a percentage of roads rated during the pilot. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 17

19 40% IBR Ratings Collected During the Pilot All Agencies 35% 30% 25% 20% 15% 10% 5% 0% Good Fair Poor Figure 8: Converted 10-point scale IBR numbers collected during the pilot. Note that rating of 10 is reserved for new construction Antrim County The collection rate of IBR data in Antrim County was the slowest of all the pilot counties at an average of 6.3 miles rated per hour over the course of the two days of collection. Antrim County was classified as a Low Volume Terminal Branch Network because its population was less than 100,000 people [8] and more than 40 percent of the land area was covered by forests [9]. Data collection teams rated almost 72 miles of road segments as shown in red on Figure 9. A large portion of the network that was rated consisted of short-length, low-volume, seasonal, dead-end roads. Conditions on these unpaved roads necessitated low travel speeds as depicted Figure 10. The presence of many lakes and streams further divided the county and made efficient travel difficult. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 18

20 Figure 9: Antrim County pilot data collection. IBR data collected on roads highlighted in red. Unpaved roads in Antrim County generally had narrow road widths, no drainage, and no structure (gravel thickness) leading to overall low IBR scores. During the IBR data collection, several unpaved roads illustrated on the Framework base map terminated early or were non-existent. The data collection effort provided a means for verifying and correcting the far terminal ends of the Framework base map to better define the public road system in Michigan. It also provided county staff with a means for checking for encroachment and documenting road right-of-way maintenance activity to guard against forced abandonment legal actions. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 19

21 Figure 10: Antrim County unpaved road showing poor Surface Width, poor Drainage Adequacy, and poor Structural Adequacy Baraga County The IBR data collection in Baraga County proved to be slower than anticipated from a productivity standpoint. The rating teams collected 99 miles of unpaved road IBR data in two days of collection as depicted in red in Figure 11. Baraga County was classified as a Low Volume Terminal Branch Network for the IBR pilot because its population was less than 100,000 people [8] and more than 40 percent of the land area was covered by forests [9]. Unpaved roads in Baraga County provide mostly seasonal or very low volume access to recreational and forest properties. Most of these gravel roads were located at the far ends of the road network, typically dead-ending at a property boundary. These characteristics required more total miles of travel to rate a road segment due to backtracking and long transits between relatively short unpaved road segments. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 20

22 Figure 11: Baraga County pilot data collection. Ninety-nine miles of IBR data were collected on roads highlighted in red. The height of roadside vegetation in Baraga County greatly complicated data collection by obscuring the presence of ditches. Figure 12 illustrates the difficulty with rating during June and July when vegetation was at its fullest. In many cases, it was nearly impossible for the rating team to determine ditch depth without stopping, exiting the vehicle, and sending a member of the rating team down to probe the ditch. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 21

23 Figure 12: Thickly vegetated roadsides in Baraga County. Note that there was a significant (good) ditch on both sides of this road that was obscured by grass and other vegetation. In general, unpaved roads in Baraga County were narrow (usually only slightly wider than one lane) had minimal drainage, and had little or no structure (gravel thickness), this lead to overall low IBR scores. While all of these characteristics would be expected and are generally appropriate for short, very low-volume, low-speed, unpaved roads that primarily serve as access to a few rural properties, almost all of the non-seasonal unpaved roads rated in Baraga County would provide better service to users if they were properly ditched and had the addition of more gravel Huron County Huron County was classified as an Agricultural Grid Network for the IBR pilot because its population was less than 100,000 people [8], less than 40 percent of the land area was covered by forests [9] and the road network follows a strong one-mile-long section line grid pattern. The Huron County rating team collected 245 miles of IBR data in less than nine hours, which was the most productive collection county in the pilot. IBR data on Agricultural Grid Networks can be collected very efficiently since there is little need for backtracking due to high connectivity, and the road conditions allow for higher speed of travel. Overall, 85 percent of the travel in Huron County was on unpaved road segments being actively rated. The terrain of Huron County has very flat vertical elevation changes and few horizontal curves as depicted in Figure 14. The interconnected unpaved grid pattern created by the section lines, as shown in Figure 13, increased collection speeds because it allowed for long collection runs through entire townships. Huron County s unpaved roads were generally wide, fully ditched, and contained significant structural gravel layers; this led to high IBR scores. These types of roads allow higher speed, volume, and travel loads. Unpaved roads in Huron County are relied upon to connect one location to another (farm to market roads) and, generally speaking, were not dead ends of the system. In Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 22

24 fact, all of the townships where data were collected for the pilot had significantly more unpaved miles of road than paved miles as observed in Figure 13. Figure 13: Huron County pilot data collection. IBR data collected on roads highlighted in red. Figure 14: Huron County unpaved road showing good Surface Width and good Drainage Adequacy. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 23

25 5.2.4 Kalamazoo County Kalamazoo County was classified as a Suburban Residential Network for the IBR pilot because its population was over 100,000 [8]. Data for the entire 103-mile network were collected in one day and are illustrated in Figure 15. The Kalamazoo County unpaved network was concentrated along the Kalamazoo County borders and away from the City of Kalamazoo. The land use of the unpaved road network was predominantly agriculture and rural residential as depicted in Figure 16. Figure 15: Kalamazoo County pilot data collection. IBR data collected on roads highlighted in red. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 24

26 Figure 16: Kalamazoo County unpaved road showing fair Surface Width and poor Drainage Adequacy Van Buren County In Van Buren County, the rating teams collected 141 miles of IBR data in two days with two local agency employees and one planning agency employee on the first day and three local agency employees on the second day. The collection rate was 11.4 miles per hour over the two days of collection. The land use for Van Buren County was mostly rural residential and agricultural. The population of Van Buren County was less than 100,000 people [8] and less than 40 percent of the land area was covered by forests [9] so it was classified as an Agricultural Grid Network for the IBR pilot. The unpaved network was interconnected with paved roads so segments usually only had to be driven once as shown in Figure 17. Van Buren County had more paved roads compared to Huron County, so there was more traveling between unpaved segments to collect data. As with Baraga County, many of the unpaved roads had high grass along the shoulders that made assessing Drainage Adequacy difficult. Figure 18 shows a typical unpaved road in the Van Buren County road network. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 25

27 Figure 17: Van Buren County pilot data collection. IBR data collected on roads highlighted in red. Figure 18: Van Buren County unpaved road with fair Surface Width and fair Drainage Adequacy. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 26

28 5.3 Benchmarking Rating Productivity Productivity benchmarking can be used to forecast the time commitment for collecting the IBR system data for gravel roads in the state of Michigan as part of a wider effort. The data collection speed of the IBR system was recorded and calculated for each pilot county individually to take into account their unique geographic and road network features. Many factors influenced the collection speed of the IBR system. The main factors were the type of network (Low Volume Terminal Branch Networks, Agricultural Grid Networks, or Suburban Residential Networks), which related to the connectivity of the unpaved roads, and the condition of the road being rated, which determined travel speed. The time of the year that the IBR condition is assessed is also a factor on collection speed. Data collection later in the growing season is likely to be more difficult and less reliable since Drainage Adequacy features can become hidden in roadside vegetative growth. A summary of the IBR data collected during the pilot is shown in Table 1. Collection time was calculated based on the time actively rating roads or transiting to and from rating areas. Breaks for lunch and switching of rating crews were deducted from actual productive rating time. Rating Productivity (Miles/Hr) Total Miles Driven Travel Speed (Miles/Hr) Collection Gravel County Time (Hr) Miles Rated Antrim % Miles Rated/ Total Driven Baraga % Huron % Kalamazoo % Van Buren % Total % Table 1: IBR data collection statistics by county. Statistics are indicative of collecting only IBR data on unpaved roads Combined PASER/IBR Collection Benchmarking In Baraga County, there were four days of data collection. During the first two days, only IBR data were collected and resulted in just over 99 miles being collected at a rate of 8.8 miles collected per hour. The IBR and PASER data were collected together on the third day and resulted in 40.9 miles of gravel IBR data and miles of paved PASER data being collected for a total of miles of data collected at a rate of 20.9 miles collected per hour. Only PASER data were collected the fourth day and it resulted in 81.6 miles of paved PASER data being collected at a rate of 14.8 miles collected per hour. The rate when collecting IBR data and PASER data together was higher than when collecting PASER data only or when collecting IBR data only due to the minimization of roads traveled without rating. A summary of the data collection from Baraga County is shown in Table 2. Inventory Based Rating System for Unpaved Roads, Pilot Data Collection Report Page 27