ST. CLOUD AREA PLANNING ORGANIZATION

Transcription

1 PAVEMENT MANAGEMENT STUDY - FINAL REPORT ST. CLOUD AREA PLANNING ORGANIZATION In Coordination With:

2 ST. CLOUD AREA PLANNING ORGANIZATION PAVEMENT MANAGEMENT STUDY FINAL REPORT Prepared by: Amy Grothaus, PE Daniel Wegman, PE Braun Intertec Corporation Lance Bernard Craig Vaughn, PE SRF Consulting Group, Inc. December 2015

3 ACKNOWLEDGEMENTS This project was advised by members of a Technical Advisory Committee (TAC) that included APO staff members Jarrett Hubbard and Angie Stenson, Ms. Jodi Teich of Stearns County, David Janisch of MnDOT and Darren Nelson of MnDOT. Furthermore, several Project Advisory Committee (PAC) meetings were held throughout the project and were attendees by staff of various member jurisdictions. The project team would like to thank both the TAC and PAC members and attendees for their participation in the project.

4 Description Table of Contents Page A. Introduction... 1 A.1. Pavement Management Study Project Description... 1 A.2. Project Scope... 2 A.3. Project Use... 2 A.4. Data Collection... 4 A.4.a. MnDOT... 6 A.4.b. Stearns County... 9 A.4.c. Sherburne County A.4.d. Benton County A.4.e. City of Sartell A.4.f. City of St. Cloud A.4.g. All Others B. Phase 2 - Local Pavement Ratings B.1. Ride Quality and Surface Condition Ratings B.2. Follow-up Evaluations C. Phase 3 Creation of a Unified Database D. Phase 4 Assessment Results D.1. Latest Available Data D.2. Pavement Projections using Performance Curves E. Phase 5 Performance Measures and Targets E.1. Performance Measure Framework E.2. Framework Findings F. Phase 6 Prioritized Project List and Funding Needs F.1. Methodology for Project Prioritization and Funding Need F.2. Discussion of Project Prioritization and Funding Need G. Phase 7 Pavement Management Process Appendix A: MnDOT Scoring Conversion Tables Appendix B: Maps of Latest Available Condition Data (State, County, and Local) Appendix C: Performance Curves Appendix D: Maps of Projected 2015 Condition Data (State, County, and Local) Appendix E: Listing of Potential Projects Appendix F: Performance Measure Framework Planning Level Cost Estimates

5 A. Introduction A.1. Pavement Management Study Project Description According to its website, the St. Cloud Area Planning Organization (APO) provides necessary information for making decisions on when, where, and what type of improvements should be made to the transportation system to satisfy current and anticipated travel demands, while promoting land development patterns that are consistent with community goals and objectives. The recent federal transportation bill, Moving Ahead for Progress in the 21st Century Act (MAP-21), requires greater agency emphasis on performance measures. Therefore, metropolitan areas and state agencies need to integrate and conduct performance measures as a key part of their planning activities. Performance measures are designed to serve as a benchmark to evaluate and quantify progress. The MAP-21 performance measurement requirements include the evaluation of the National Highway System (NHS) and other MAP-21 NHS Principal Arterial routes from a pavement condition perspective. This performance-based approach is meant to improve accountability of federal transportation investments, assess risks related to different performance levels, and increase transparency. The objective of the Pavement Management Study was to assist the APO in meeting these requirements. To fulfil the MAP-21 requirements, performance measures have been adopted as part of the St. Cloud Area Planning Organization (APO) Long Range Transportation Plan (LRTP). The Plan s performance measures focus on a variety of transportation elements (e.g., access and mobility, system connectivity, system management, and active transportation). The pavement management performance measures related to this study include: Limit pavement in poor condition and maintain a percentage of the system in good condition Percent of investment in road preservation and maintenance projects. The St. Cloud APO Pavement Management Study is moving the MAP-21 requirements forward by creating a baseline for tracking pavement conditions and helping inform investment decisions to maintain the system in a state of good repair. Pavements within the APO are managed by a number of jurisdictions in the St. Cloud area, including the Minnesota Department of Transportation (MnDOT) and several counties, cities, and townships. Together, these jurisdictions adopt transportation plans, projects and policies for the region. As such, the study is helping set the stage for the APO to work with MnDOT and local jurisdictions to track pavement conditions on other roadways, such as minor arterials and collectors.

6 Page 2 Through the completion of the Pavement Management Study, information related to existing and future pavement conditions has been gathered as a way of determining system need. Furthermore, guidance related to the management of the data and communication of the pavement information moving forward has been provided, allowing the APO to better address future needs. A.2. Project Scope Our scope of services for this project was submitted as a Proposal to Ms. Angie Stenson, dated March 6, We received a signed contract to proceed on April 23, Tasks performed in accordance with our authorized scope of services included: Overall Project Management, Inventory and Data Collection, Local Pavement Ratings, Creation of a Unified Database System, Performing a Needs Assessment, Public Involvement Workshops, Developing a List of Prioritized Projects, Performing a Pavement Management Software Review, and Reporting. As work on the project progressed, and as was directed by the Technical Advisory Committee (TAC), work on some tasks was modified, as needed, and new tasks were incorporated. Newly added tasks included data entry forms within the APO database, the development of pavement performance curves, and projection of funding needs. It was determined by the TAC that through the completion of these tasks, significant value would be provided to the project deliverables. Furthermore, it was determined that information obtained would assist the APO with the management and monitoring of the pavement network moving forward. A.3. Project Use The APO will be incorporating elements of the study into their next Long Range Transportation Plan update. In the meantime, the study serves as a foundation for meeting MAP-21 requirements by implementing performance measures that track and monitor a state of good repair from a pavement perspective. Information from the study will also serve as a means for area agencies to communicate transportation needs. It is also likely that information from the study will assist in the technical evaluation of future STP project applications.

7 Page 3 Phase 1 Inventory and Data Collection The first phase of the APO Pavement Management Study included establishing an inventory of APO roadways, followed by the collection of pavement data for those roadways. Roadways that were to be included in the study were classified as Minor Collector and higher and are maintained by a number of agencies in the St. Cloud area. At the onset of the project, the APO provided two GIS shapefiles, each of which contained some useful attribute data such as roadway name and functional class. The two shapefiles were merged and served as the primary building block for the study (Figure 1). Figure 1 APO Roadways

8 Page 4 Figure 2 provides a breakdown of APO mileage, by jurisdiction Township (Minden, Haven and Le Sauk) City of St Cloud City of St Joseph City of Sartell City of Sauk Rapids City of St Stephen 47.0 City of Waite Park Benton County 23.9 Sherburne County Stearns County MnDOT Figure 2 APO Study Mileage, by Jurisdiction A.4. Data Collection Data collection included the gathering and compiling of pavement data from each of the jurisdictions. As part of our work on this Phase, the project team conducted interviews with each of the jurisdictions. The intent of the interviews was to understand their pavement management practices, available resources, historical expenditures, and desired pavement condition levels. The following key themes emerged from these interviews: In general, the local jurisdictions do not have a formal pavement management system. Pavement data is collected and maintained in various forms (e.g., spreadsheets and GIS), The local jurisdictions do not actively track historical revenue/expenditures dedicated for past preservation projects, Maintenance activities generally reflect current industry standards, and

9 Page 5 For the most part, the local jurisdictions (cities and townships) do not have formal pavement performance measures that are being tracked or monitored. Using the list of known APO roadways established in the Inventory phase, each agency was provided a list of roadways within their jurisdiction that are within the APO boundary. For each roadway, agencies were asked to populate and/or provide a variety of pavement-related data. As previously noted, the roadways are classified by the APO as Minor Collector and higher. This is despite local jurisdictional classifications, which may vary from those of the APO. Figure 3 provides a breakdown of mileage by functional class designation. As is shown, the majority of the roadways are classified as Major Collectors. 40% 35% 34% 30% 25% 20% 26% 28% 15% 12% 10% 5% 0% Minor Collector Major Collector Minor Arterial Principal Arterial - Other Figure 3 APO Study Mileage, by Functional Class Designation

10 Page 6 As anticipated, agency data existed in a variety of formats including spreadsheets, GIS, third-party pavement management systems, and hard-copy paper files. Pavement data was compiled, by jurisdiction, and included the attributes listed below. It should be noted that not all information existed or was known. For example, in some cases, agencies did not know when a roadway was constructed or last repaired, or pavement condition data was not being collected. For this reason, some of the information was initially left blank, for completion at a later time. Roadway name Surface type Roadway limits Dimensions (length and width) Functional class GIS ID Owner/jurisdiction Construction and repair history 5-year Capital Improvement Plan (CIP) Pavement condition(s) Provided below is a summary of the pavement data and management practices gathered from each of the jurisdictions. A.4.a. MnDOT MnDOT APO roadways total approximately 119 centerline miles, comprising about 26 percent of the APO system. The roadways are classified mainly as Principal Arterial, with the exception of a few that are classified as Minor Arterial. Pavement surface types consist of both concrete and bituminous. MnDOT maintains and tracks their pavement information using an internal Pavement Management System. For each roadway, a historical record of construction and repair information exists. Based on information provided for the APO roadways, all of those maintained by MnDOT have received some sort of treatment within the past eleven years. To evaluate roadway conditions, MnDOT uses a Pathway Services Inc. Digital Inspection Vehicle (DIV) for data collection. This same vehicle is used to collect condition data for counties in Minnesota. Lasers mounted to the DIV collect roughness data, which is converted to an International Roughness Index (IRI). Roughness surveys are performed every year on all roadways. Once collected, the IRI information is converted to a Ride Quality Index (RQI) rating. RQI ratings range from 0.0 to 5.0, with 5.0 representing a perfectly smooth roadway. Table 1 provides how MnDOT classifies the various ride ratings and measures performance.

11 Page 7 Table 1. RQI Descriptions and Performance Measures Descriptive Category RQI Range Performance Measure Very Good Good Good Fair Fair Poor Very Poor Poor RQI is used by MnDOT in the determination of Remaining Service Life (RSL). This is the time, in years, until the RQI reaches a score of 2.5. Roadways are considered to have reached the end of their design life when the RQI reaches a rating of 2.5. It should be noted that a roadway with a rating of 2.5 can be driven on; however, it will likely feel uncomfortable to the traveling public and generally needs some type of rehabilitation. In addition to lasers, the DIV is equipped with several cameras used to capture pavement surface distress (cracking). MnDOT performs distress surveys every year on the interstates, and every other year on non-interstate roadways. As an exception, in order to establish a benchmark, non-interstate roadways may be evaluated yearly if some type of rehabilitation has been performed. Using images collected by the cameras, a detailed crack inventory is performed. This information factors into a Surface Rating (SR), which uses a 0.0 to 4.0 scale. The higher the SR, the fewer cracks there are. Using the RQI and SR, an overall Pavement Quality Index (PQI) is calculated. PQI is a composite of ride and surface quality and uses a 0.0 to 4.5 scale. MnDOT uses the PQI to determine whether or not the state highway system is meeting performance thresholds. Overall, MnDOT s principal arterials must meet a PQI of 3.0 or higher and non-principal arterials must meet a PQI of 2.8 or higher. Together, MnDOT uses the three indices to represent pavement condition and to predict future conditions. They are also used by MnDOT for project planning and programming purposes. Table 2 provides a summary of the indices MnDOT uses and provided for the APO study.

12 Page 8 Table 2. MnDOT Pavement Condition Indices Index Pavement Attribute Rating Scale Ride Quality Index (RQI) Pavement Roughness Surface Rating (SR) Pavement Distress Pavement Quality Index (PQI) Overall Pavement Quality Looking specifically at the data MnDOT provided for the APO roadways, roughly 60 percent were last evaluated in 2013 and about 40 percent were last evaluated in Historical condition data through 2007 was provided and is summarized in Table 3. Overall, existing conditions have essentially been maintained, residing within the good category as it relates to ride quality. Table 3. Historical Condition, MnDOT APO Roadways Evaluation Year Average RQI Average SR Average PQI 2007/ / / / Each year, MnDOT produces a Pavement Management Report. The intent of the report is to discuss performance trends compared to established goals, and to compare conditions against other transportation partners. MnDOT s established performance goals state that at least 70 percent of the interstate system must have a good RQI rating, and that no more than 2 percent can have a poor RQI rating. For non-interstate roadways, 60 percent or more must have a good RQI rating and no more than 10 percent can have a poor RQI rating. MnDOT s pavement management system uses pavement performance curves to predict future conditions. Various funding scenarios are then reviewed to evaluate the impacts on the system as a whole. For actual project selection, decision tree matrices for each surface type are used. Factors such as distress, ride quality, the presence of curb and the extent of rutting will trigger various methods of repair.

13 Page 9 A.4.b. Stearns County Roughly 36 percent of the APO roadways are maintained by Stearns County, totaling approximately 162 centerline miles. The county tracks and maintains their pavement data using spreadsheets and GIS, and does not utilize a pavement management system per se. Roadways are classified as both collectors and arterials, and are largely surfaced with bituminous, with the exception of a few concrete roadways. The county is tracking historical preservation and rehabilitation projects, and is also maintaining a Capital Improvement Plan, extending out to Preservation type projects include seal coating, general maintenance of roads, and the reconstruction/rehabilitation of roads. After many years of falling behind on seal coating operations, and at the urging of staff, the County Board recently increased the amount for annual seal coating maintenance to $700,000, which allows for the seal coating of approximately 52 miles per year. This represents an increase of $400,000 per year from the 2005 budget level of approximately $300,000. The County often contracts for approximately 20 miles of reclaim and overlay or mill and overlay work per year; however, in 2011 this was increased to 30 miles. Additionally, the County has saved money by performing more roadway maintenance and repair tasks with its own maintenance staff. As previously noted, MnDOT performs pavement evaluations for Minnesota Counties using their DIV. Provided by MnDOT State Aid, evaluations are performed every four years on all County State Aid Highways (CSAHs). At the same time, counties have the option to authorize MnDOT to also evaluate their County Roads (CRs). MnDOT last performed evaluations for the Stearns County network in 2014, with previous evaluations performed in 2010 and In all survey years, both the CSAHs and CRs were evaluated. Table 4 summarizes the condition data provided by Stearns County for the APO roadways. As shown, the PQIs have been deteriorating slightly from 2006 to 2014, going from a 3.4 to a 3.2. RQI and SR were not provided for 2006 and Table 4. Historical Condition, Stearns County APO Roadways Evaluation Year Average RQI Average SR Average PQI

14 Page 10 A.4.c. Sherburne County All 24 miles of the Sherburne County APO roadways have a bituminous surface. Classified as both collectors and arterials, the roadways account for slightly more than 5 percent of the APO roads. The roadways have been evaluated twice by MnDOT, once in 2008 and again in 2012 (Table 5). A third round of evaluations will be performed by MnDOT in Since 2008, the ride quality of evaluated roadways has deteriorated from good to fair. Overall, the PQI has also decreased, going from an average of 3.0 to 2.7. Sherburne County recently implemented a pavement management system developed by RtVision. Working off of a qualitative rating assigned internally by the County, the system stores numerical ratings on a 1 to 5 scale and is used for project prioritization. The internal qualitative evaluations were performed in 2013, with the intent to perform subsequent evaluations every two years. The qualitative ratings were not provided as part of the APO study. Historical preventive and rehabilitation data dated back to 1990, with an overall pavement surface age of about 14 years. Table 5. Historical Condition, Sherburne County APO Roadways Evaluation Year Average RQI Average SR Average PQI A.4.d. Benton County Approximately 47 miles of collector and arterial Benton County roads are part of the APO network. The roadways comprise about 10 percent of the overall APO mileage and all have a bituminous surface. Historical project history information was provided by the County for nearly all of the study roadways, with an overall average pavement age of about 23 years. Pavement condition surveys were performed by MnDOT in 2011 and in 2015 (to date, the 2015 data had not been compiled). Based on the calculated scores, the average RQI in 2011 was 2.7, the average SR was 3.2, and the average PQI was a 2.9. Benton County does not utilize a pavement management system and is tracking their pavement data using spreadsheets. This information is used to help inform the County s Capital Improvement Program (CIP). On average, between 2009 and 2014, the County has spent over a million dollars for the

15 Page 11 maintenance of roads. The reconstruction and rehabilitation of roads has ranged between two to six million dollars a year ( ). A.4.e. City of Sartell Sartell is the only city within the APO boundary that is utilizing a third-party pavement management system (PMS). The system was developed by Cartegraph and is used by the City to establish the need for budget increases. For the 15 miles of collector and arterial roadways within the APO boundary, condition surveys were last performed in Recommendations have been provided to the City to perform follow-up surveys every three years and will potentially be performed again in Calculated condition scores for Sartell are on a 0 to 100 scale, with 0 representing a failed roadway and 100 representing a brand new roadway. Referred to as an Overall Condition Index (OCI), the average rating in 2013 was a 64 and is a measure of pavement surface distress. Ride quality is not included in the OCI, nor is this information being collected for the roadways. The OCI ratings are used by the City to determine ideal candidates for preventive and major repairs. When considering reconstruction, the need for utility repairs is a large factor in the decision process. The last reconstruction date was provided for each of the roadways, with an average pavement age of approximately 14 years. Information on preventive maintenance was not provided for the study. A.4.f. City of St. Cloud The City of St. Cloud maintains approximately 56 miles of bituminous roadways that are within the APO boundary, accounting for about 12 percent of the overall APO mileage. Last performed in 2012, the City conducts surface distress evaluations for each of the roadways. Using a modified PASER rating procedure, the City assigns a 1.0 to 5.0 rating based on the extent of surface distress. A rating of 1.0 indicates extreme crack erosion, while a rating of 5.0 indicates no distress. During the surface evaluations, the City is also collecting information related to what they refer to as uniformity and weathering. Uniformity is an indicator of patching, bumps, sags and ruts, while weathering is indicative of seal coating condition and whether or not the surface has exposed aggregate. Although ride quality data is not being collected by the City, uniformity may provide some sort of indication as to the ride quality. Construction history was not provided for the St. Cloud roadways. The City is maintaining a 5-year CIP that includes both preventive and major rehabilitation projects.

16 Page 12 A.4.g. All Others For the 20 miles of roadways maintained by the Cities of Sauk Rapids, St. Joseph, St. Stephen and Waite Park, little to no pavement information was provided, although some project history data was provided by Sauk Rapids and Waite Park. No pavement data was provided for the 5.6 miles of township roadways. According to our discussions with these jurisdictions, condition and/or ride quality surveys are not being performed. To determine pavement condition for these roadways, which totaled about 25.6 miles, ride quality and surface ratings were performed as part of this study. The process used to collect and calculate this information is discussed in the Local Pavement Ratings section of this report. As a whole, an extensive amount of pavement data was collected for the APO roadways. Of the 448 miles, approximately 94 percent had recent surface condition data (some had multiple years of historical data), and nearly 80 percent had available ride quality data. Attribute data including roadway limits, dimensions, functional class and surface type was able to be populated for the entire data set. Additionally, the project team was able to gather project history information for about 85 percent of the roadways.

17 Page 13 B. Phase 2 - Local Pavement Ratings B.1. Ride Quality and Surface Condition Ratings As determined through Phase 1, the total APO centerline mileage is approximately 448 miles. Through the Inventory and Data Collection process, only about 6 percent (25.6 miles) were identified as needing a surface condition evaluation. This primarily included township roadways and a portion of the city roadways. Of the 94 percent that already had surface condition data, nearly 80 percent were utilizing the MnDOT rating methodology. Since the vast majority of APO pavements had been evaluated using the MnDOT methodology, this same methodology was used for data that was to be collected as part of the study. For roadways requiring surface condition evaluations, surveys were performed in the summer of To perform the evaluations, field raters were equipped with a handheld tablet device that was tied to GPS. The tablet was preloaded with segment information, as well as a complete list of the MnDOT method distress types and severities, and allowed the rater to visually see their exact spatial location during data collection. The tablet facilitated the data collection process and increased data gathering quality by eliminating the need to re-enter data back in the office. For roadways that did not have ride quality data, which essentially included all non-mndot and non- County maintained roadways, International Roughness Index (IRI) data was also collected in IRI data was collected continuously along the roadway in both wheelpaths in a single direction of travel. The average right and left wheelpath IRI (in inches per mile) was then determined on a per mile and per segment basis. Following the MnDOT methodology, IRI data was correlated to an RQI, and collected distresses were compiled and used to calculate a Surface Rating (SR). Both the ride quality and surface condition data was then used to calculate an overall Pavement Quality Index (PQI) for each segment. B.2. Follow-up Evaluations To meet Federal MAP-21 requirements, the APO must demonstrate their commitment to maintaining the existing transportation network. To continually monitor and track roadway conditions, follow-up evaluations of surface and ride quality should be performed. Standard industry practices are to perform repeat evaluations every two to four years so as to capture ongoing changes in pavement condition. Based on historical data and practices, MnDOT will likely continue to evaluate their roadways on a yearly or every other year basis. It is also likely that MnDOT will continue to conduct evaluations every four

18 Page 14 years for the county maintained roadways. It is recommended that as these evaluations are performed, results are provided to the APO for inclusion in the unified pavement management study database. For the city and township maintained roadways, it is recommended that repeat evaluations be performed in accordance with MnDOT s rating methodology. Since nearly 80 percent of the APO roadways are currently evaluated using this method, following the same rating procedure will simplify performance measure goals and reporting. It will also simplify the process for tracking and comparing any trends in pavement condition. To reduce the number of needed evaluations, since some of the cities are performing surface evaluations using other methodologies, the APO could normalize that data to a MnDOT rating, as was done for this study. Following the MnDOT rating procedure would reduce the surface evaluation mileage; however, ride quality data would still be needed for all city and township maintained roadways. Regardless of the approach, it is recommended that follow-up evaluations are performed, and that the frequency be no more than every four years. This is based on the functional class designations, which indicate high traffic volumes that can lead to the rapid deterioration of pavements if not sufficiently monitored and maintained. As part of our work on this Phase, condition data obtained from the jurisdictions and from our field evaluations was normalized into a single rating system. Having the data in a single rating system allows the APO to easily contrast and compare relative pavement conditions from one roadway to another or from one jurisdiction to another. It also provides consistency and a better understanding of the condition ratings. As previously noted, of the 94 percent that already had condition data, nearly 80 percent utilized the MnDOT rating methodology which includes both surface and ride quality. Since the vast majority of APO pavements were evaluated using the MnDOT methodology, other rating methodologies, which included OCI (City of Sartell) and a modified PASER rating (City of St. Cloud), were normalized to the MnDOT methodology. To normalize the OCI and PASER surface rating scores to a MnDOT surface rating score, surface evaluations were performed on a sample set of roadway segments using both rating methodologies. The collected distresses from each rating procedure were used to calculate the respective rating scores. Figures 4 and 5 provide a sampling of the collected data used to determine how the various scores correlated to one another. Using the correlation, SR scores were assigned to each of the Sartell and St. Cloud roadway segments.

19 Page 15 To obtain a PQI for the Sartell and St. Cloud roadway segments, ride quality data was collected and correlated to an RQI following MnDOT s methodology. The SR and RQI scores were then used to calculate an overall PQI. Appendix A provides detailed information related to the conversion to MnDOT scores. For roadways included in the study that did not have condition or ride quality data, data was collected following MnDOT s methodology and calculation process. 4 SR vs OCI Figure 4 Correlation of OCI to SR Ratings SR vs PASER Figure 5 Correlation of PASER to SR Ratings

20 Page 16 C. Phase 3 Creation of a Unified Database At the completion of Phases 1 and 2, a single, unified database was compiled. The database is linked to GIS and contains all data collected as part of the study. While creating the database, the data was normalized to minimize redundancy and was organized into columns (data attributes) and numerous relational tables. Essentially, smaller, less redundant tables were created for consistency. Each roadway segment was assigned a unique ID that is stored in GIS and throughout the database, and each attribute, such as functional class, was assigned a lookup ID. The objective was to isolate data so that additions, deletions, and modifications of an attribute can be made in a single location and then propagated through the rest of the database. A primary objective for the unified database was that it be structured such that the data could be easily queried into a pavement management system database, should the APO decide to implement a thirdparty system. Through discussions with APO staff, as well as the TAC and PAC, it was determined that a third-party pavement management system would not be immediately implemented by the APO. In large part, the reasons for this included the immediate required functionality, which was determined to be fairly basic, and the desire for a system that was extremely user-friendly. As such, the project team worked to include some enhanced functionality within the unified database that would allow for easy data entry, as well as the creation of necessary summary reports. Data entry forms incorporated in the unified database allow for the revision of attribute data, such as surface type and functional class, the addition of new condition data, and the entry of completed maintenance project information. Data is entered by the user via easy-to-use, pull-down dialog boxes. Revised or newly added data is then applied to the pertinent database tables for storage. Furthermore, once data is updated via the forms, it becomes available for immediate reporting and can be exported to GIS for the creation of color-coded maps. Moving forward, as the APO updates the database via the data entry forms and reports on the data, additional functionality may be desired that cannot be accomplished through the use of the database and GIS. As previously noted, the structure of the database, and its link to GIS, should allow for the straightforward implementation of third-party pavement management system.

21 Page 17 D. Phase 4 Assessment Results D.1. Latest Available Data Approximately 82 percent of the APO roadways were evaluated between 2013 and 2015, and nearly 18 percent (81 miles) were evaluated in 2010 and In large part, the older survey data from 2010 and 2011 included the Benton County roadways, which as previously noted, were evaluated in 2015 but were not compiled in time for this study. Once that data becomes available, nearly all of the study mileage will have a recent surface and ride quality rating. Using the latest available ratings, Figure 6 provides a breakdown, by agency, of average weighted Ride Quality Index (RQI), Surface Rating (SR), and Pavement Quality Index (PQI). For the APO as a whole, the average weighted RQI is a 2.8. Using MnDOT s performance measures, this is at the high end of the fair category which extends from 2.1 to 3.0. Once the RQI reaches a 2.5, the design life may be reached and some type of major rehabilitation may be required. In terms of SR, the APO as a whole has an average rating of 3.3 on a 4.0 scale, indicating the roadways have a fairly minimal amount of visible surface cracking. The APO s composite PQI has an average rating of 3.1 on a 0.0 to 4.5 scale. Generally speaking, a rating of 3.1 exceeds the current performance measure target of 3.0 that MnDOT has set for their principal arterials. Appendix B provides maps of the Latest Available Condition data for each jurisdictional type (state, county, or local) Figure 6 Latest Available Average Ratings RQI (0-5.0) SR (0-4.0) PQI (0-4.5)

22 Page 18 D.2. Pavement Projections using Performance Curves As noted in Section E.1, APO roadways were evaluated between 2010 and To better assess current system need as it relates to the performance measures and targets, performance curves were developed for the APO pavement network. Using actual historical condition data provided for the roadways, the objective of the curves was to project the latest available condition data to 2015 conditions. Given the oldest survey date of 2010, at most, roadways were projected out five years. The projection provided for more accurate system need assessments and prioritized project lists. To develop the curves, like pavements were grouped together. Groupings were based on three primary attributes including jurisdiction (state, county or local), surface type (bituminous or concrete), and functional class (minor collector, major collector, minor arterial, and principal arterial). For each group, scatter plots of pavement age (at the time of the survey) and pavement condition were produced. Using a horizontal axis for pavement age and a vertical axis for pavement condition, the plots correlate condition to age. It should be noted that some groups did not have enough data points for the development of a curve. As such, a curve was not developed for all groupings. Appendix C provides the performance curves developed as part of the study. When producing the plots, outlier data points were excluded and a best-fit curve was determined by the data. The equation for the curve provided the rate of decay for pavements within each group and was used to project 2015 condition scores. For each jurisdiction, Appendix D provides maps of the projected 2015 condition scores using the curves.

23 Page 19 E. Phase 5 Performance Measures and Targets E.1. Performance Measure Framework To help comply with MAP-21 requirements, a performance measure framework (Tables 6 through 8) was established for each jurisdiction. It is important to note that the performance measure indices provided in Tables 6 through 8 are intended to serve as a benchmark and were not adopted as part of this study. Instead, indices will be adopted as part of the APO s long range transportation planning process. The intent of the framework was to evaluate and report pavement conditions based on the established performance targets. The framework is comprised of five key elements. Together these elements help inform the region s system preservation needs, guide the allocation of federal funds, and help determine the most cost effective preservation investment strategy. Guidance and direction on how to use the framework is discussed below, as is the framework s methodology and objective for each of the five key elements. Table 6. Performance Measure Framework for State Roadways State Limit X% of pavements Limit X% of pavements in Fair Condition in Poor Condition Functional Class % of % of (RQI ) (RQI ) (Priority) System Miles System Target RQI Target RQI Principal Arterial (1) 28% % 31% 18.9% 4% 0.9% Minor Arterial (2) 26% % 30% 0.0% 10% 0.0% Major Collector (3) 34% NA Minor Collector (4) 12% NA

24 Page 20 Table 7. Performance Measure Framework for County Roadways County Limit X% of pavements Limit X% of pavements in in Fair Condition Poor Condition Functional Class % of % of (PQI ) (PQI ) (Priority) System Miles System Target RQI Target RQI Principal Arterial (1) 28% % 31% 80.7% 4% 2.7% Minor Arterial (2) 26% % 30% 15.5% 10% 17.2% Major Collector (3) 34% % 30% 11.4% 10% 23.7% Minor Collector (4) 12% % 30% 3.2% 10% 28.8% Table 8. Performance Measure Framework for Local Roadways Local Limit X% of pavements Limit X% of pavements in in Fair Condition Poor Condition Functional Class % of % of (PQI ) (PQI ) (Priority) System Miles System Target RQI Target RQI Principal Arterial (1) 28% NA Minor Arterial (2) 26% % 30% 40.3% 10% 17.1% Major Collector (3) 34% % 30% 41.9% 10% 37.9% Minor Collector (4) 12% % 30% 60.1% 10% 25.3% Element #1: Performance Measure Objective: The framework is built around performance measures that track pavement conditions using pavement indices. Summary: The performance measures are designed to track pavement conditions in fair and poor conditions (see Element #4: Pavement Index and Element #5: Targets). Metrics help determine if the overall system is in a state of good repair. This theme is important to recognize as it avoids a worst-

25 Page 21 first approach. Referring to Figure 7, the address the worst roads first approach (i.e., delaying work until costly reconstruction is needed) is not recommended as a satisfactory preservation strategy. For example, best practices indicate when pavement preservation/maintenance activities are delayed on roadways in good or fair condition, pavements will deteriorate more rapidly as they advance further along the performance curve. When this occurs, maintenance needs on these roadways is more substantial and subsequently more costly. Instead, to reduce cost and better maintain the roadway system long term, it is encouraged to focus on keeping good roads in good condition, while also focusing on road segments sliding into the fair condition category. The objective is to catch those roadways and apply treatments in a timely fashion, before they slide into the poor condition range, which is substantially more costly. Element #2: Jurisdiction Objective: The performance measures are aligned by jurisdictional type (i.e., State, County, and Local) and functional classification. This should help the agencies track and meet their roadways respective performance targets (see Element #5: Targets) set within the framework. Tracking by functional classification also helps demonstrate if the NHS and other MAP-21 NHS Principal Arterial routes are being maintained in a state of good repair (see Element #3: Priorities by Functional Classification). Summary: The St. Cloud APO is comprised of 13 roadway agencies, which includes the Minnesota Department of Transportation (MnDOT), three counties, and nine cities/townships. These agencies were categorized into three jurisdictional types: State (MnDOT) County (Benton, Sherburne and Stearns) Local (City of St. Cloud, City of St Joseph, City of Sartell, City of Sauk Rapids, City of St. Stephen, City of Waite Park, Minden Township, Haven Township, Le Sauk Township) The categories are linked to the appropriate performance measures, pavement ratings, and targets for that particular jurisdiction and their roadway system by functional classification. Element #3: Priorities by Functional Classification Objective: As noted, the performance measures (see Element #1: Performance Measures) were aligned by functional classification and jurisdictional type (see Element #2: Jurisdiction). The functional classification system was further defined by priority areas. This approach helps emphasize the importance in meeting MAP-21 requirements that address the preservation of NHS and other MAP-21 NHS Principal Arterial routes. Summary: The framework defines the highest priority as being the NHS system and Principal Arterials (Priority #1); Minor Arterials (Priority #2), Major Collectors (Priority #3), and Minor Collectors (Priority

26 Page 22 #4). It is important to recognize the framework is designed to only track roads functionally classified by the St. Cloud APO. The mileage for these roads are presented within the framework by jurisdictional type (see Element #2: Jurisdiction). Element #4: Pavement Index Objective: The performance measures (see Element #1: Performance Measures) are based on metrics to track targets (see Element #5: Targets) set for the St. Cloud APO. The metrics chosen for the performance measures are based on a pavement index that defines the quality of the roadway pavement (e.g., good, fair, or poor). There are varying types of pavement indices that can be used to measure pavement quality. Selecting the appropriate pavement indices for the framework was a critical step in the process and is documented throughout this section. Summary: Pavement condition is determined using a number of factors, including roughness, pavement distress, or both. A summary of the various pavement indices used by the St. Cloud APO s member jurisdictions is summarized in Section B.1.a and below. Ride Quality Index Ride Quality Index (RQI) is developed from the International Roughness Index (IRI). The IRI is an international standard that measures the expected vertical movement in inches per mile. The RQI was created by the State of Minnesota by converting IRI to a 0.0 to 5.0 rating system. This was done by utilizing data from a panel of citizens that rated over 120 sections of road and reflects how people feel when riding on a road. The RQI ranges are described in Table 9. Table 9. RQI Ranges Descriptive Category RQI Range Pavement Condition Very Good Good Good Fair Fair Poor Very Poor Poor The local St. Cloud APO jurisdictions (cities and townships) do not use ride quality to measure pavement condition. For their roadways, ride quality alone is viewed as an ineffective measure for determining pavement condition; local roads typically have a lower speed, which in turn

27 Page 23 renders ride quality not an effective measure compared to other indices. It stands that RQI is the preferred method for tracking pavement condition on NHS routes and other Principal Arterials. It is also important to recognize that Benton, Stearns, and Sherburne County collect and utilize RQI to some degree. Surface Rating Surface Rating is a pavement condition index that evaluates pavement distress by identifying the type and severity of cracks, potholes, and pavement deterioration. The cracks are visually rated by an observer. The MnDOT method for collecting data creates a Surface Rating from 0.0 to 4.0. It takes into account the depth of the cracking, or deterioration, and the amount / severity of the cracking. Nearly 94 percent of the St. Cloud APO system has some sort of pavement distress rating. However, the local jurisdictions use a variety of pavement condition indices including Overall Condition Rating (City of Sartell) and PASER (City of St. Cloud). These indices have been normalized to correspond with the Surface Rating index. Pavement Quality Index The Pavement Quality Index (PQI) is a composite index calculated by taking the square root of the product of the RQI and Surface Rating. It ranges from 0.0 to 4.5. The PQI range is described in Table 10, which is consistent with current PQI standards used by the local St. Cloud APO member jurisdictions. Table 10. PQI Ranges Descriptive Category PQI Range Pavement Condition Very Good Good Good Fair Fair Poor Very Poor Poor

28 Page 24 Selected Pavement Index The selected pavement indices used for this framework were based on current pavement monitoring processes, available data sets, and coordination amongst the St. Cloud APO jurisdictions. In that respect, the local jurisdictions (counties, cities and townships) collectively agreed upon using a PQI for tracking pavement conditions. RQI will continue to be used to track MnDOT roadways. Element #5: Targets Objective: The selected pavement indices served as a foundation for setting the performance targets. The target corresponds with the appropriate pavement index assigned to the jurisdiction. Summary: The selected targets (Table 11) are based on industry standards, MAP-21 guidance, and current targets set by MnDOT. More importantly, the targets were set to avoid a worst-first approach. The targets limit the amount of the system in poor condition, while maintaining a larger portion in fair and good condition. It is important to recognize that because the targets are focused on good, fair, and poor condition they are the same for both pavement indices. Recall that PQI is being used for local jurisdictions (i.e., counties, cities, and townships), and RQI for the State system. Table 11. Targets Functional Class Good Fair Poor Principal Arterial 65% 31% 4% Minor Arterial 60% 30% 10% Major Collector 60% 30% 10% Minor Collector 60% 30% 10% E.2. Framework Findings Currently, most of the St. Cloud APO system is not achieving the targets set for roads in poor condition (see Table 12). Referring to roads in fair condition, some County roads are below the target, while most Local roads exceed the established target (Table 12). It is important to recognize the pavement data used for this analysis is a snap-shot-in-time. Maintenance projects recently completed may not be reflected in the data received for this analysis (the margin of error is approximately less than five percent). Targets should be refined and evaluated as more data is collected over time. A summary of the targets not being met are highlighted in red in Table 12.

29 Page 25 Table 12. Framework Findings Fair Poor Functional Class (Priority) Target State County Local Target State County Local Principal Arterial (1) 31% 18.9% 80.7% N/A 4% 0.9% 2.7% N/A Minor Arterial (2) 30% 0.0% 15.5% 40.3% 10% 0.0% 17.2% 17.1% Major Collector (3) 30% N/A 11.4% 41.9% 10% N/A 23.7% 37.9% Minor Collector (4) 30% N/A 3.2% 60.1% 10% N/A 28.8% 25.3% Note: Items highlighted in red indicate performance targets not being met.

30 Page 26 F. Phase 6 Prioritized Project List and Funding Needs F.1. Methodology for Project Prioritization and Funding Need Approximately 65 miles of roads do not meet the performance targets established for this study. As previously noted, the targets are intended to serve as a benchmark and were not adopted as part of this study. Instead, indices will be adopted as part of the APO s long range transportation planning process. To achieve the benchmark targets, a list of potential projects was identified (see Appendix E) and planning-level cost estimates were prepared (Appendix F). The intent of the project list was to provide a defensible, prioritized list of transportation projects that addresses the needs of the APO area and is transparent to key stakeholders. It should be noted that the project list is based on available data and the 2015 needs. Project need will vary depending on when projects are actually performed (i.e., delaying work will likely result in more extensive repair needs). The intent of the cost estimates is to provide an order of magnitude for future investments in meeting a state of good repair. The methodology for selecting potential projects and determining funding need are described in the following five step process: Step 1: Each segment of road was assigned a pavement rating of good, fair, or poor based upon the appropriate indices (RQI or PQI) for their functional classification and jurisdiction. This was based on the pavement data provided by the jurisdictions and updated pavement data collected for this study. Step 2: Based on the pavement rating, a treatment type was assigned to roads in fair or poor condition. For the purposes of this study, a less intensive preventative repair was assigned to roads in fair condition. The intent of these repairs was to slightly improve conditions (i.e. the condition may improve from fair to the lower end of good ) and to extend the life of the pavement until a more extensive repair is needed (i.e. these repairs buy time while prolonging pavement life). A major repair was assigned to roads in poor condition. Major repairs are those that correct deficiencies and result in a renewed condition score (i.e. they improve the roadway from poor to good ). Pavements needing a major repair have typically deteriorated beyond the point of benefitting from preventive maintenance. Roads that were considered good or excellent were excluded from this assessment. Potential repair types are defined below: Preventative Repair: Fog Seal, Chip Seal, Microsurfacing, or Thin Overlay Major Repair: Mill and Overlay, Full Depth Reclamation (FDR) and Stabilized FDR, Cold In Place Recycling, or Full Reconstruction

31 Page 27 Step 3: Using typical current costs, a cost per square foot was applied to the roadway segment based on its assigned treatment type (Table 13). The average cost reflects the average cost for various treatments under that category (i.e., preventative repair or major repair). The high cost reflects the most expense fix for that treatment type. This approach provides an order of magnitude cost to achieve a state of good repair. Table 13. Planning Level Cost Assumptions Treatment Average Cost (per Sq. Ft.) High Cost (per Sq. Ft.) Preventative Repair $ 0.24 $ 0.44 Major Repair $ 2.96 $ 8.33 For sensitivity purposes, a five percent increase was applied to the cost estimates. This funding scenario helps demonstrate the funds needed to meet current performance targets, while preventing additional roads from falling into a lower pavement condition. If a five percent increase were possible, the region could do more preventative maintenance before roads require more costly fixes (e.g., reconstruction and major repairs). In essence, this scenario provides the member jurisdictions with an understanding of how much it would cost to maintain a state of good repair with the system continuing to degrade as improvements are programmed. Step 4: The cost estimates were applied to the mileage required to meet the performance targets by jurisdiction and functional classification. The cost estimates demonstrate the potential level of investment required to bring the regional system into a state of good repair. It should be noted that the costs represent the projected need as of As projects are delayed, the costs will vary. A summary of findings are described below: Principal Arterials in fair condition require approximately $586,000 (average cost) to meet the performance targets. Minor Arterials in fair condition will require approximately $211,000 (average cost) to meet the performance targets. Major Collectors in fair condition will require approximately $207,000 (average cost) to meet the performance targets. Minor Collectors in fair condition will require approximately $233,000 (average cost) to meet the performance targets. Minor Arterials in poor condition will require approximately $4,087,000 (average cost) to meet the performance targets. Major Collectors in poor condition will require approximately $12,103,000 (average cost) to meet the performance targets. Minor Collectors in poor condition will require approximately $4,505,000 (average cost) to meet the performance targets.

32 Page 28 F.2. Step 5: A list of potential projects was developed (see Appendix E) using the roadways assigned to either preventative or major repair. This list serves as a guide in selecting potential projects to help achieve the performance targets. However, it is important to recognize higher priority should be given to the principal arterial other and minor arterials. Each segment is assigned a planning-level cost estimate. Discussion of Project Prioritization and Funding Need As it relates to meeting Federal Moving Ahead for Progress in the 21st Century Act (MAP-21) requirements and implementing an APO system-wide long range pavement management plan, by knowing the overall system need, it can be determined when and if annual funding should be dedicated to maintenance of the existing system or expansion of the system. Approximately $22 million (average cost) to $60 million (high cost: worst case scenario) is needed to address today s pavement condition and meet the performance targets set in this study. Currently, the St. Cloud APO receives about $1.6 million annually in STP funds. The allocation of federal funds will not achieve all performance target needs. In order to address the target gap, strategic investments need to be made to help demonstrate efforts are being made towards a state of good repair. The functional classification priorities set within the Performance Measure Framework serve as a foundation for these decisions. For example, a stronger emphasis should be made on the NHS and MAP-21 NHS Principal Arterials routes (Priority #1). However, these routes are primarily owned and operated by MnDOT. The St. Cloud APO will continue to coordinate and collaborate with MnDOT in addressing these needs. The allocation of the St. Cloud APO s STP funds will be directed towards the local jurisdictions (counties, cities, and townships). The potential project list established with the cost estimates (Appendix E) can serve as a guide for technical evaluation of project submittals. Surface Transportation Program (STP) The STP is the most flexible of all the federal highway programs and historically one of the largest single programs. States and large metropolitan areas may use these funds for highway, bridge, transit (including intercity bus terminals), pedestrian and bicycle infrastructure projects. Under MAP- 21, new responsibilities were added to the program (e.g., funding non-nhs and federal-aid bridges) though funding was not increased proportionally. The majority of STP funds are subtargeted across Minnesota to regional Area Transportation Partnerships (ATPs), and in this case, by the St. Cloud APO. Each ATP develops its own specific allocation policies, and annually determines distributions. Typically, county roadways and reconstruction and bridge rehabilitation/replacement projects are eligible for a portion of these ATP funds and compete within the region. For systems such as the APO s, as previously discussed, effective pavement management includes maintenance activities that keep pavements in good condition. This is done through the application of

33 Page 29 timely, preventive maintenance activities, as well as through the application of timely major repairs. The overall goal is to perform these repairs before pavements deteriorate into more costly repair ranges. Figure 7 provides a typical pavement performance curve. As is shown, over time, roadways will deteriorate from good (shown in green) to fair (yellow) to poor (red). Repairs within the good and fair categories are less costly than repairs applied within the poor condition range. Figure 7 Typical Performance Curve