MPO Staff Report Technical Advisory Committee: April 8, 2015 MPO Executive Board: April 15, 2015

Transcription

1 MPO Staff Report Technical Advisory Committee: April 8, 2015 MPO Executive Board: April 15, 2015 RECOMMENDED ACTION: Approve the Final. RECOMMENDED ACTION from TAC: Accept the Final and include the NDDOT request to add text explaining how this ties-back to the planning process. TAC Discussion: There are two recommendations being presented to the Executive Board. MPO Staff s perception is that approval would be indicating that this is something that we would be utilizing in the future, while accepting would mean that we receive and file the report and it will just sit on a shelf. Grand Forks Engineering staff s made the motion adopted by TAC and their perception is the difference between approving and accepting the report is if we use the wording approval sometimes it may infer that it has a level of precision that maybe it doesn t, and accepting is a way of maybe recognizing the limitations of the tool that we have. Pavement management is a good tool, but it does have limitations. It s a tool, but it s not the tool. In general, to approve is to confirm agreement with, or to indicate reliance on, the pavement management system to assist in the planning and programming of transportation. In contrast, to accept is to recognize an obligation to pay the consultant for the work. NDDOT staff did recommend approval and did request the MPO incorporate into the documentation of the pavement management system the language from the RFP that allowed the activity to be eligible for planning funds. From the RFP: Pavement management will be used in prioritization for MPO documents (TIP and LRTP specifically) and performance measures reporting. This allows for roadway conditions to be analyzed in a more quantifiable and objective context with a variety of GIS information which will assist the Cities, DOTs and the MPO in identifying contributing factors to pavement management. Further, the Pavement Management System will assist the MPO and its member jurisdictions in budgeting and prioritizing appropriate pavement improvements.

2 Matter of Approval of the Final. Background: In February 2014 a RFP went out to do a pavement condition analysis of the pavement in Grand Forks and East Grand Forks. Goodpointe was awarded the contract was extended to March 31, 2015 to do a few budget scenarios and a report on the pavement condition in the GF-EGF MPO area. Goodpointe has run the scenarios and written the report. The report was distributed and discussed at the March TAC meeting. Based upon feedback given on that draft, Goodpointe has submitted another draft. The comments from the TAC were included. Some were small changes that made clarifications in the text of the report. There were two significant changes to the final report from the draft. 1) The inflation rate used was increased from 3% to 4% for the scenarios. This is in line with what was used in the MPO LRTP and what both Cities are using as well. See next two tables. The end result is growth in the backlog cost since it is inflating higher. 2) The inclusion of photos with the condition rating and the recommended maintenance & repair activities for the material type of the road. 3) Grand Forks Engineering suggested the PCI ratings use a differing nomenclature to present to the public (see attached memo) PCI Good = No Action PCI Satisfactory = Minor Maintenance PCI Fair = Major Rehabilitation PCI < 40 Poor = Reconstruction For this staff report, sample photos with Goodpointe s description from the report has been placed into a table with an added column to the far right to depict GF Engineering s nomenclature; this is shown below.

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5 The following is a sample of the photo table in the report with GF Engineering nomenclature. Location PCI Recommended M&R Activity (Typical) Report Verbiage GF Verbiage Cherry St Pines Circle to 47 th Ave S. 86 Routine PCC Maintenance Local repairs (crack sealing, joint seal replacement, patching) Good No Action Chestnut St Walnut to 15 th Ave S. 68 AC Preventive Maintenance Surface Treatment (seal coat, crack sealing) Satisfactory Minor Maintenance Knight Dr East End to Columbia Rd 51 PCC Maintenance Local rehab repairs (patching and slab replacement) Fair Major Rehab

6 Walnut St 28 th Ave S. to Schroeder Dr 38 PCC Maintenance Local rehab repairs (patching and slab replacement) Or PCC Reconstruction Poor Reconstruction

7 Findings and Analysis: Pavement Management Project is complete Support Materials: Final Report Memo from GF Engineering.

8 Grand Forks-East Grand Forks MPO FINAL REPORT March 23, 2015 Submitted by: GoodPointe Technology Corporation 287 E. 6 th Street, Suite 200 St. Paul, MN Direct: (651) Fax: (651) Internet: info@goodpointe.com

9 Executive Summary Studies conducted by a number of agencies indicate that as pavement conditions decrease, the cost for the appropriate method of repair can quadruple. A graph of a typical case taken from the American Public Works Association, The Hole Story, is shown in the following figure. Figure 1 A pavement management system is a set of tools used to assist with maintaining a network of roadways. Pavement management systems are a critical component of managing and maintaining transportation infrastructure. Agencies must address transportation needs with limited resources, while legislative bodies are requiring more efficiency in highway agencies and more accountability for the expenditure of taxpayers money. As a result, the importance of a management system to help with the allocation of these resources, to manage the infrastructure, becomes more critical than ever. In a time of decreasing budgets and increasing demands, a pavement management system can be a strategic tool to make cost effective decisions on your pavement. Pavement management systems can help identify and prioritize maintenance and rehabilitation needs. Pavement management systems can be used to analyze different pavement treatments to determine their cost savings over time. Agencies can use pavement management systems to provide information to the public and agency council/board to gain support and secure project funding Grand Forks-East Grand Forks MPO contracted GoodPointe Technology to perform pavement management consultation services, which included a pavement surface data collection survey (in 2013) and PCI extraction project (in 2014) to determine the surface condition of selected roadway and pavements under the jurisdiction of the City of Grand Forks, City of East Grand Forks, NDDOT and MNDOT for use in pavement project development and budgeting for future MPO planning related efforts- both Transportation Improvement Program and Long Range Transportation Plan related activities. This allows for roadway conditions to be analyzed in a more quantifiable and objective context with a variety of GIS information which may assist in identifying contributing factors to pavement deterioration. Further, the analysis is the basis for pavement conditions for a Pavement Management System that assist the MPO and its GoodPointe Technology, Inc. Page 1 of 29

10 member jurisdictions in budgeting and prioritizing appropriate pavement improvements that are proposed to be programmed into the Transportation Improvement Program. The Grand Forks street network consists of approximately centerline-miles of roadways. Of this total: miles are Portland Cement Concrete (PCC); 97.3 miles are Asphaltic Concrete (AC); 4.6 miles are gravel surfaced, and 1.1 miles are Brick/Granitoid pavement. The roadway pavements of each City were visually inspected using the ASTM Pavement Condition Index (PCI) methodology. The PCI is a methodology used to evaluate and rate pavements on a range of 100 (newly surfaced pavement) to 0 (failed pavement). The overall average PCI of the Grand Forks Network is 74; the average PCI of PCC pavements is 80; the average PCI of AC pavements is 63. The East Grand Forks street network consists of approximately 67.7 centerline-miles of roadways. Of this total: 44.1 miles are Portland Cement Concrete (PCC); 20.5 miles are Asphaltic Concrete (AC), and 3.2 miles are gravel surfaced. The overall average PCI of the East Grand Forks Network is 85; the average PCI of PCC pavements is 89; the average PCI of AC pavements is 76. Three multi-year budget analysis scenarios were run for each City network, the results of which are presented in this report. Based on the results of this analysis, the projected budget needs of each network are shown to be greater than what is currently being invested for each City. Project Procedures Digital Imaging Inventory Survey of ROW Features In the summer of 2013, all designated roadway routes under the jurisdiction of each City were driven and imaged by one of our specially equipped data collection vehicles in both directions of travel. All imagery was captured with multiple full-frame progressive scan digital color cameras. The cameras are high-resolution digital cameras, which take snapshots along the designated route, leaving intelligent-, geo-positioned images with accurately recorded six degrees of freedom information positioned along the roadway. GoodPointe Technology, Inc. Page 2 of 29

11 Pavement Inventory and Condition Survey GoodPointe Technology used the most current set of available inventory data from available City sources to perform the field survey of physical pavement features (e.g. pavement surface type, geometrics, etc.). Using the collected images, GoodPointe validated the information and any incorrect and/or missing inventory data was updated in the ICON database accordingly. In 2014 GoodPointe was contracted to use the 2013 digital image data to facilitate a detailed pavement condition survey, in which the various pavement distresses were digitally measured from the images collected in the field. GoodPointe Technology, Inc. Page 3 of 29

12 A condition survey sample box was laid down based on the PCI sampling methodology upon each PMS inventory section and then the individual pavement distresses within the sample box were identified and measured (e.g. % area of sample, etc.). Digital image from the Extraction Software, showing sample box width, distress list, map of distress locations and attributes for each pavement condition feature re-projected on the perspective views. The PCI survey distress data was recorded based upon actual surface conditions and physical characteristics of each pavement inventory unit defined in this project. All digital image data collected in the field was automatically geo-referenced and stored in onboard computer hardware, for eventual transfer into the ICON Infrastructure Management system database systems managed by the Cities of Grand Forks and East Grand Forks, which are hosted by GoodPointe on our cloud server. Definition of Pavement Sampling Methods The pavement sampling procedures generally followed the standard PCI sampling guidelines outlined in ASTM PCI Standard D : Given Evaluate 1 to 5 Sample Units 1 Sample Unit 6 to 10 Sample Units 2 Sample Units 11 to 15 Sample Units 3 Sample Units 16 to 40 Sample Units 4 Sample Units Over 40 Sample Units 10% of the Sample Units Where a pavement sample unit is defined as 2,500 square feet +/- 1,000 square feet; the following lists of distresses were evaluated in this project: GoodPointe Technology, Inc. Page 4 of 29

13 Asphaltic Concrete (AC) Bituminous Surfaced Pavements Distress ASTM PCI Severity Measurement Units # Distress # and Name Levels 1 1. Alligator Cracking L, M, H Area (square feet) 2 2. Bleeding L, M, H Area (square feet) 3 3. Block Cracking L, M, H Area (square feet) 4 7. Edge Cracking L, M, H Linear (feet) 5 8. Joint Reflection Cracking L, M, H Linear (feet) Longitudinal/Transverse Cracking L, M, H Linear (feet) Patching and Utility Cut Patching L, M, H Area (square feet) Potholes L, M, H Count Raveling L, M, H Area (square feet) Rutting L, M, H Area (square feet) Portland Cement Concrete (PCC) Surfaced Pavements Distress # ASTM PCI Severity Measurement Units Distress # and Name Levels Blow-Ups L, M, H Number of Slabs Corner Break L, M, H Number of Slabs Durability ( D ) Cracking L, M, H Number of Slabs Divided Slab L, M, H Number of Slabs Faulting L, M, H Number of Slabs Joint Seal Damage L, M, H Overall Rating Linear Cracking L, M, H Number of Slabs Patching, Large, & Utility Cuts L, M, H Number of Slabs Patching, Small L, M, H Number of Slabs Popouts No Severity Number of Slabs Punchouts L, M, H Number of Slabs Railroad Crossing L, M, H Number of Slabs Scaling/Map Cracking/Crazing L, M, H Number of Slabs Spalling, Joint L, M, H Number of Slabs PCI Survey Sample Images The following images depict typical conditions and recommended maintenance and repair (M&R) activities for Asphaltic Concrete (AC) and Portland Cement Concrete (PCC) pavements. GoodPointe Technology, Inc. Page 5 of 29

14 Location PCI Recommended M&R Activity (Typical) 55 th St N. 1 st Ave N. to University Ave 100 Do Nothing Alpha St 6 th St N. to Red Dot Pl 92 Routine PCC Local Maintenance University Ave S. Ent to 55 th St N. 90 AC Preventive Maintenance Surface Treatment (seal coat, crack sealing) Cherry St Pines Circle to 47 th Ave S. 86 Routine PCC Local Maintenance GoodPointe Technology, Inc. Page 6 of 29

15 Pinehurst Dr Columbia Rd to Pavement Change 86 AC Preventive Maintenance (crack sealing) Cherry St Central Ct to Great Plains Ct 83 Routine PCC Maintenance Local repairs (crack sealing, joint seal replacement, patching) Chestnut St 55 th Ave S. to 52 nd Ave S. 79 Routine PCC Maintenance Local repairs (crack sealing, joint seal replacement, patching) Cherry St 28 th Ave S. to 25 th Ave S. 77 Routine PCC Maintenance Local repairs (crack sealing, joint seal replacement, patching) GoodPointe Technology, Inc. Page 7 of 29

16 Cherry St 41 s t Ave S. to Great Plains Ct 72 Routine PCC Maintenance Local repairs (crack sealing, joint seal replacement, patching) Chestnut St Walnut to 15 th Ave S. 68 AC Preventive Maintenance Surface Treatment (seal coat, crack sealing) Library Cir Library Cir S to 20th 60 Routine PCC Maintenance Local repairs (crack sealing, joint seal replacement, patching) Knight Dr East End to Columbia Rd 51 PCC Maintenance Local rehab repairs (slab replacement) GoodPointe Technology, Inc. Page 8 of 29

17 Euclid Plum to Pakenham Ave 49 PCC Maintenance Local rehab repairs (patching and slab replacement) Chestnut St 24 th Ave S. to 23 rd Ave S. ` 43 AC Major Repair Overlay, Mill & Overlay Walnut St 28 th Ave S. to Schroeder Dr 38 PCC Maintenance Local rehab repairs (patching and slab replacement) Or PCC Reconstruction Chestnut St 22 nd to 23 rd Ave 38 AC Major Repair Overlay, Mill & Overlay GoodPointe Technology, Inc. Page 9 of 29

18 Lanark 10 th Ave S to Euclid 22 PCC Reconstruction GoodPointe Technology, Inc. Page 10 of 29

19 Project Results Grand Forks Inventory Summary Grand Forks Network Functional Class (Centerline Miles) Local Collector Arterial Grand Forks Network Functional Class (% Area) Local 55% Collector 16% Arterial 29% 0% 10% 20% 30% 40% 50% 60% Grand Forks Network Functional Class (Millions of Square Feet) Local Collector Arterial GoodPointe Technology, Inc. Page 11 of 29

20 PCC Gravel Brick Paver AC Grand Forks Network Pavement Surface Type (Centerline Miles) AC Brick Paver Gravel PCC PCC Gravel Brick Paver AC Grand Forks Network Pavement Surface Type (% Area) 2% 0.4% 34% 64% 0% 10% 20% 30% 40% 50% 60% 70% AC Brick Paver Gravel PCC Grand Forks Network Pavement Surface Type (Millions of Square Feet) PCC Gravel Brick Paver AC AC Brick Paver Gravel PCC GoodPointe Technology, Inc. Page 12 of 29

21 Grand Forks Condition Summary Grand Forks Network Average Condition By Surface Type AC PCC Grand Forks Average PCI by Functional Class Arterial Collector Local GoodPointe Technology, Inc. Page 13 of 29

22 Grand Forks PCI Distribution Street Blocks (Sections) AC PCC Grand Forks PCI Distribution Centerline Miles AC PCC Grand Forks PCI Distribution % Area AC PCC GoodPointe Technology, Inc. Page 14 of 29

23 MPO PCI Rating Scale (% by Area) Grand Forks Network 3% 41% 20% 36% 0-40 Poor Fair Satisfactory Good MPO PCI Rating Scale (Centerline Miles) Grand Forks Network Poor Fair Satisfactory Good MPO PCI Rating Scale (Millions of Square Feet) Grand Forks Network Poor Fair Satisfactory Good GoodPointe Technology, Inc. Page 15 of 29

24 East Grand Forks Inventory Summary East Grand Forks Network Functional Class (Centerline Miles) Principal Arterial - State Aid Principal Arterial Minor Arterial - State Aid Minor Arterial Collector - State Aid Collector Local - State Aid Local Principal Arterial - State Aid Principal Arterial Minor Arterial - State Aid East Grand Forks Network Functional Class (% Area) Minor Arterial Collector - State Aid Collector Local - State Aid Local 4% 11% 6% 4% 11% 3% 4% 55% 0% 10% 20% 30% 40% 50% 60% East Grand Forks Network Functional Class (Millions of Square Feet) Principal Arterial - State Aid Principal Arterial Minor Arterial - State Aid Minor Arterial Collector - State Aid Collector Local - State Aid Local GoodPointe Technology, Inc. Page 16 of 29

25 East Grand Forks Network Pavement Surface Type (Centerline Miles) Gravel 3.2 PCC 44.0 AC AC PCC Gravel East Grand Forks Network Pavement Surface Type (% Area) Gravel 4% PCC 67% AC 30% 0% 10% 20% 30% 40% 50% 60% 70% AC PCC Gravel East Grand Forks Network Pavement Surface Type (Millions of Square Feet) Gravel PCC AC AC PCC Gravel GoodPointe Technology, Inc. Page 17 of 29

26 East Grand Forks Condition Summary East Grand Forks Network Average Condition By Surface Type AC PCC East Grand Forks Network Average PCI By Functional Class Principal Arterial - State Aid Principal Arterial Minor Arterial - State Aid Minor Arterial Collector - State Aid Collector Local - State Aid Local GoodPointe Technology, Inc. Page 18 of 29

27 East Grand Forks PCI Distribution Street Blocks (Sections) AC PCC East Grand Forks PCI Distribution Centerline Miles AC PCC East Grand Forks PCI Distribution % Area AC PCC GoodPointe Technology, Inc. Page 19 of 29

28 MPO PCI Rating Scale (% by Area) East Grand Forks Network 1% 6% 66% 27% Poor Fair Satisfactory Good MPO PCI Rating Scale (Centerline Miles) East Grand Forks Network Poor Fair Satisfactory Good MPO PCI Rating Scale (Millions of Square Feet) East Grand Forks Network Poor Fair Satisfactory Good GoodPointe Technology, Inc. Page 20 of 29

29 Infrastructure Investment Analysis After updating the inventory and condition data, an investment analysis was prepared. The goal of this analysis was to define the optimal level of investment needed to meet desired city infrastructure performance. As part of this analysis, GoodPointe coordinate with the MPO and designated engineering staff from each City to identify performance targets (i.e., standard infrastructure service life, maintenance activities/frequency, etc.). Cost estimates for future capital and maintenance investments were then developed in accordance with these performance targets, and used to prepare alternative investment scenarios. For each of the infrastructure categories included three investment scenarios were developed in order to analyze the potential impacts and implications over time. These investment scenarios generally included the following: 1. Current Funding Scenario Reflects each city s current approach to infrastructure investments, using current funding levels to establish a baseline for comparison. 2. Current Funding with 1.5% Budget Increase Scenario improves upon the current funding scenario by increasing the budget 1.5% per year for the next five (5) years; and, 3. Maintain Current Condition Scenario Determines the level of investment required to maintain the current condition of the roadway network for the next five (5) years. For the budget input type of scenarios (1 and 2), the estimated revenue used for modeling future capital and maintenance investments come from federal, state, and local sources. The scenarios compiled for this report were primarily based upon the pavement condition index (PCI) data that was collected for this project. Any roadways that were under construction at the time of the condition survey project or that were re-paved after the condition survey, were updated with an improved PCI rating before performing this analysis. Parameters Used in the Analysis For each scenario: Analysis Period: Five (5) Years ( ) Inflation Factor: 4.0% Budget: investment level that is required to achieve a stated scenario objective. Backlog: calculated cost to fix all the deferred maintenance work in the given year of an analysis period. Backlog: Budget (Ba:Bu) Ratio: the numerical ratio of Budget divided by Backlog; for every $1.00 of budget investment, the Ba:Bu indicates how many dollars of projects are deferred due to a lack of funding. GoodPointe Technology, Inc. Page 21 of 29

30 Grand Forks Tabular Scenario Results Scenario 1: Current Funding Year Budget ($K) Backlog ($K) Backlog: Budget Average CI 2016 $16,945 $ 76, $16,945 $ 78, $16,945 $ 75, $16,945 $ 71, $16,945 $ 71, Average: $16,945 $ 74, Total: $84,726 Scenario 2: 1.5% Budget Increase Year Budget ($K) Backlog ($K) Backlog: Budget Average CI 2016 $ 16,945 $ 76, $ 17,199 $ 77, $ 17,457 $ 74, $ 17,719 $ 69, $ 17,985 $ 69, Average: $ 17,461 $ 73, Total: $ 87,306 Scenario 3: Maintain Current PCI Year Budget ($K) Backlog ($K) Backlog: Budget Average CI 2016 $ 26,345 $ 70, $ 23,913 $ 72, $ 23,549 $ 74, $ 22,639 $ 75, $ 23,173 $ 78, Average: $ 23,924 $ 74, Total: $119,619 GoodPointe Technology, Inc. Page 22 of 29

31 Grand Forks Scenario Results Budget Investment Level $30,000 $25,000 $20,000 $15,000 $10,000 $5,000 City of Grand Forks Budget Investment Level ($K) $ Scenario 1 Scenario 2 Scenario 3 City of Grand Forks Budget Investment Level ($K) $30,000 $25,000 $20,000 $15,000 $10,000 $5,000 $ Scenario 1 Scenario 2 Scenario 3 GoodPointe Technology, Inc. Page 23 of 29

32 Maintenance Backlog City of Grand Forks Maintenance Backlog ($K) $80,000 $70,000 $60,000 $50,000 $40,000 $30,000 $20,000 $10,000 $ Scenario 1 Scenario 2 Scenario 3 City of Grand Forks Maintenance Backlog ($K) $80,000 $70,000 $60,000 $50,000 $40,000 $30,000 $20,000 $10,000 $ Scenario 1 Scenario 2 Scenario 3 GoodPointe Technology, Inc. Page 24 of 29

33 Budget vs. Backlog $80,000 $70,000 $60,000 $50,000 $40,000 $30,000 $20,000 $10,000 City of Grand Forks Maintenance Backlog ($K) $ Scenario 1 Backlog Scenario 2 Backlog Scenario 3 Backlog Scenario 1 Budget Scenario 2 Budget Scenario 3 Budget GoodPointe Technology, Inc. Page 25 of 29

34 East Grand Forks Tabular Scenario Results Scenario 1: Current Funding Year Budget ($K) Backlog ($K) Backlog: Budget Average CI 2016 $ 300 $ 3, $ 300 $ 5, $ 300 $ 6, $ 300 $ 8, $ 300 $ 9, Average: $ 300 $ 6, Total: $1,500 Scenario 2: 1.5% Budget Increase Year Budget ($K) Backlog ($K) Backlog: Budget Average CI 2016 $ 300 $ 3, $ 304 $ 5, $ 309 $ 6, $ 314 $ 8, $ 318 $ 9, Average: $ 309 $ 6, Total: $1,546 Scenario 3: Maintain Current PCI Year Budget ($K) Backlog ($K) Backlog: Budget Average CI 2016 $ 1,646 $ 2, $ 1,434 $ 2, $ 1,508 $ 2, $ 1,662 $ 3, $ 1,345 $ 3, Average: $ 1,519 $ 2, Total: $ 7,595 GoodPointe Technology, Inc. Page 26 of 29

35 East Grand Forks Scenario Results Budget Investment Level City of East Grand Forks Budget Investment Level ($K) $2,000 $1,800 $1,600 $1,400 $1,200 $1,000 $800 $600 $400 $200 $ Scenario 1 Scenario 2 Scenario 3 City of East Grand Forks Budget Investment Level ($K) $2,000 $1,800 $1,600 $1,400 $1,200 $1,000 $800 $600 $400 $200 $ Scenario 1 Scenario 2 Scenario 3 GoodPointe Technology, Inc. Page 27 of 29

36 Maintenance Backlog City of East Grand Forks Maintenance Backlog ($K) $10,000 $9,000 $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 $ Scenario 1 Scenario 2 Scenario 3 City of East Grand Forks Maintenance Backlog ($K) $10,000 $9,000 $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 $ Scenario 1 Scenario 2 Scenario 3 GoodPointe Technology, Inc. Page 28 of 29

37 Budget vs, Backlog $10,000 $9,000 $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 City of East Grand Forks Budget vs. Backlog ($K) $ Scenario 1 Backlog Scenario 2 Backlog Scenario 3 Backlog Scenario 1 Budget Scenario 2 Budget Scenario 3 Budget GoodPointe Technology, Inc. Page 29 of 29

38 Use in MPO Planning and Programming Now that this updated data set has been completed, the MPO and its member jurisdictions can begin to utilize the system as a strategic tool to make cost effective decisions on your pavement. The pavement management system can help identify and prioritize maintenance and rehabilitation needs. It can be used to analyze different pavement treatments to determine their cost savings over time. The MPO has use it to provide information to the public and Executive Board to gain support and secure project funding. An example from the 2040 Long Range Transportation shows how this system is used during the planning process: Goal 7: Performance Measures and Monitoring Activities Performance Measures Performance Targets Condition of pavement. Reduce percentage of pavements rated in poor condition. Pavement Quality With new federal and state policies, MPO plans are required to give greater emphasis to preserving and maintaining existing transportation infrastructure. A large part of this focus is a result of aging pavements, rising costs of materials, and stagnant or declining funds. Importantly, the GF/EGF MPO s updated goals, objectives and performance measures emphasize the need to preserve and maintain existing transportation systems. The study s pavement analysis utilized data from pavement management software that is maintained by the GF/EGF MPO and local agencies. The different pavement conditions assigned to the roads fall into five categories and contain a rating system known as a Pavement Condition Index (PCI): Good condition Satisfactory condition Fair conditions. Poor condition Very poor condition The most recent pavement condition data available for the GF/EGF MPO metro area is for the year This assessment also looked at data available for 1999 and During the course of this time period, pavement conditions improved between 1999 and 2003 and then declined between 2003 and 2008 (see Table 7), however the weighted average PCI improved between 2003 and GoodPointe Technology, Inc. Page 30 of 29

39 Table 7: GF/EGF MPO Pavement Trends Year Average Pavement Condition Index (PCI) Weighted Average PCI City Grand Forks East Grand Forks Grand Forks East Grand Forks NA NA Source: GF/EGF MPO 2008 Note: The average is the sum of the PCI for all segments divided by the number of segments. The weighted average is calculated by multiplying each value by the weight that it holds, adding them up, and then dividing by the sum of the weights. The spike in improved pavement conditions after 1999 can be contributed to roadway investments after the major 1997 flood. However, if the decline in PCI continues (as it has between 2003 and 2008), significant policy decisions and alternatives investment allocations by state and local government leaders will need to be made to ensure that these roadways maintain a state of good repair. Table 8 summarizes the current pavement conditions (2008) for the GF/EGF MPO area. Table 8: GF/EGF MPO Pavement Conditions (2008) Pavement Condition GF/EGF MPO Area MN-side ND-side Local Roads State Roads Local Roads State Roads Local Roads State Roads Good 7% 25% 20% 32% 4% 23% Satisfactory 8% 17% 18% 11% 6% 18% Fair 4% 7% 3% 0% 4% 9% Poor 3% 3% 3% 2% 3% 4% Very Poor 2% 13% 0% 0% 3% 16% No Data 5% 5% 9% 3% 4% 6% Source: GF/EGF MPO 2008 MAP-21 requires special consideration for the preservation of National Highway System. Based on the data presented in Table 8, it should be noted that 42 percent of the total state system is considered to be in good or satisfactory pavement conditions. It is also important to note that nearly 16 percent of the State DOT s pavement is considered to be in poor or very poor condition and when a roadway system falls into these categories, it is often too deteriorated for rehabilitation and requires a costly total reconstruction, while segments within the fair category can be rehabilitated at a lower GoodPointe Technology, Inc. Page 31 of 29

40 cost. Therefore, it is important to maintain the number of miles in the good condition and to ensure mileage in the fair condition category do not drop into poor condition, which will necessitate greater taxpayer expenditures. An example from the MPO Transportation Improvement Program Project Scoring Criteria shows how this system is used during the programming process: GoodPointe Technology, Inc. Page 32 of 29