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Evaluation of Highway Performance Measures for a Multi-Study Corridor - A Pilot Study

3.0 Data Collection and Integration

This section details the results of the data collection effort described in Section 2. Observations regarding the structure and content of the pavement and bridge data are presented. This section also contains information on how these data have been integrated into the ICAT repository.

3.1 Data Collection Results

CS completed the data collection process, described in Section 2, on Wednesday, December 9, 2009. CS received the following data:

  • Virginia:
    • Bridge Data: a Pontis database containing National Bridge Inventory (NBI) and element data for all Virginia bridges. This information was collected by Virginia primarily in 2009.
    • Pavement Data: a Microsoft Access database containing pavement seg-ments (identification, location, condition) for all Virginia interstates. This information was collected by Virginia primarily in 2008.
  • Maryland:
    • Bridge Data: a Microsoft Excel spreadsheet containing element data for bridges on I-95 maintained by the Maryland State Highway Administration. This information was collected by Maryland primarily in 2008. (Note: additional bridges on I-95 owned by the Maryland Turnpike Authority were not provided.)
    • Pavement Data: a Microsoft Excel spreadsheet containing pavement segments (identification, location, condition) for I-95 in Maryland. This information was collected by Maryland primarily in 2007. Due to a systemic issue with the 2008 pavement data, Maryland elected to provide 2007 data.
  • Delaware:
    • Bridge Data: a Pontis database containing NBI and element data for all Delaware bridges. This information was collected by Delaware primarily in 2008.
    • Pavement Data: a Microsoft Excel spreadsheet containing pavement segments (identification, location, condition) for state-managed roads and highways in Delaware. This information was collected by Delaware primarily in 2008.
  • Other:
    • NBI data for all Maryland bridges (FHWA web site);
    • NBI and element data for all District of Columbia bridges (Pontis database); and
    • Highway Performance Monitoring System (HPMS) data for Virginia, Maryland, and Delaware (FHWA web site).

3.2 Data Structure and Content

Bridge Data

Virginia and Delaware bridge data were provided as Pontis databases. The Delaware bridges are contained in a Sybase Adaptive Server Anywhere database file while the Virginia bridges were delivered in an Oracle database export file, which CS has loaded into an Oracle database. These Pontis databases contain NBI and element data, stored in a hierarchical collection of tables, for all bridges in their respective states. The Virginia database contains 22,454 bridges of which 363 are on I-95. The Delaware database contains 1,606 bridges of which 59 are on I-95.

Maryland bridge data were provided in a Microsoft Excel spreadsheet. This spreadsheet contained the following items:

  • Structure Number (NBI Item #8);
  • Record Type (NBI Item #5A);
  • Route Number (NBI Item #5D);
  • Inspection Date (NBI Item #90);
  • Element ID;
  • Element Total Quantity;
  • Element Quantity in Condition State 1;
  • Element Quantity in Condition State 2;
  • Element Quantity in Condition State 3;
  • Element Quantity in Condition State 4; and
  • Element Quantity in Condition State 5.

The spreadsheet includes one record for each combination of Structure Number and Element ID. A total of 2,269 records were provided. These records represent 118 structures. All of these structures are "on" (Record Type = 1) I-95. In addition, Maryland provided a document, the Pontis Element Data Collection Manual prepared by the Maryland Department of Transportation State Highway Administration Office of Bridge Development, revised July 2003, which describes the State's elements, condition state definitions, and available actions.

In order to facilitate calculation of values such as health index, CS converted the Maryland bridge information into a Pontis database. CS began by loading the Maryland NBI data for all bridges into a blank Pontis database. CS then used a manual process to create a single element inspection based on the spreadsheet information for each of the 118 I-95 bridges. Most of the elements received from Maryland exactly matched Commonly Recognized (CoRe) elements available in Pontis. However, Maryland has defined 15 elements that are not in Pontis. CS created custom elements in Pontis to accommodate these records.

In addition to the bridge data provided by Virginia, Maryland, and Delaware, CS reviewed bridges for the District of Columbia. The source for these bridges was a Pontis database last updated during the summer of 2008, which already was available to CS. After this review, it was determined that the D.C. Pontis database did not contain any bridges on I-95.

Pavement Data

Virginia provided pavement information in a Microsoft Access 2000 database. This database contains three tables for different types of pavement, including Asphalt Concrete Pavement (ACP), Continuously Reinforced Concrete Pavement (CRCP), and Jointed Reinforced Concrete Pavement (JRCP). These tables include common columns that identify the pavement segment location, physical characteristics, and inspection information. The only condition measures included in these standard columns are International Roughness Index (IRI) left, right, and average, and Critical Condition Index (CCI).

In addition to IRI and CCI, each table contains condition measures unique to the pavement type. For example, the ACP table includes these additional measures:

  • Transverse Cracking Severity 1;
  • Transverse Cracking Severity 2;
  • Longitudinal Cracking Severity 1;
  • Longitudinal Cracking Severity 2;
  • Longitudinal Lane Joint Severity 1;
  • Longitudinal Lane Joint Severity 2;
  • Reflective Transverse Cracking Severity 1;
  • Reflective Transverse Cracking Severity 2;
  • Reflective Transverse Cracking Severity 3;
  • Reflective Longitudinal Cracking Severity 1;
  • Reflective Longitudinal Cracking Severity 2;
  • Reflective Longitudinal Cracking Severity 3;
  • Alligator Cracking Severity 1;
  • Alligator Cracking Severity 2;
  • Alligator Cracking Severity 3;
  • Patching Area - wheel path;
  • Patching Area - nonwheel path;
  • Pothole Count;
  • Delamination Area;
  • Bleeding Severity 1;
  • Bleeding Severity 2;
  • Average Deeper Rut (straight-edge);
  • Average Deeper Rut (wire method);
  • Load Distress Rating; and
  • Nonload Distress Rating (Note: Load and Nonload Distress Ratings are calculated values and CCI always is the "worst" of these two ratings).

Combined, the three tables in the database appear to contain road segments for all interstates in Virginia. After reviewing these data, CS determined that 192 segments for I-95 were found only in the ACP table. These segments range in length from 0.15 to 9.83 miles with an average length of 1.935 miles. There are no I-95 segments for pavement types CRCP and JRCP. Consistent with our project strategy, the CS analysis used only the I-95 segments from the ACP table.

Maryland provided pavement information in a Microsoft Excel spreadsheet. The spreadsheet contains one workbook holding one-tenth-mile sections and a second workbook holding one-half-mile sections. Both workbooks contain data only for I-95 northbound and southbound. The workbooks are identical in structure and include segment identification and location information and the following measures:

  • IRI;
  • Rutting Depth;
  • Count of Rut Depth > 0.5 inches;
  • Cracking Index;
  • Friction Number;
  • IRI Condition (0, 1-5);
  • Rut Condition (0, 1-5);
  • Crack Condition (0, 1-5); and
  • Friction Condition (0, 1-5).

After reviewing the Maryland pavement data, CS determined that the analysis would focus on 2,179 one-tenth-mile segments for I-95 and the one-half-mile data would not be used.

Delaware provided pavement information in a Microsoft Excel spreadsheet. The spreadsheet appears to contain data for all state-managed roads and highways in Delaware. In addition to route identification and begin/end mile points, the spreadsheet contains the following measures:

  • IRI - left and right; and
  • Overall Pavement Condition (OPC).

After reviewing all pavement data provided by Delaware, CS determined that 193 segments for I-95 would be used in the analysis. These segments range in length from 0.01 to 1.01 miles with an average length of 0.195 miles.

In addition, CS obtained 2008 HPMS information for Virginia, Maryland, and Delaware. CS compared the HPMS data with the pavement data received from each state. CS did not find any significant gaps in the state data and did not include any of the HPMS information in the analysis.

3.3 Data Issues

CS conducted a review of all the information received from Virginia, Maryland, and Delaware. For the bridge data, no significant issues were uncovered. Bridge inspection and condition reporting has been heavily influenced by the FHWA through the creation of the NBI and by the American Association of State Highway and Transportation Officials (AASHTO) through the creation of Pontis. These standards, and the processes that support them, have resulted in a high degree of uniformity in bridge information available from states.

Uniform bridge data does mean that there are, potentially, fewer avenues to explore as part of the data analysis. Condition ratings, Sufficiency Rating and Health Index continue to be the leading candidates for evaluating the physical condition of a bridge.

For the pavement data, however, there was much less consistency. Currently, IRI is the only condition measure captured by all participating states. As defined in the HPMS Reassessment 2010+, states already should be collecting new data elements and will begin reporting these elements in June 2010. This will provide a larger universe of common information from which to judge pavement structure and condition.

The amount and completeness of pavement data available from a state depends heavily on the inspection process. States with automated inspection capability tend to provide more complete and comprehensive data, which on the surface appears to be more accurate. Inspections that are primarily manual tend to capture fewer data elements. And the very nature of these differing inspection processes virtually guarantees that each state will measure the same value in different ways.

Based on the data alone, it is difficult to determine which inspection process better represents the pavement structure and condition. As shown in Section 4, CS analyzed and compared available data both within a state and across states in an effort to categorize the information and recommend options to FHWA regarding performance measures to support corridor-wide decision-making.

3.4 ICAT Integration

There were two key steps to preparing data for integration into the database for the Integrated Corridor Analysis Tool (ICAT). First, common data structures were defined for bridge and pavement information. Second, CS ensured that sufficient data elements existed to match bridge and pavement records to the existing ICAT network.

For both bridge and pavement data, CS defined data structures in the ICAT repository. For bridges, CS extended the existing NBI record structure with columns that hold other measures such as Health Index (overall and by bridge component). For pavement, CS defined a custom data structure that is a superset of the elements received from Virginia, Maryland, and Delaware.

Both the bridge and the pavement data in ICAT were updated to include com-puted metrics defined in Task 3. These metrics were placed in columns added to the bridge and pavement tables described above. CS reviewed both the measures and the metrics and selected a subset of them to display within the ICAT web interface (WebCAT). Refer to Section 5 for additional information on this interface and how it can be used to display bridge and pavement performance measures.

CS reviewed the bridge and pavement data and confirmed that sufficient information existed to place these structures on the ICAT road network. For bridges, a combination of NBI Item 5 - Inventory Route, Item 11 - Kilometer Point, Item 16 - Latitude, and Item 17 - Longitude were used to “snap” the bridge to the closest point on the identified route.

For pavement, sufficient values were received from each state to reproduce the linear referencing system (LRS) key that was incorporated into the ICAT network when it was built from original state road information. This LRS key, in combination with the beginning and ending mile point, was used to “overlay” the pavement data onto an ICAT route. For some states, CS also had to adjust the mile points to ensure that these values represented a continuous range across the state. For example, the Virginia pavement data reset the mile point to zero at each county boundary. This was not consistent with the mile points used by ICAT.

3.5 Data Analysis

Task 3 focused on a comprehensive analysis of the bridge and pavement data collected for this project, including reporting information on how the states perform their inspections and control the quality of their data. CS applied a variety of statistical functions to evaluate the quality, consistency, and completeness of the data. Using this information, CS defined candidate performance measures that summarize the detailed bridge and pavement data. CS also performed calculations and transformations to compute other measures using the available data. Refer to Section 4 for the results of this analysis.

In Task 4, CS developed the functionality to display bridge and pavement data and performance measures in WebCAT. Refer to Section 5 for design information showing how the WebCAT web interface allows users to select one or more candidate measures and display categorized pavement and bridge data in Virginia, Maryland, and Delaware.

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Updated: 06/27/2017
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