Technology Choices for Data Collection and Condition Assessment Leonard (Len) Schultz Transportation Engineer Manager Highway Maintenance Division Maryland State Highway Administration

ASSET MANAGEMENT DATA COLLECTION GUIDE DRAFT DOCUMENT Version: June 2004 Prepared by: J. W. Bryant, Jr., Ph. D., P. E., Virginia Transportation Research Council C. D. Larson, P. E., PMP, Virginia Department of Transportation

Asset Management Primer

ASSET MANAGEMENT SYSTEM (page 19)

Maintenance Management Systems

A. Module 1 – Planning (page 20) A. Module 1 – Planning (page 20)  A sset Inventory  M aintenance Activity Guidelines  C ustomer Input  P erformance Targets  C ondition Assessments

ASSET MANAGEMENT DATA COLLECTION GUIDE DRAFT DOCUMENT Version: June 2004 Prepared by: J. W. Bryant, Jr., Ph. D., P. E., Virginia Transportation Research Council C. D. Larson, P. E., PMP, Virginia Department of Transportation

In general the asset data collection requirements can be categorized into the following: (1) Location; (2) Physical Attributes; and (3) Condition. Locations are usually denoted by a “from – to” county-route-mile (CRM) for linear assets. Nonlinear assets are point specific and are denoted by either a single CRM or by use of landmark data. In all cases GPS coordinates and or physical landmarks can be used to acquire the location information for both linear and nonlinear assets. The physical attributes collected will vary from asset to asset. Physical attributes are used to describe the asset in question. General attributes that are consistent across assets include: material type, size, and length. Condition assessment is depended on the specified performance criteria for the asset. Data for condition assessment can be broad for some assets requiring only Good, Bad or fair, while other assets may require a more detailed approach set forth by national or regional accepted practices or standards. Table 2-1 presents the basic inventory attributes for transportation assets. Condition attributes vary greatly from asset to asset as to how they are reported; therefore they were excluded from Table 2-1.

General assets groups and associated asset types   Pavements: Flexible Pavements (HMA), PCC Pavements, Unpaved Roads; Paved Shoulders, and Unpaved Shoulders   Roadsides: Vegetation and aesthetics, Trees, Shrubs and brush, Historic Markers, and Right-of-way Fence   Drainage Structures: Cross Pipes and Box Culverts, Entrance Pipes, Curb & Gutter, Paved Ditches, Unpaved Ditches, Edge Drains and Under-drains, Storm Water Ponds, and Drop Inlets   Traffic: Attenuators, Guardrail, Pavement striping, Pavement markings, Raised pavement markers, Delineators, Signs, and Highway Lighting   Structures and Bridges: Overhead Sign Structures, Structural Culverts, Overall Bridge, Sound Barriers, and Retaining Walls   Special Facilities: Movable Bridges, Rest Areas, River and Mountain Tunnels, Weigh Stations, and Traffic monitoring Systems

Data Collection  Data collection methods should be developed with data at its core rather than the applications they serve. Applications may go obsolete and be updated but the data collection and how it is structured must be able to be migrated and integrated to multiple/other systems. Therefore, the electronic collection, dissemination, and updating is critical.

What is it? Sign Installation? Number of Signs? Type of Sign? Number of Posts? Type of Posts? All the above?

Each Installation or Location?

Traffic Control Devices  Installation?  Signal Heads?

“In general the asset data collection requirement can be categorized into the following: Locations are usually denoted by a “from – to” county-route-mile (CRM) for linear assets. Nonlinear assets are point specific and are denoted by either a single CRM or by use of landmark data. In all cases GPS coordinates and or physical landmarks can be used to acquire the location information for both linear and nonlinear assets. Physical Attributes collected will vary from asset to asset. Physical attributes are used to describe the asset in question. General attributes that are consistent across assets include: material type, size, and length. Condition assessment is depended on the specified performance criteria for the asset. Condition Assessment can be broad for some assets requiring only Good, Bad or Fair, while other assets may require a more detailed approach set forth by national or regional accepted practices or standards. Table 2-1 presents the basic inventory attributes for transportation assets. Condition attributes vary greatly from asset to asset as to how they are reported; therefore they were excluded from Table 2-1.”

Asset Units of Measures Point data Linear data Area

Scanning Existing Documents

Where is it? Physical count of assets on the highway by: coordinates milepoints road section geographical area road network maintenance section

Dynamic Segmentation  Dynamic Segmentation is a term used to describe the process of combining data from two or more perspectives by "dynamically" creating a third set of sections that represents the smallest common denominator sections between the first two sets.  Dynamic Segmentation is useful for performing, "show me" kind of reports on a roadway database. It is not, however, very useful for sharing data across many applications.  It is easy to illustrate Dynamic Segmentation by drawing two strip maps of a road both showing the set of sections from two different section perspectives

Dynamic Segmentation Milepoint ADT10,0005,000 Number of Lanes4 Lane2 Lane Guardrail 12,000' 500' Mowable Acres1025 Maintenance Cost$100$240 Results of DS AADT10,0005,000 Number of Lanes42 Guardrail4,0008,500 Mowable Acres1025 Maintenance Cost$100$240

Data Collection Methods Efforts to streamline asset data collection have been underway since the 1960’s. The general progression of transportation asset data collection is presented below:  Photo log: Originally collected form of the data (e.g. had to be viewed through sequential image access of film). Mainly occurred through 1960’s to 1980’s. Many DOT’s had this type of program though sometimes the activity got cut in times of economic pressure.  Video log: This data collection form data could be random accessed when placed on a laser disk. Mainly occurred 1980’s to some DOT’s at present (though most converting to digital).  Regular Resolution Digital images: (i.e., 640 by 480 resolution). These are typically placed on CD’s, DVD’s or a large network server. Mainly mid- 1990’s to present.  High-resolution digital images: (i.e., 1300 by 1000 resolution). Mainly later 1990’s to present. Increasingly DOT’s are looking to place the image data on a large server and make available across a network (where sufficient bandwidth and speed exist). e.g., Minnesota DOT.

Mobile data collection Involves the use of a vehicle that is equipped with a distance measuring device and or GPS capabilities, digital video camera’s, and the appropriate computer hardware to capture, store and process the data collected.

Satellite or Aerial Imagery High resolution images that are acquired via satellite, or plane may also be used to reference the location information for transportation assets. The individual pixels corresponding to the assets in the picture are geo-referenced with respect to ground locations. Once the image is geo-referenced the location of the assets can be extracted, manually or via a software computer package.

Data Collection Tablets and On-board Computers with User Defined Keys

Collection Devices for the Appropriate Asset

Handheld Data Collectors GPS Touch Entry Voice Digital Camera

Condition Assessments Reflectometer

Condition Assessment

ARAN Automated Road Analyzer by Roadware Skid Truck Condition Assessments

Sharing the Pain   Pavements: Flexible Pavements (HMA), PCC Pavements, Unpaved Roads; Paved Shoulders, and Unpaved Shoulders   Roadsides: Vegetation and aesthetics, Trees, Shrubs and brush, Historic Markers, and Right-of-way Fence   Drainage Structures: Cross Pipes and Box Culverts, Entrance Pipes, Curb & Gutter, Paved Ditches, Unpaved Ditches, Edge Drains and Under- drains, Storm Water Ponds, and Drop Inlets   Traffic: Attenuators, Guardrail, Pavement striping, Pavement markings, Raised pavement markers, Delineators, Signs, and Highway Lighting   Structures and Bridges: Overhead Sign Structures, Structural Culverts, Overall Bridge, Sound Barriers, and Retaining Walls   Special Facilities: Movable Bridges, Rest Areas, River and Mountain Tunnels, Weigh Stations, and Traffic monitoring Systems

Team Activity Card ( front ) Identifies:  Location  Labor  Equipment Maintenance Activities

Team Activity Card ( back ) Identifies:  Material  Reimbursable Incidents  Remarks Maintenance Activities

Handheld Data Collectors GPS - location Touch Entry Voice payroll vehicle usage accomplishments

Clip-on scanner Voice, Data, and Bar Code Data Capture Fixed Scanners Handheld Scanners Windows CE applications Inventory Stock Control

Digital Video Imaging Synchronization of multiple cameras  Safety  Posted Speed  Desktop data collection  Validation

Accuracy

Sign Installation / Tree Height

Overview of Digital images Data Capture Session Image Metadata GPS+DMI Image Files Data Collection Tools Database Field Operation Office Operation Field Data Collection Optional Data Collection While in the Field, or Back in the Office

Data Collection Method of Data Collection Asset Type Number of Installations Time to Collect Data QA/QC Digital ImagingInlets925 hours2 hours Digital ImagingGuardrail75 (10,636 feet)2 hours1 hour Digital ImagingTraffic Control Devices401 hour0.5 hours Digital ImagingMowing Acres202 hours0.5 hours Digital ImagingWildflower Beds4.5 hours.25 hours Digital ImagingSigns1453 hours1 hours Field backpack/GPSGuardrail124 hours Field backpack/GPSInlets9416 hours8 hours

Associated Cost Digital Imaging Asset Type Hours for Asset PersonnelRateAsset Cost Guardrail2Field Collection Specialist$53.72$ GIS Specialist$ Project Manager$69.89 Sub Total $ Inlets5Field Collection Specialist$53.72$ GIS Specialist$44.33$ Project Manager$69.89 Sub Total $ Total $ Backpack/GPS Guardrail4Field Collection Specialist$53.72$ GIS Specialist$44.33$ Project Manager$69.89$ Sub Total $ Inlets16Field Collection Specialist$53.72$ GIS Specialist$44.33$ Project Manager$69.89 Sub Total $1, Total $1,816.03

Who will collect the data?

In-House vs. Outsourcing Data Collection In-House: Advantage Disadvantage  Knows maintenance Less familiar to technology  Knows the roadway Cost to train  No cost to Collect Impedes maintenance work  Ownership/Control Cost to upgrade Outsourcing:  Knows technology Cost  Maintains equipment Cost  Upgrades TechnologyCost  Does not require state resources n/a

main - te - nance \ mant - nen (t)s\ n [ME, fr. mainteni] 1; the art of preserving, protecting, restoring o repairing property - previously viewed as an incidental operation; now considered integral to well managed entities. 2: the last and most critical element in the planning, design, and construction of a facility. 3: a planned and organized effort often performed under chaotic circumstances or impossible time limits, usually without ample resources (see miracles) 4: slang : a person or group of persons reputed to be able to complete any task regardless of the circumstance or conditions placed upon that person or group of persons. Maintenance