A Wealth of Transportation Data—Idaho Builds a Statewide Asset Inventory

The Idaho Transportation Department (ITD) required a repeatable method to get timely, accurate, and complete highway transportation asset data inventories. Staff across the organization recognized the analytical value of this data to improve and expedite highway infrastructure planning, operations, and design projects. However, various barriers made these goals problematic.

Over the years, ITD had acquired a variety of spatial data-based applications. For example, ITD has a system for handling over-legal truck route permitting and another for asset management. But often the organization had inadequate data to effectively utilize these applications. ITD also needed to be able to demonstrate its knowledge of specific assets to meet federal regulations and other types of legal scrutiny.

Though GPS technology helped, the process of gathering asset data along ITD's highways remained manual and time-consuming. Work in the field had to be distributed across numerous ITD personnel using various methods and timetables, but that introduced consistency issues between Idaho's six semiautonomous districts. Inventories often ended up piecemeal or unfinished due to the staggering time investment required. Finally, even when completed on a district-wide basis, there were no standard update methods, and inventories frequently went stale.

This scenario likely sounds all too familiar to anyone managing transportation data. Limited resources and intense focus on delivering roadway infrastructure projects often means deprioritizing data collection efforts. To rectify this, transportation departments across the US are increasingly relying on mobile data collection and extraction typically provided by specialized vendors utilizing technologies such as light detection and ranging (lidar), high-resolution roadway photography, and automated intelligence software.

ITD initiated a pilot project called the Statewide Asset Attribute Inventory (SWAAI, pronounced sway) to accomplish these tasks. A data stakeholder team was organized with a core committee to guide the project and an extended group of staff from fields across ITD that ultimately numbered over 300 people. The SWAAI team's focus was on defining and prioritizing data needs, including attending two full rounds of meetings in each ITD district for feedback from interested groups.

After two years of project planning and vendor evaluation, the ITD team selected Cyclomedia Technology, an Esri partner, to capture the vehicle-based lidar imagery and photography for Idaho's entire state highway system.

Cyclomedia used the collected data to extract a list of deliverable Esri geodatabase feature class roadway asset inventories in accordance with ITD's specifications. The team needed imagery for 28 feature classes—barriers, lanes, intersections, lights, signs, signals, sidewalks, shoulders, utilities, and more.

The lidar data essentially produced a 3D model of ITD's entire state highway system at a high degree of point-to-point accuracy—±0.79 inches (2 centimeters)—allowing for reliable measurements of everything on or near the road, including curb ramp dimensions, vertical clearance, guardrail heights, and lane widths.

All 7,200 miles of Idaho's state highway system were captured in less than three weeks in July 2020. Cyclomedia's extraction of the 28 asset feature classes started immediately afterward, and they were delivered to ITD in a cumulative monthly series of geodatabases for quality assurance. ITD's geographic information system (GIS) staff around the state reviewed the data and provided feedback. It was vital to identify the refinements in Cyclomedia's semiautomated data extraction process that were needed to ensure ITD's specifications. Issues regarding approach (driveway) pavement status, guardrail post materials, and lane configuration definitions were clarified and rectified through this process.

Another key but unexpected aspect of the SWAAI project was Cyclomedia's Street Smart, a browser-based viewer for the collected 360° roadway photography and lidar data (viewable as a lightweight depth surface). Equipped with tools allowing ad hoc measurements to be made directly from the high-resolution imagery, ITD users quickly discovered many use cases.

For ITD's GIS staff, one of the most exciting aspects was the integration of Street Smart into the extracted GIS feature data. Every single GIS feature was given a link that opens Street Smart, zooms the user to its location in the street-level imagery, and turns the camera viewpoint to look directly at it. Online GIS web applications can leverage this capability, zooming users right to any sign, guardrail, or bridge clearance.

Data usefulness depends on accessibility. A set of geodatabase feature classes makes the data not only usable across a wide variety of geospatial and computer-aided design and drafting (CADD) applications, but also easily exportable to tabular formats. A copy of the final geodatabase was provided to each of ITD's six districts via their GIS analysts.

Esri's suite of ArcGIS Online tools was also readily available to provide data to stakeholders. The full geodatabase was published in ArcGIS Online as a feature service and placed in an array of online GIS mapping applications showcasing the data. These are also featured on the ITD SWAAI hub site, created using ArcGIS Hub. The site is a project data showcase, providing an amazing level of detail and revealing fascinating statistics and patterns. The processed lidar point cloud data is also available for ITD planning and design staff looking to generate topographic surfaces for CADD drawings.

In all, nearly 650,000 GIS asset features were extracted as part of the project. By one estimate, SWAAI saved personnel nearly 300,000 data collection hours, translating to an estimated $3.8 million in maintenance staff wages.

One of the earliest cost-saving uses of the data came from ITD's HQ Planning Services division. Over the years, ITD has struggled to maintain its data on Americans with Disabilities Act (ADA) compliance, which is necessary for keeping curb ramps and sidewalks accessible. The SWAAI data was rapidly categorized to show all sidewalks already in compliance, those that could be cost-effectively retrofitted, and those that needed replacement.

ITD staff, led by district GIS analysts, are finding other innovative ways to leverage the new data, ranging from performing speed zone analysis to identifying the locations of all incorrectly sized stop signs needing replacement. The SWAAI data is also being used to provide infrastructure project estimates on everything from the measurements of pavement needing striping to the quantities of guardrails and signs.

Following the statewide success of this enterprise data-gathering venture, ITD is planning to refresh the data every three years.