Caltrans CARS

Every year, State Departments of Transportation (DOTs) like Caltrans submit highway performance data to the Federal Highways Administration (FHWA) via the Highway Performance Monitoring System (HPMS). This data helps FHWA assess the nation's highways and distribute funds accordingly. In 2014, FHWA requested that states begin submitting a geospatial network for all public roads, not just the National Highway System. Caltrans' road centerline mileage ballooned from 15,000 miles to 160,000 miles. Initially, Caltrans contracted with local universities but found the process manually intensive, slow, and of poor data quality. To mitigate these challenges, Caltrans determined that an automated data supply chain could alleviate much of their pain. They contracted with 1Spatial through the EGIS pooled fund study to implement an automated process to receive road centerlines from local counties and integrate the data with their linear reference system. This project has successfully integrated six counties into their LRS, with additional counties being added as the phases continue. 

Initially, Caltrans contracted with a local California university to hire college interns to hand-digitize all the public roads in California. Using imagery, it took the university 1.5 years to complete the effort. The time to complete, compounded by the imagery not being up-to-date, caused the data to be stale the day it was delivered. Caltrans has not updated the data in over three years. The data was not only out of date but also had quality issues, such as connectivity problems. With upcoming FHWA programs like MIRE and NRN, Caltrans knew they needed a better solution. They proposed building an automated data supply chain by collecting road centerlines from county emergency service providers.

Automating road centerline matching challenges: 

• Different Linework: The scale and precision of the digitized road centerline network from two different sources are never aligned due to varying scales and precision at which they are collected. This requires fuzzy buffer distance tolerances to account for the offset networks. 
• Different Segmentation: Emergency Service road centerlines are segmented at each intersection, while LRS data is one complete route or segmented by changes in data items only when the route information changes. This necessitates many-to-one matches between Emergency Service road segments and the LRS Routes. 
• Different Representation of the Same Feature: Divided highways, ramps, and intersections may be represented differently to support the data's core mission. For example, emergency road centerlines may contain “pinch points” to support Computer-Aided Dispatch systems, while the LRS has a “has tag” format. This requires pre-processing logic to ensure a better match rate between the two datasets. 
• Common Attributes: In many cases, the naming conventions differ between the two datasets, requiring fuzzy name matching or pre-processing to format the attributes the same. 

Through the EGIS Pooled Fund Study, Caltrans contracted with 1Spatial to build an automated data supply chain to collect local counties' NG9-1-1 Road Centerline data. This data, which includes all public roads, supports Emergency Call Routing Systems (CAD systems). Because of the nature of the data, it must be kept current and accurate. Additionally, local counties, responsible for maintaining the data, have a better geographic understanding of their communities. Caltrans aimed to collect this data and integrate it into their linear reference system. 

Caltrans leveraged 1Spatial’s 1Integrate and 1Data Gateway technology, along with their pre-configured segment network to LRS conflation algorithm and NG9-1-1 Road Centerline Validation checks. This enabled counties to submit data through an upload portal, where the data is validated and conflated automatically. This process allows Caltrans to maintain a more up-to-date and accurate network for their submissions to FHWA. The automated conflation process includes pre-processing steps to standardize the data, ensuring a higher match rate. It also incorporates fuzzy logic to account for differences in scale, precision, and attribution. The automated process can handle over 90% of the conflation, requiring minimal manual review.

Key Benefits:

• Caltrans can support Federal Initiatives (HPMS, MIRE, NRN) 
• Data is accurate, current, and topologically connected 
• Manual processes significantly reduced
• Locals have near real time access to NHS updates