An Automated System for Large-Scale Intersection Marking Data Collection and Condition Assessment

Intersection markings gradually degrade due to traffic, rain, and snowplowing. The degradation can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale.

The TRB IDEA (Innovations Deserving Exploratory Analysis) Program's NCHRP IDEA Final Report for Project 225: An Automated System for Large-Scale Intersection Marking Data Collection and Condition Assessment develops an automated system to intelligently detect and characterize intersection markings and to assess their degradation conditions with existing roadway geographic information systems (GIS) data and aerial images.