Complete Pavement Markings for Safe and Complete Streets

Safety is central to any complete street project. This research aims to utilize LiDAR data to automatically detect and evaluate pavement marking conditions. These markings are essential for delineating bike and bus lanes and communicating safety-critical traffic rules such as yield and stop signs to road users. Poorly maintained markings, if faded or worn out, significantly jeopardize the safety of vulnerable road users and can expose transportation agencies to substantial legal liability. Additionally, degraded markings can compromise the functionality of complete street projects by causing confusion and misunderstanding among drivers and vulnerable road users. This project will demonstrate the feasibility and cost-effectiveness of using large-scale LiDAR data to automatically assess pavement marking conditions, with a particular focus on retro-reflectivity. We will concentrate on bike lanes in a region serving a population of over half a million. We will explore two data collection methods: mobile LiDAR mounted on a vehicle and airborne LiDAR, with the latter enhancing the scalability of the proposed solution. The results will be delivered in an accessible GIS format.