City-Scale Bike Lane Facility Management through LiDAR, Weakly Supervised Learning, and Dimensionality Reduction

City-scale facility management plays a critical role in public health. This research aims to analyze city-scale infrastructure through a cost-effective approach by utilizing light detection and ranging (LiDAR) point cloud acquisition, dimensionality reduction processing, and advanced machine learning segmentation methods. A case study in a southwestern city in the United States examined bike lane infrastructure along 38.6 km using a backpack-mounted light detection and ranging LiDAR scanner on an electric scooter. The collected point cloud data were projected into two dimensions (2D) along the z-axis (up/down) and partitioned into crops in image format. A weakly supervised learning approach was employed for segmentation. The neural network segmented moving car trajectories, bike lane marking, and road curb segmentation. The postprocessed outputs evaluated city-scale bike infrastructure by measuring (1) lane widths; and (2) the closest distance between moving cars and the lanes, providing insights into the current state of road safety for cyclists. The results indicated that 85% of the roads examined had unsafe conditions due to reduced lane widths or shorter lateral distances, thus falling short of design guidelines. This study demonstrates a cost-effective and scalable method for city-scale data collection and analysis to inform infrastructure planning and cyclist safety assessments.