Neighborhood-scale barrier-free accessibility assessment using handheld LiDAR point cloud

Aging in place depends on a barrier-free neighborhood travel environment, yet quantitative assessment of the “last kilometer” barrier-free accessibility has not been adequately addressed. This study proposes an assessment framework based on handheld LiDAR point clouds. Near ground obstacles are first detected using elevation interquartile range (IQR) values to construct an obstacle cost map. An eight-neighbor reverse Dijkstra procedure is then used to compute accessible areas, geometric and equivalent travel distances for key neighborhood nodes. Experiments across three sites (including both flat and hilly residential neighborhoods, as well as a school) demonstrate that: 1) The elevation IQR-based obstacle detection method, evaluated at the adopted 0.5 m grid resolution, achieved a Precision of 88.2%, Recall of 83.6%, and F1-score of 85.8% across five manually labelled subsets. 2) Geometric and equivalent distances provide complementary information on wheelchair accessibility. Under the prescribed optimized scenarios, geometric path distance and equivalent travel distance across five functional nodes in the flat terrain neighborhood decreased by 4.24%–76.17% and 17.12%–76.86%, respectively, while the accessible area in the hilly neighborhood increased from 14,501 to 68,761 m 2 . The proposed framework provides a quantitative means for diagnosing accessibility deficiencies and assessing potential barrier-free modifications in the examined neighborhood-scale environments. To foster further research, the handheld LiDAR point cloud dataset used in this study is publicly available at: https://github.com/c175044/Handheld-LiDAR-Neighborhood-Accessibility-Dataset .