PREREQUISITE FOR ACCIDENT-FREE TRAFFIC AT SIGNAL-CONTROLLED INTERSECTIONS

Introduction: Improving intersection capacity will not be possible without accounting for traffic safety. Purpose of the study: We aim to determine the prerequisite for accident-free traffic at signal-controlled intersections with turning traffic flows. Methods: In our study, we used the methods of observation, comparison, and mathematical analysis. Results: We have carried out a field observation of traffic intensity at signal-controlled intersections in the city of Yekaterinburg, focusing on vehicles that moved when the green light was on. We have also analyzed traffic flow moving in three directions in the same lanes. We have discovered that traffic accident likelihood is the highest (54%) at four-way intersections. Three-way intersections account for 44% of traffic accidents, while the remaining 2% of accidents occur at multi-way intersections. Furthermore, we have determined the additional factors that impact safety in turning traffic flows at intersections. Our study demonstrates that in order to ensure maximum intersection capacity, the duration of the traffic signal cycle must be adjusted with the minimum safe distance between vehicles in mind.

Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing

Building extraction from LiDAR data has been an active research area, but it is difficult to discriminate between buildings and vegetation in complex urban scenes. A building extraction method from LiDAR data based on minimum cut (min-cut) and improved post-processing is proposed. To discriminate building points on the intersecting roof planes from vegetation, a point feature based on the variance of normal vectors estimated via low-rank subspace clustering (LRSC) technique is proposed, and non-ground points are separated into two subsets based on min-cut after filtering. Then, the results of building extraction are refined via improved post-processing using restricted region growing and the constraints of height, the maximum intersection angle and consistency. The maximum intersection angle constraint removes large non-building point clusters with narrow width, such as greenbelt along streets. Contextual information and consistency constraint are both used to eliminate inhomogeneity. Experiments of seven datasets, including five datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), one dataset with high-density point data and one dataset with dense buildings, verify that most buildings, even with curved roofs, are successfully extracted by the proposed method, with over 94.1% completeness and a minimum 89.8% correctness at the per-area level. In addition, the proposed point feature significantly outperforms the comparison alternative and is less sensitive to feature threshold in complex scenes. Hence, the extracted building points can be used in various applications.