A random set approach for modeling integrated uncertainties of traffic Islands derived from airborne laser scanning points

Traffic islands play a major role in transport studies by affecting traffic behavior safety, air pollution, and transport decision support. Point data obtained by laser scanning enable the determination of their locations. Planimetric errors, vertical errors, and limited point spacing however affect their spatial data quality (SDQ). In this study, we defined uncertainty as the lack of accuracy and analyzed its importance by modeling each traffic island as a random set. The covering functions of the point data and their intermediate locations were determined by point segmentation, followed by interpolation. In this way, traffic islands were delineated from the background with a transition zone. The study showed that point spacing has the largest contribution to the positonal accuracy of a traffic island. The area of the transition zone has a linear relation with the planimetric errors, whereas the influence of the vertical errors on the accuracy decreases with increasing point spacing. Experiments were conducted to investigate the influences of the parameters in an SDQ analysis. The study demonstrated how different sources of uncertainty can be integrated. Results showed the advantages of using random sets for SDQ modelling. We concluded that modelling of traffic islands by random sets provides meaningful information to integrate uncertainties.