Fig. 5: Spatiotemporal detection of ‘Inside Station Catchment Areas’ DBS segments (ISCA) within the dataset.
From: Modal synergies between dockless electric bikes and rail transit in Lausanne Switzerland
The diagram illustrates the three-stage filtering pipeline applied to the cleaned dockless bikesharing (DBS) database to identify trips classified as Inside Station Catchment Areas (ISCA) segments. The input database (gdf_cleaned_df) contained 59,529 trips made by 6978 users. Detection 1 applied a spatial filter retaining only trips whose origin or destination fell within the merged 100-m station catchment areas, reducing the dataset to 17,253 trips and 3096 users, corresponding to a removal of 42,227 trips (71.00%) and 3882 users (55.63%). Detection 2 applied an origin–destination matrix filter to ensure that retained trips exhibited a valid intermodal origin–destination pair, resulting in no additional data loss. Detection 3 applied a temporal filter restricting trips to the operational window of train and metro services, removing a further 918 trips (5.32%) and 63 users (2.03%). The final ISCA database (gdf_cleaned_isca_df) comprised 16,335 trips made by 3033 potentially intermodal users, representing an overall reduction of 43,194 trips (72.56%) and 3945 users (56.55%) relative to the cleaned database.
