Table 3 Independent variables and associated spatial layers used in the regression models (hex100 and constant across τ)
From: Modal synergies between dockless electric bikes and rail transit in Lausanne Switzerland
Aspect | ID | X | Unit | Source |
|---|---|---|---|---|
Accessibility (D1) | ||||
Station Proximity (X1) | X1A | Distance to nearest entrance | Metres | OSMNx (2025) |
X1B | Distance to Lausanne-Flon | Metres | ||
X1C | Distance to Gare de Lausanne | Metres | ||
Road Network (X2) | X2A | Cycling Paths | Metres | OSM (2025) |
X2B | Intersections | Density | OSM (2025) | |
X2C | Slope | Mean | swissALTI3D (2024) | |
Parkings (X3) | X3A | Cycling Facilities | Capacity | OSM (2025) |
X3B | Car Parking Areas | Surface | ||
Multimodal Services (X4) | X4A | BRT and Bus Stops | Sum | GTFS (2025) |
X4B | PBS Stations | Sum | OSM (2025) | |
Land use (D2) | ||||
Population Density | X5 | Residents | Density | STATPOP (2024) |
Employment Density (X6) | X6A | Sector 1 | Density | STATENT (2023) |
X6B | Sector 2 | Density | ||
X6C | Sector 3 | Density | ||
POIs Density (X7) | X7A | ‘Local’ POIs | Density | OSM (2025) |
X7B | ‘Intermediate’ POIs | Density | ||
X7C | ‘Regional’ POIs | Density | ||
Land Use Types (X8) | X8A | Residential | Binary | NOAS04 (2020–2025) |
X8B | Public services | Binary | ||
X8C | Commercial and Office | Binary | ||
X8D | Industrial | Binary | ||
X8E | Farmland | Binary | ||
X8F | Green Space | Binary | ||
Mobility tool and social factors (D3, weighted) | ||||
Ownership (X9) | X9A | Driving Licence | Ratio | MTMC (2021) |
X9B | Motorisation | Mean | ||
X9C | Bicycles | Mean | ||
X9D | e-Bikes | Mean | ||
Availability (X10) | X10A | Transit Pass | Ratio | MTMC (2021) |
X10B | PBS Pass | Ratio | ||
Demographics (X11) | X11A | Age | Mean | MTMC (2021) |
X11B | Household Size | Mean | ||
Resources | X12 | Income | Mean | MTMC (2021) |
