Calibrated multi-modal transport networks for Switzerland

Cleaned OpenStreetMap-derived walk, bike, and car networks for Switzerland (plus a cross-border buffer for realistic frontier-cell routing), with per-edge calibrated travel durations. These are the networks used to compute the companion accessibility metrics dataset: https://doi.org/10.5281/zenodo.21410968. Contents 1) Networks in .graphml representation (read e.g. using python OSMnx) and in node/edge .gpkg representation.2) Per-edge calibrated durations for every routing profile in the companion dataset (.csv): Mode Profile ID Description Walk rwalk Average pedestrian Walk walk_prm Person with reduced mobility — slower baseline, higher slope penalty, hard-exclusion of steps Bike rbike Regular bicycle Bike ebike25 E-bike, 25 km/h class (pedelec) Bike ebike45 E-bike, 45 km/h class (s-pedelec) Car car_night Late-evening / night traffic Car car_base Baseline daytime hours Car car_peak Peak traffic hours (Mon–Fri, 7:30–8:30 and 15:30–18:30) Usage To attach calibrated durations to the network, join edges_<mode>_calibrated.csv on the edge_id column (format u:v:key) into whichever representation you prefer: networkx — load with nx.read_graphml(...) (or ox.load_graphml(...)), split each edge_id back into (u, v, key), and apply with nx.set_edge_attributes. GeoPandas / QGIS — load <mode>_edges.gpkg and merge (or add a table join) on edge_id. Attach only the profiles you need — each duration_calibrated_<profile> column is independent, so a car-peak-only analysis doesn't need the other columns loaded. Methodology Networks extracted from OSM via the aperta-atlas preparation pipeline (PBF clipping, degree-2 chain collapse, snap-eligibility flagging, per-edge attribute decoration). Per-edge durations for walk, bike, and each car profile are fit against Google Maps API travel times sampled from Swiss travel-survey (MTMC) trips. Calibration effectiveness Per-edge travel-time weights are fitted against Google-Maps-derived travel times for a sampled subset of Swiss travel-survey (MTMC 2015/2021) trips. The improvement over raw / uncalibrated OSM speed limits is substantial — especially for car, where raw OSM speeds don't account for signalization, intersections, or congestion: Mode Trip distance R² uncalibrated R² calibrated Walk all 0.85 0.94 Bike < 5 km 0.75 0.93 Bike 5–25 km 0.73 0.92 Car (base hours) all 0.80 0.97 Car (base hours) < 5 km 0.00 0.85 Car (base hours) 5–25 km 0.16 0.91 Car (peak hours) all 0.69 0.96 Car (peak hours) < 5 km −0.10 0.84 Car (peak hours) 5–25 km −0.16 0.84 The "all" rows for car are dominated by long-distance trips (where raw OSM speeds happen to perform reasonably); the sub-5 km bands are more informative for urban-accessibility applications. R² = 1 is a perfect fit; R² < 0 means the prediction is worse than just returning the mean of the observed travel times. Raw OSM speeds for peak-hour car trips fall into that regime — a strong signal that uncalibrated OSM edge weights should not be used at all for time-based accessibility analyses in urban environments. Reproduction Produced with the aperta accessibility library (https://github.com/mmiotti/aperta) via the aperta-atlas pipeline (https://github.com/mmiotti/aperta-atlas), scenario switzerland-default. Attribution and license This dataset is a Derivative Database of OpenStreetMap and is released under the Open Database License (ODbL). Contains data © OpenStreetMap contributors; see https://www.openstreetmap.org/copyright.