Description This dataset provides high-resolution spatio-temporal data on shared mobility vehicles in Munich, Germany, collected between June 1, 2023, and May 31, 2025. It includes: Idling data, identifying stationary periods of vehicles based on spatial clustering of consecutive GPS positions. Trip-level data, representing movements between idling locations, filtered by distance and duration. The dataset covers five providers across three shared mobility modes: Car-sharing: Miles, ShareNow Bike-sharing: MVG Rad E-scooter-sharing: TIER, VOI Geographic Scope City: Munich, Germany Latitude: 47.9° N to 48.4° N Longitude: 11.15° E to 11.9° E Files Included 1. idling_{provider}.parquet.gz Content: Idling periods derived from position data Criteria: Stationary within 100 m radius Columns:- id: Vehicle ID - lat: latitude (EPSG:4326) of the vehicle’s idling location- lon: longitude (EPSG:4326) of the vehicle’s idling location- starttime: unix timestamp of the vehicle’s idling start time- endtime: unix timestamp of the vehicle’s idling end time 2. trips_{provider}.parquet.gz Content: Derived trips between idling periods Criteria: Distance >= 100 m, duration <= 6 hours Columns:- id: Vehicle ID - startlat: latitude (EPSG:4326) of the trip's departure position- startlon: longitude (EPSG:4326) of the trip's departure position- starttime: unix timestamp of departure- endlat: latitude (EPSG:4326) of the trip's arrival position- endlon: longitude (EPSG:4326) of the trip's arrival position- endtime: unix timestamp of arrival 3. vehicles_{provider}.parquet.gz Content: Vehicle-specific informationColumns:- id: vehicle ID- vehicle_type: vehicle’s model specification - fuel_type: vehicle's primary energy source- color: vehicle color- time_first_seen: unix timestamp of vehicle’s first appearance in the data- time_last_seen: unix timestamp of vehicle’s last appearance in the data 4. service_area_{provider}.parquet.gz Content: Service Areas Columns:- provider- geom_service_area: multipolygon of provider’s service area (EPSG:4326) 5. scraped_urls.json Content: List of all queried URLs 6. sample_trips_mvgrad_june2023.csv Content: Sample of trips made with MVG Rad in June 2023, extracted from trips_mvgrad.parquet.gzColumns:- id: Vehicle ID - startlat: latitude (EPSG:4326) of the trip's departure position- startlon: longitude (EPSG:4326) of the trip's departure position- starttime: unix timestamp of departure- endlat: latitude (EPSG:4326) of the trip's arrival position- endlon: longitude (EPSG:4326) of the trip's arrival position- endtime: unix timestamp of arrival Key Statistics Provider Mode Unique IDs Entries (Idling Records) Miles Car-Sharing 5,019 2,873,693 MVG Rad Bike-Sharing 3,796 1,582,172 ShareNow Car-Sharing 1,727 1,348,692 TIER E-Scooter 6,705 3,011,856 VOI E-Scooter 9,242 5,454,555 Data Collection & Processing - Source: move.mvg.de (now offline) - Method: Python-based web scraping in 3-minute intervals - Coverage: Munich area divided into overlapping grid cells - Storage: PostgreSQL database - Idling detection: Based on spatial clustering within 100 m - Trip detection: Transitions between idling periods, filtered by distance and duration Validation Trip data for MVG Rad was partially validated against official open data. In June 2023, 88.2% of official trips were matched with derived trips. Limitations - Gaps may occur due to scraping interruptions, reservations, or round-trips.- Some ShareNow vehicle IDs show unusually short trip durations.- No raw position data, route, pricing, or user data is included.