DOI: 10.5281/zenodo.15629734
title: Dataset of Idling Positions and Derived Trips of Shared Mobility Vehicles in Munich, Germany (2023–2025)

description: 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.