High-resolution dataset of car, bike, and e-scooter sharing in Munich, Germany (2023–2025): Vehicle idling positions and derived trips

TL;DR

This paper introduces a detailed dataset tracking shared vehicles in Munich from 2023 to 2025, including their locations and trips, to support urban mobility research.

Contribution

The novel contribution is a high-resolution, validated dataset combining vehicle idling positions and derived trips across multiple shared mobility providers in Munich.

Findings

The dataset includes vehicle idling locations and derived trips for five providers across three mobility modes.

Vehicle metadata like model, color, and timestamps are included, enabling analysis of fleet dynamics and user behavior.

The dataset is publicly accessible and partially validated against official trip records for reliability.

Abstract

This dataset provides high-resolution, processed spatio-temporal data on shared mobility vehicles in Munich, Germany, collected between June 2023 and May 2025. It includes vehicle idling locations and periods, as well as derived trips for five providers across three mobility modes: two car-sharing, one bike-sharing, and two e-scooter-sharing systems. The dataset was created by processing vehicle availability data scraped at regular intervals from a public mobility platform. Idling locations were identified by clustering consecutive vehicle positions within a small spatial radius, while trips were inferred as movements between these idling periods. In addition, vehicle-level metadata such as model, color, fuel type, and timestamps of first and last appearance are included. The resulting dataset enables detailed investigations into, among others, fleet dynamics, user behavior, and temporal trends. All data are publicly accessible and partially validated against official trip records, offering a reliable resource for urban mobility research and multimodal transport analysis.

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