This dataset provides an authoritative, nationwide representation of public transport stops and landing areas as defined in the currently published public transport timetable of Switzerland. It covers all major transport modes, including railways, trams, buses, ships, and cable cars, and reflects the officially maintained spatial reference used for operational and planning purposes. Each location is precisely positioned using the Swiss national coordinate reference system, enabling meter-level spatial accuracy.
Beyond passenger-facing stops, the dataset also includes so-called pure operational points. These are spatially identifiable locations within the public transport network that do not necessarily function as public stops, but that play a critical operational or structural role. Examples include infrastructure reference points, internal operational markers, or elements required for network management and coordination.
Their inclusion ensures a comprehensive representation of the physical and logical structure of the public transport system. Together, these elements form a foundational geospatial layer for public transport analysis in Switzerland. The dataset supports a wide range of use cases, from timetable integration and accessibility studies to infrastructure planning, multimodal network modeling, and historical or temporal analyses through its built-in validity attributes.
It is designed to function as a stable reference dataset that can be reliably combined with network edges, service data, and demographic or geographic layers..
Switzerland
1
data-ri.ch
Freshness
Single-source
API Status
No API
Compliance (vendor-reported)
Quality Breakdown
Data-rich is an alternative data vendor headquartered in Switzerland. Data-rich specializes in audience segmentation, foot traffic analytics, geospatial, government, location planning data. This vendor has a Vedex Intelligence Score of 19 out of 100, reflecting market presence, compliance posture, integration readiness, and business maturity.
Data-rich operates in the following alternative data categories.