For more information please visit:
· Python Package: https://github.com/datasciencecampus/transport-network-performance
· Docker Image: https://github.com/datasciencecampus/transport-performance-docker
Known
Limitations/Caveats:
These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below.
Urban Centre and Population Estimates:
· Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details.
Public Transit Schedule Data (GTFS):
· Does not include effects due to delays (such as congestion and diversions).
· Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.
Transport Network Routing:
· “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary.
Please also visit the Python package and Docker Image GitHub issues pages for more details.
How to Contribute:
We hope that the public, other public sector organisations,
and National Statistics Institutions can collaborate and build on these data,
to help improve the international comparability of statistics and enable higher
frequency and more timely comparisons. We welcome feedback and contribution
either through GitHub or by contacting datacampus@ons.gov.uk.