Open Road Risk Methodology

Guide to the Open Road Risk methodology pages covering data joining, feature engineering, modelling, shrinkage, and diagnostics.

What this section covers

The Methodology section documents the decisions and processes that turn raw data sources into model-ready features. It is distinct from the Models section, which covers the predictive models themselves and their performance.

A useful way to think of the split:

  • Methodology: how the data is shaped — joining sources together, building features, handling overdispersion in count data.
  • Models: what predictions are made from that data — traffic estimation, time-zone profiles, collision risk.

The two sections sit on either side of a clear handoff. Methodology produces road_link_annual.parquet with one row per link per year and a fixed schema. Models consume that table and produce predictions.

Pages in this section

Joining the Datasets — how STATS19 collisions, AADF count points, WebTRIS sensors, and OS Open Roads links are spatially joined into a single road-link-by-year table. Includes the four-dimension weighted snap, distance caps, and quality tracking.

Feature Engineering — link-level features built on top of the joined table: network centrality, road geometry, OSM attributes, population density. Documents the imputation rules for sparse OSM fields and the speed_limit_mph_effective tiered fallback.

Empirical Bayes Shrinkage — the post-modelling adjustment that handles overconfidence on links with zero observed collisions. Explains the choice of shrinkage parameter k and the operational impact on rankings.

Methodology vs Models

A few decisions sit on the boundary between sections:

  • Year handling is a methodology question (how to encode time) but interacts directly with model choice. The pipeline uses year as a categorical feature; the COVID years (2020–2021) are absorbed by year-specific fixed effects. An is_covid flag is the natural alternative if year is not already in the model.
  • Group-aware cross-validation is a model-choice decision but depends on methodology — the grouping key (count_point_id, site_id, link_id) is determined by what the joining pipeline produces.
  • Forbidden post-event columns — several derived columns are excluded from modelling because they are computed from collision records themselves. This is enforced in the modelling code but the list is documented as a methodology decision.

These cross-cutting items are documented on the page where they have most impact. Cross-references run in both directions.

What is not in Methodology

  • Individual model details (architectures, hyperparameters, metrics) are in Models.
  • Exploratory analysis of source data is in Exploratory Data Analysis — what the raw data looks like before processing.
  • Source dataset descriptions are in Data Sources — provider, format, fields, project location.