Public sector & GeM
Road-safety analytics at network scale
Severity modelling and hotspot analysis over millions of incident records — evidence for where the next crore of road-safety spend goes.
This page describes a problem class and the architecture we deploy for it, written against a named public benchmark — the open dataset or competition where the world's data scientists have tested approaches to this exact problem against a hard metric. It is not a client engagement, and the benchmark's results, prizes and rankings belong to its host and participants, not to us. Client work is confidential and is only ever published with written permission.
The problem
What it costs when this goes unsolved.
Road-safety budgets are allocated on anecdote more often than anyone admits: the junction with the recent tragedy gets the signal, whether or not it is the network's riskiest. At national scale the evidence exists — millions of incident records with location, conditions and severity — and the analytical job is turning them into a ranked, defensible answer to one question: where does intervention save the most harm per rupee.
Incident data at this scale is observational and biased by reporting: severity is recorded unevenly, low-harm incidents go missing in exactly the places with least enforcement, and exposure (traffic volume) is what makes a raw count meaningful. The reference dataset for this class runs to 7.7 million records with 40+ attributes; the published work on it is a lesson in how much of the problem is geospatial data engineering.
How we build it
The architecture, stage by stage.
01
Exposure-adjusted risk, not raw counts
Incident counts normalised against traffic volume and road characteristics, because a busy junction is not the same thing as a dangerous one.
02
Severity modelling with GBMs
Gradient-boosted models over location, road features, time and weather estimate severity risk per segment — with the reporting bias named and bounded, not ignored.
03
Hotspot clustering at network scale
Spatial clustering that finds persistent risk concentrations rather than one-off tragedy sites, stable across years.
04
Decision-grade outputs
A ranked intervention list with the evidence behind each entry — the format a public works committee or an insurer's pricing team can actually act on.
Model families on this problem class: Geospatial pipelines · GBM severity models · Spatial clustering · Exposure normalisation.
What you get
What an engagement hands over.
Everything below goes in the scope document before you sign it, with a fixed price or a rate with a ceiling — the same terms as every other engagement in the catalogue.
- A severity model and network-wide risk map over your incident data
- Hotspot rankings with the confounders stated, not buried
- Seasonal and temporal patterns for enforcement and response planning
- An open methodology note — public-sector work should be auditable by the public
Provenance
The benchmark behind this page.
US Accidents (2016–2023), run by Sobhan Moosavi et al., a peer-reviewed research dataset, is the public proving ground for this problem class. The figures below are the host's, cited as context for how seriously this problem is tested in the open — they are not our results and we do not claim them.
How to buy this
The services this build draws on.
More proof
Next step
Thirty minutes on whether this fits your problem.
Bring the constraint — the regulator, the data boundary, the latency budget. If your data cannot support this build yet, the call will conclude with what to fix first, not with a proposal.