---
title: "Road Collision Analysis – United Kingdom"
url: "https://www.systra.com/en/projects/road-collision-analysis-united-kingdom/"
type: projects
date_published: 2026-03-17
date_modified: 2026-05-21
schema:
  @type: WebPage
language: en-US
word_count: 347
reading_time: 2 min
canonical: "https://www.systra.com/en/projects/road-collision-analysis-united-kingdom/"
featured_image: "https://www.systra.com/wp-content/uploads/2026/03/gettyimages-1644775768.jpg"
type-market:
  - Planning and consultancy
project-market:
  - Other sectors
project-service:
  - Planning and Consultancy
project-location:
  - United Kingdom
---

# Road Collision Analysis – United Kingdom

![Road Collision Analysis – United Kingdom](https://www.systra.com/wp-content/uploads/2026/03/gettyimages-1644775768.jpg)

**SYSTRA has exploited datasets and performed analysis to help the UK’s Department for Transport (DfT) identify and prioritise road collision causes, combining analysis and cutting-edge technology.**

A combination of statistical analysis, geospatial analysis and innovative
machine learning\* techniques were applied to highlight the circumstances and
contributory factors that lead to death and serious injury on roads in Great
Britain and determine the key components affecting the outcome of reported
collisions.

## Fine-tuning data

More specifically, the machine learning\* techniques were applied to correlate
collision data with detailed mapping information (OpenStreetMap and OS
MasterMap), allowing exploration of relationships between collisions and the
physical environment.

The key findings were that most collisions occur in densely populated areas,
however fatal and serious collisions are more commonly observed on rural roads.
The most vulnerable road users in these rural areas are motorcyclists or pedal
cyclists.

![gettyimages-1937282970](https://www.systra.com/wp-content/uploads/2026/03/gettyimages-1937282970-660x660.jpg)

## A clear view of results

An interactive StoryMap was delivered to visualise the project outcomes,
highlighting problematic areas and demonstrating the value of maps to assist in
decision making.

\*Machine learning is a field of study in artificial intelligence concerned with
the development and study of statistical algorithms that can learn from data and
generalise to unseen data and thus perform tasks without explicit instructions.

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