Road crashes result in yearly 1.3 million deaths and 2.3 trillion USD of economic damage. Because of this pressing societal issue, the UN has declared in 2015 the global sustainability goal to halve the number of road deaths by 2020. This goal failed: Road deaths keep rising worldwide.
Many cities are wondering how to solve this issue. However, they might not have the full picture because road crash statistics tend to be reported in a victim-centered way.
There are detailed statistics on the distributions of victim demographics such as age or gender, but this neglects necessary information for answering two important questions towards better crash prevention: (1) Who causes the crashes? (2) Why do crashes happen?
The first question can be explored via the so-called casualty matrix. It shows the casualties between all combinations of different traffic participants, for example the threat of cars on cars, of cars on pedestrians, or the threat of trucks on cyclists.
As has been previously shown in an impressive data visualization by a Dutch journalist and researcher team, the by far biggest threat to human life on urban streets in the Netherlands is motorized vehicles – cars and trucks – while cyclists and pedestrians are overwhelmingly their victims and harmless.
This sounds plausible, but a systematic, quantitative study over multiple countries has been missing.
The second question – Why do crashes happen? – is much harder to answer. Generally, crashes happen in an interplay between the individual behaviors of crash participants and their environments.
Environmental features like the extent of pedestrian areas, cycling tracks or speed limits are easier to collect than behavioral data, therefore their relation to road user risk could be explored in a straightforward way.
And because the environment can be changed or regulated by decision makers, they can be held responsible to act.
To support with evidence such actions towards making cities safer, the OECD recently called for developing a modern approach to road safety: (1) collect and analyze crash data from a larger set of cities, (2) investigate the relationships between urban shape, density, speeds, and road user risk, and (3) analyze casualty matrices.
Inspired by these OECD recommendations and the Dutch data visualization, in our work we collected crash data from 24 cities in 5 European countries with high enough resolution to build and explore casualty matrices, to quantify road safety in a systemic approach, and to identify those urban features that are most relevant, especially for vulnerable road users like pedestrians who are known to be disproportionally impacted.
Exploring the casualty matrices first, we found the same overall picture as our Dutch colleagues – see Fig. 1: Cars are the most substantial hazard.