Methodology
Data, formulas, sources. Open.
No black box. Everything you see on the page can be independently verified — from public sources or by replicating the calculation.
01. Data and sources
Four public sources, each with its own license.
| Source | Content | Range |
|---|---|---|
| Czech Police (Policie ČR) | Accident dataset — location, severity, type, cause, damage | 2015 – present |
| Road and Motorway Directorate (ŘSD) | Road network and traffic-intensity data (traffic counts) | Network current, count from 2020 |
| Transport Research Centre (CDV) | Unit costs for fatalities and injuries, updated annually | 2015 – 2024 |
| Weather data | Historical analysis and short-term weather-based risk prediction | continuous |
02. Spatial matching
How an accident finds its segment.
Each accident from the Czech Police is a point on the map. The algorithm assigns it to the nearest segment of the road network within a narrow tolerance band. If no segment lies within the band (typically class III roads and local roads outside the published ŘSD network), the accident appears separately in the application, off the segment map.
The result is a dataset where every catchable accident is bound to a specific segment — a precondition for computing all indicators at the segment level.
03. Indicator formulas
Three indicators, each answering a different question.
Unit: accidents per million vehicle kilometres per year. Answers the question "What is the accident rate on a segment relative to vehicle exposure?"
Fatal accidents weighted 130×, seriously injured 70× — the index respects severity, not just count.
Specific values AF, AS, AM for 2015–2024 are in the table below.
CDV unit costs 2015 – 2024 (CZK)
AF = fatality, AS = seriously injured, AM = lightly injured. Methodological discontinuity in 2020 – 2021 (introduction of WTP survey).
| Year | AF (fatality) | AS (serious) | AM (light) |
|---|---|---|---|
| 2015 | 20,790,000 | 5,033,600 | 649,800 |
| 2016 | 19,411,000 | 5,094,200 | 668,500 |
| 2017 | 19,784,000 | 5,097,500 | 716,700 |
| 2018 | 22,534,000 | 5,983,000 | 739,700 |
| 2019 | 25,041,000 | 5,567,000 | 809,000 |
| 2020 | 35,021,000 | 5,800,000 | 362,600 |
| 2021 | 58,235,000 | 12,211,000 | 575,600 |
| 2022 | 66,763,000 | 13,847,000 | 655,000 |
| 2023 | 75,000,000 | 16,575,000 | 1,544,000 |
| 2024 | 78,184,600 | 16,002,300 | 749,000 |
04. Relationships between metrics
Spearman rank correlation between RSI, H, RAR, and accident count.
If all indicators expressed the same thing, one would suffice. Each describes a different aspect of a segment, and the degree of mutual similarity changes with the applied filter. The correlation view in the application respects the active filter and lets you verify relationships across different network slices.
- RSI and H are methodologically related — severity weights resemble the CDV coefficients. A very strong correlation is therefore expected and serves as a data-consistency check.
- RAR vs. accident count — the weaker relationship shows that segments with the highest accident counts are not necessarily the riskiest relative to traffic exposure (depends on traffic intensity).
- The By severity switch separates correlations for fatal, serious, light, and property-only accidents.
- The strong RSI–H link is also the reason a TOPSIS composite index is built on top of the indicators — it combines both indicators into a single severity indicator so the same information is not counted twice.
05. Composite index
TOPSIS — one ranking from three independent criteria.
The RSI and H indicators essentially measure the same thing — how severe the outcomes of accidents on a segment are. On the reference sample (Central Bohemian Region, 2023, 416 segments with an injury accident) their rank (Spearman) correlation is ρ = 0.98. Adding them into a single score counts severity twice — and the ranking then floats up segments that stand out in that one property alone.
The TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) avoids that: the pair that measures the same thing is first combined into a single severity indicator, and segments are then ranked on three criteria, each describing a different aspect of risk. The result is a single number between 0 and 1 and the ranking that follows from it, where rank 1 goes to the most hazardous segment in the selected set. In the application the index is available as a sortable column in the segment table, as a map colouring option, and as a value in the segment detail.
Three independent criteria
The number of injury accidents relative to how many vehicles pass through the segment and how long the segment is. A busy motorway is therefore not automatically more hazardous than a short road with lighter traffic.
A single severity indicator derived from the RSI and H pair. On the reference sample it stands for 95.3 % of the information both metrics carry jointly — so severity enters the ranking once, not twice.
How many injury accidents occurred on the segment at all, regardless of traffic intensity. Covers accidents with fatal, serious, and light injuries.
- The absolute count of all accidents is not a criterion. It is not related to traffic intensity and is negatively correlated with the RAR indicator (ρ = −0.28 on the reference sample) — including it would shift the ranking toward high-traffic rather than high-risk segments. The count of injury accidents is used instead — the same quantity RAR is built on.
- The index is relative to the active filter. TOPSIS is recomputed over the currently selected set of segments (year, region, severity, road class). Both the rank and the index value change with the filter — it is a ranking within the chosen slice of the network, not an absolute scale. Values from two different filters are not comparable.
- Which segments have an index: those with at least one injury accident and a known traffic intensity from the ŘSD count. The remaining segments have no index and stay neutral on the map.
The procedure follows our previously published work: doi.org/10.1016/j.ets.2026.100075. The reference figures quoted above (ρ, share of joint variance) come from its analysis of the Central Bohemian Region for 2023.
06. Limitations and caveats
What the page cannot do and what to watch out for.
- Traffic counts: average daily traffic intensity is available only for 2020. RAR and RSI use this value as a constant exposure — in years with markedly different traffic (2020 COVID), the value can be skewed.
- Accidents off the ŘSD network: class III roads and local roads are not part of the published network with traffic counts — accidents on them appear separately in the application, not as segment data.
- Accident-location accuracy: Czech Police coordinates have an accuracy on the order of single metres. The matching tolerance band covers most cases, but segments running close to each other can in rare cases be confused.
- CDV methodological discontinuity: the jump in AF between 2020 and 2021 is not inflation but a change in methodology (introduction of the WTP survey). Comparing annual H values across this boundary is invalid — for cross-year comparison use the relative view between segments within the same year.
- Weather data: used for weather-based risk analysis, with spatial resolution on the order of single to tens of kilometres — local phenomena (valley fog, bridge ice) are not faithfully captured. It serves as a proxy for segment-level analysis, not a substitute for a meteorological station.
Questions
Get in touch if you'd like to discuss the methodology
Comments, suggestions for extensions, or alternative data sources are welcome.