Henk Stipdonk (SWOV) Paul Wesemann (SWOV) Ben Ale (TU-Delft)

1. The expected number of road traffic casualtiesby stratification of crash data and data on distance travelled Henk Stipdonk (SWOV) Paul Wesemann (SWOV) Ben Ale (TU-Delft)

2. The main research goal How to estimate the expected number of casualties in a chosen future year. Basic form: C(t) = R(t) ∙ M(t)

3. A very simple modelestimated with a state space approach 68% interval: 680

4. Uncertainties in the estimationof the expected number of fatalities • Crash data are subject to chance • Mobility data subject to measurement error • The model does not contain road safety knowledge. • Ad 1: switch from fatalities to killed+seriously injured? • Ad 2. mobility: refrain from forecasting mobility. • Ad 3. improve the simple model C(t) = R(t) ∙ M(t) Apart from the uncertainty in the estimation, there is still another uncertainty: the actual number of casualties is influenced by chance.

5. Projection with fixed future mobility 68% Interval: 207

6. Projection with fixed future mobility and without mathematical intervention in 2004 Shift: 45 68% Interval: 218

7. Goal and method • Reduce the uncertainty of the prediction without the use of artificial (hence not based on knowledge of safety changes) interventions in the data. • Improve the quality of the model, by finding logical relations between the number of casualties and the factors that influence this number • Allow for the estimation of the effect of (additional) safety measures, on the total number of casualties, to help policy makers choose and decide.

8. How to improve the model Basic form: C(t) = R(t) ∙ M(t). For the past we have data on C(t) and M(t) to calculate R(t). For the future we estimate R and M, to calculate C. • Replace time t as the explanatory variable. • Stratify the model. Apply different models for differences in the development of M(t) en R(t) for different subsets. • Add safety measures and other external factors fi, that relate to R through t, such that R(t) = R(fi(t)).

9. Stratification We must look for: • Subsets of crashes that differ from each other both in the development of M(t) ánd in the observed R(t). • Subsets of crashes that coincide with relevant domains of explanatory factors We have started with: • Stratification by traffic mode • Stratification by driver age. Both for different traffic modes and for different driver age, there are many examples of differences in M(t) and R(t)

10. Crashes, mobility and riskby traffic mode and age. Single vehicle casualty risk Bicycle-car casualty risk

11. Mobility, stratified by age =Mobility per capita x population By estimating the mobility per capita, and using population data, mobility data can be optimized and predicted by age.

12. How to allow for explanatory factors? To add an explanatory factor to the model, we need three conditions to be met: • We must know the relation between the factor and traffic safety. • We must know the observed time dependent presence of the factor in the transport system. • We must separate the specific domain of the crashes that is susceptible to the explanatory factor.

13. Some examples of explanatory factors Example: safety belts: • Risk reduction 40% (for fatalities) • We need data of the use of safety belts as time series • Safety belts are only effective for car (etc) occupants. Example: roundabouts: • Risk reduction 75% (for fatalities) • How many roundabouts built (new and reconstructions)? • Separate crashes on junctions from crashes on links.

14. Recent results: Aarts et al.A maximum of 500 road deaths in 2020: why not? Approach: baseline projections for different scenario’s and different stratifications. Estimations of expected number of road deaths in 2020 in the Netherlands

15. Additional measures The baseline projections assume some continuation of policy efforts that caused the downward risk trend. Only truly additional measures can further reduce the expected number of fatal casualties: • Road pricing • Strategic policy plan+ (advisory ISA, accompanied driving, DRL, ESC, continuation of sustainably safe infra). • Additional measures (extra infrastructural improvements, forcing ISA, alcohollock, reduction of dangerous moblity).

16. Result Without any extra safety efforts, but with the introduction of road pricing, an expected number of 500 fatalities may be feasible. A maximum number of 440 fatalities seems feasible with extra measures related to the current Dutch policy, and with strong extra efforts, a maximum number of 350 fatalities may be feasible. Based on these results, The Dutch minister of Transport decided to decrease the road safety target for 2020 from 580 to the more ambitious maximum of 500.

17. Next steps Use stratification by conflict type for all relevant traffic modes: one traffic mode for single vehicle crashes, and two traffic modes for two vehicle crashes. Thus, for two vehicle (a, b) crashes: Cab(t) = ρab(t) ∙ Ma(t) ∙ Mb(t) • Apply estimation of the effect of safety measures to the relevant subset. • Stratify by driver age, bring population data into the model • Use smoothed mobility data, estimate smoothed risk values.

18. Dutch data seem to enable a plausible model for risk, stratified by age and traffic mode Example: Bicycle casualties. Data and model

19. Thank you for your kind attention