henk stipdonk (swov) paul wesemann (swov) ben ale (tu-delft)

Henk Stipdonk, 16 november 2009SWOV workshop, Haarlem

Scientific Research on Road Safety Management

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)



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)



A very simple modelestimated with a state space approach

68% interval:

680

stochastic projection of distance travelled Mmathematical intervention on risk in 2004

0

1000

2000

3000

1950 1960 1970 1980 1990 2000 2010 2020

year t

fata

litie

s C

F (fatalities)

expected F

F±σ



Uncertainties in the estimationof the expected number of fatalities

1. Crash data are subject to chance2. Mobility data subject to measurement error 3. 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.



Projection with fixed future mobility

68% Interval:

207

deterministic distance travelled Mmathematical intervention on risk in 2004

0

1000

2000

3000

1950 1960 1970 1980 1990 2000 2010 2020

year t

fata

litie

s C

F (fatalities)

expected F

F±σ



Projection with fixed future mobility and without mathematical intervention in 2004

68% Interval:

218

Shift: 45deterministic distance travelled M

no mathematical intervention on risk in 2004

0

1000

2000

3000

1950 1960 1970 1980 1990 2000 2010 2020

year

fata

litie

s C

F (fatalities)

expected F

F±σ



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.



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)).



Stratification

We must look for:1. Subsets of crashes that differ from each other both in the

development of M(t) ánd in the observed R(t).2. 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)



Crashes, mobility and riskby traffic mode and age.

1

10

100

0 10 20 30 40 50 60 70

driver age [year]

ca

r s

ing

le v

eh

icle

dri

ve

r ri

sk

[10

-9 p

oli

ce

re

gis

tere

d K

SI/

km

]

19871992199720022007

10

100

1000

0 10 20 30 40 50 60 70

bicycle driver age [year]

bic

ycle

-car

dri

ver

risk

[10-9

po

lice

reg

iste

red

KS

I/km

]

19871992199720022007

Single vehicle casualty risk

Bicycle-car casualty risk

motorcycle

0

0,5

1

1,5

2

1950 1960 1970 1980 1990 2000

year t

mo

bil

ity

[1

09 k

m]

Motorcycle

0

100

200

300

1950 1960 1970 1980 1990 2000

year tri

sk [

10-9

fata

liti

es/k

m]



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.



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.



Some examples of explanatory factors

Example: safety belts:

1. Risk reduction 40% (for fatalities)

2. We need data of the use of safety belts as time series

3. Safety belts are only effective for car (etc) occupants.

Example: roundabouts:

1. Risk reduction 75% (for fatalities)

2. How many roundabouts built (new and reconstructions)?

3. Separate crashes on junctions from crashes on links.



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.

Stratification Risk scenario 2004

By traffic mode By road type By age

A single unique decrease 585 630

Continuous annual decrease 549 490 562

Average 567 490 596

Average ( range) 563 (490-630)

Estimations of expected number of road deaths in 2020 in the Netherlands



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:

1. Road pricing

2. Strategic policy plan+ (advisory ISA, accompanied driving, DRL, ESC, continuation of sustainably safe infra).

3. Additional measures (extra infrastructural improvements, forcing ISA, alcohollock, reduction of dangerous moblity).



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.



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.



Dutch data seem to enable a plausible model for risk, stratified by age and traffic mode

Example:

Bicycle casualties. Data and model



Thank you for your kind attention

henk stipdonk (swov) paul wesemann (swov) ben ale (tu-delft)

Documents