The DRAG model in Québec (Demande Routière, Accidents et Gravité) Robert Simard Société de l’assurance automobile du Québec Paris, May 30, 2007

2 Plan of the presentation History of the model in Québec History of the model in Québec Administrative aspects Administrative aspects Dependent variables Dependent variables Independent variables Independent variables Examples of results Examples of results Forecasting Forecasting Usefulness of the results at the SAAQ Usefulness of the results at the SAAQ –SAAQ: Société de l’Assurance Automobile du Québec Future Future

3 History of the model in Québec It all began in 1982 Professor Gaudry suggested to the leaders of the SAAQ the development of an econometric model that would Professor Gaudry suggested to the leaders of the SAAQ the development of an econometric model that would –identify the factors affecting the number of crashes and victims –estimate the impact of these factors: elasticity The report of Professor Gaudry was published in 1984 The report of Professor Gaudry was published in 1984

4 History of the model in Québec (continued) A couple of years later, in 1989, the leaders of the SAAQ agreed with Professor Gaudry to: –Implement the DRAG model for the province of Québec at the SAAQ –Train 2 of the SAAQ staff, so that they would become as familiar as possible with all the aspects of the model

5 Administrative aspects of the project Duration: Duration: –The full development of the model at SAAQ spanned over a period of 9 years –The development took place on a part-time basis

6 Administrative aspects of the project (continued) 8 reports have been published 8 reports have been published –1: Estimate of the monthly amount of distance travelled (kilometres) in Québec and analysis of the number of victims (no econometrics), Oct –2: Methodological aspects of the model, March 1994 –3: A econometric model developed for the monthly amount of distance travelled (kilometres) with gas (essentially by cars) and with diesel (essentially by trucks), August 1994 –4: Main Model: A econometric model developed for the monthly number of crashes, their severity and the number of victims, May 1995

7 Administrative aspects of the project (continued) –5: Synthesis of these results, Nov –6: Updating of the distance travelled (kilometres) and the number of crashes and victims on a monthly basis, Feb –7: Updating of the model (see n.4) developed for the distance travelled (kilometres - gas and diesel) and the monthly number of crashes, their severity and the number of victims, Mar –8: Forecasting for the monthly amount of distance travelled, the number of crashes and victims for the period , based on data available at the end of 1996, June 1999

8 Administrative aspects of the project (continued) Other publications, mainly Other publications, mainly –Conference at Saskatoon, Canada in 1993 –Conference in Paris in 1998 –One chapter in the book "Structural Road Accident Models - The International DRAG Family", edited by M. Gaudry and S. Lassarre, 2000

9 Main characteristics of the DRAG model developed in Québec Aggregated at the provincial level (all of Quebec) Aggregated at the provincial level (all of Quebec) –Population: 7.7 millions in 2006 –Registered Vehicles: 5.4 millions –Licensed Drivers: 4.8 millions Monthly data Monthly data Period Period –First time: (397 obs.) –Update: (445 obs.) –Forecasting: , based on

10 9 DEPENDENT VARIABLES Distance travelled (kilometres) Distance travelled (kilometres) –with gas –with diesel Number of crashes Number of crashes –Fatal –Bodily injury –Property damage only (PDO) Severity Severity –Mortality: n. of victims killed / n. of fatal crashes –Morbidity: n. of victims injured / n. (fatal + b. i.) crashes Number of victims Number of victims –Killed –Injured

in in 2001

12

13 INDEPENDENT VARIABLES: examples Exposure (model on the number of crashes and victims): Exposure (model on the number of crashes and victims): –Distance travelled (kilometres) Prices Prices –Gas –Maintenance of vehicles Characteristics of automobiles Characteristics of automobiles –Proportion of small cars Motorcyclists Motorcyclists –Number of vehicles –Laws: »Mandatory wearing of safety helmet (July 1972) »Headlights 24 hours a day (July 1974)

14 INDEPENDENT VARIABLES: examples (continued) Drivers: Drivers: –Proportion of young drivers: years old Laws related to road safety (in general): Laws related to road safety (in general): –Demerit points system (March 1973) –Lowering of speed limit on main roads AND mandatory seat belt use (August 1976) –New highway safety code (1982, 1987) Laws related to alcohol and driving Laws related to alcohol and driving –"0.08" (Dec. 1969) –Lowering drinking age from 21 to 18 (July 1971) –Increasing severity of Criminal Code related to alcohol and driving (Dec. 1985)

15 INDEPENDENT VARIABLES: examples (continued) Economics: Economics: –Ratio: number of unemployed / number of drivers Weather conditions: Weather conditions: –Rainfall –Snowfall –Temperature –Hours of light –Proportion of hours of sunshine / light Crash reporting Crash reporting –Improvement of the reporting procedure (July 1962) –"Constat à l’amiable" joint report (June 1979) –New reporting: min. $500 vs $250 AND less information for PDO crashes (Sept. 1988)

16 EXAMPLES OF RESULTS ( ) Effect of an increase of 10% for the following independent variables on the distance travelled (kilometres) with GAS Price of gas to travel 1 km (taking into account inflation, energy consumption and temperature): Price of gas to travel 1 km (taking into account inflation, energy consumption and temperature): –Decrease of 4.6% (|student " t"| > 2) Number of vehicles (gas) / adult Number of vehicles (gas) / adult –Increase of 11.1% (|student " t"| > 2)

17 EXAMPLES OF RESULTS ( ) Effect of an increase of 10 % for the following independent variables on the number of crashes Total distance travelled (kilometres) Total distance travelled (kilometres) –PDO crashes: Increase of 6% (|student " t"| > 2 ) –Bodily injury cr.: Increase of 7.8% (|student " t"| > 2) –Fatal cr.: Increase of 4.6% (1 < |student " t"| < 2) »Possibility of road congestion effect. If so, then »KM 1 (λ=1): Increase of 15.1% (|student " t"| > 2) WITH KM 2 (λ=2): Decrease of 7.1% (|student " t"| > 2) Price of gas to travel 1 km (with inflation, energy and temperature) Price of gas to travel 1 km (with inflation, energy and temperature) –PDO crashes: Decrease of 0.2% (|student " t"| < 1) –Bodily injury crashes.: Decrease of 3.9% (|student " t"| > 2) –Fatal crashes.: Decrease of 4.4% (|student " t"| > 2)

18 EXAMPLES OF RESULTS ( ) Effect of the following laws on the number of crashes Lowering of the speed limit on "main roads" from 100 to 90 km/h ( provincial roads, not divided 4-lane roads ) and mandatory seat belt use (August 1976) Lowering of the speed limit on "main roads" from 100 to 90 km/h ( provincial roads, not divided 4-lane roads ) and mandatory seat belt use (August 1976) –PDO crashes: Increase of 2% (|student " t"| < 1) –Bodily injury crashes: Decrease of 9.4% (|student " t"| > 2) –Fatal crashes: Decrease of 14.7% (|student " t"| > 2) Increasing severity of Criminal Code related to alcohol and driving (Dec. 1985) Increasing severity of Criminal Code related to alcohol and driving (Dec. 1985) –PDO crashes: Decrease of 4.4% (|student " t"| < 1) –Bodily injury cr.: Decrease of 15% (|student " t"| > 2) –Fatal cr.: Decrease of 28.8% (|student " t"| > 2)

19 EXAMPLES OF RESULTS FORECASTING FORECASTING

20 Examples of results Forecasting of the number of victims killed in Quebec for Observed Forecasted

21 Examples of results Number of victims killed in Quebec Forecasted vs Observed

Examples of results (continued) Number of victims killed: 2004 vs 1996 Forecasted: Decrease of 18% Forecasted: Decrease of 18% Observed: Decrease of 25% Observed: Decrease of 25%

Examples of results (continued) Forecasting with different scenarios on independent variables Forecasting with different scenarios on independent variables – Optimistic, pessimistic, … Sensitivity analysis: change in only 1 variable Sensitivity analysis: change in only 1 variable – Example » Continuous variable: impact of 10% more of that variable » Intervening variable: a particular law is not in force during a given period of time

Usefulness of such a model Estimated impact of a given factor on distance travelled, number of crashes and number of victims by severity Estimated impact of a given factor on distance travelled, number of crashes and number of victims by severity Each year: tool to better understand the evolution of the number of crashes and victims of each severity Each year: tool to better understand the evolution of the number of crashes and victims of each severity –For instance: variation between 2005 and 2006

Usefulness of such a model: o ther example Increase in the price of gas in Sept August: $1.058 ; September: $1.197 (max. of $1.364 ) ; October: $1.042 Sales of gas: Sept vs Sept Sales of gas: Sept vs Sept –Decrease of 5.2% (as expected by the model) Number of victims Number of victims –Even though the number of victims (killed, severely injured or slightly injured) increased for all of 2005 compared to 2004, it decreased or slightly increased in September 2005 (as expected by the model)

26 Future: what could be done Update the model to include more than 10 years Update the model to include more than 10 years –That would also allow consideration of new variables, especially laws that have come into effect since then –Another very important point would be the possibility of taking into account the difference between the severely or slightly injured victims »This information has been available since 1978

27 Other possibilities Build a model for a particular area Build a model for a particular area –Example: A very populated area like Montréal Build a model for a particular kind of road user Build a model for a particular kind of road user –Example: Pedestrians, cyclists, motorcyclists …

28 Thank you very much for your attention