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Filip A.M. Van den Bossche

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  1. Modeling National or Regional Road Safety PerformanceThe state of the national models for Belgium Filip A.M. Van den Bossche IMOB - Transportation Research InstituteHasselt UniversityDiepenbeek - Belgium

  2. Overview • IMOB: Transportation Research Institute • Macroscopic Road Safety Models in Belgium • Explanatory models on monthly data • Aggregated models on yearly data • Subset models on yearly data • Conclusions • Future directions

  3. Overview • IMOB: Transportation Research Institute • Macroscopic Road Safety Models in Belgium • Explanatory models on monthly data • Aggregated models on yearly data • Subset models on yearly data • Conclusions • Future directions

  4. IMOB:Transportation Research Institute • Independent scientific research institute, related to Hasselt University •  40 staff members • Activities • Fundamental and applied research in transportation and road safety • Activity-based transportation models • Macroscopic and microscopic road safety research • Policy research centre for traffic safety • Educational programs in Traffic Science (Bachelor/Master program + short courses)

  5. Overview • IMOB: Transportation Research Institute • Macroscopic Road Safety Models in Belgium • Explanatory models on monthly data • Aggregated models on yearly data • Subset models on yearly data • Conclusions • Future directions

  6. Macroscopic Road Safety Models in Belgium • Approach • Three dimensions: exposure, risk, consequences • Whenever possible… • Time series analysis • Different levels of aggregation • Aggregation in time: yearly or monthly data • Subset models: per type of road, road user, accident,… • Based on Belgian data • Exposure data • Road safety data • Explanatory variables • Various models have been developed

  7. Overview • IMOB: Transportation Research Institute • Macroscopic Road Safety Models in Belgium • Explanatory models on monthly data • Aggregated models on yearly data • Subset models on yearly data • Conclusions • Future directions

  8. Explanatory models on monthly dataObjectives and modeling techniques • Objectives • Explain road safety developments • Formulate road safety forecasts • Investigate the role of exposure • Modeling techniques • BC-GAUHESEQ: Box-Cox General Autoregressive Heteroskedastic Single Equation modeling • Box-Cox transformations • Autoregressive error structure • Heteroskedasticity correction • Regression models with ARMA errors (in logs) • Autoregressive – Moving Average • State space models (in logs) • Explicit modeling of trend, slope, seasonal • ARMA Regression model with GARCH error correction mechanism • Autoregressive error structure • Heteroskedasticity correction

  9. Explanatory models on monthly dataData issues • Explanatory data • Measures of exposure • Total fuel consumption • Number of vehicles counted on highways • Prices • Fuel prices and taxes, Car maintenance, Public transport • Laws • Speed limits, Alcohol, Safety belt, Vulnerable road users • Weather Conditions • Precipitation, Temperature, Sunlight, Thunderstorm, Frost, Snow • Economic activity • Inflation, Unemployment, Net export, Car registrations, % 2nd Hand cars • Time Variables • Week / weekend days, Trend, Seasonal, Trading days • Outlier correction variables

  10. Explanatory models on monthly dataData issues • Road safety data • Number of (accidents with) persons KIL • Number of (accidents with) persons SI • Number of (accidents with) persons LI • Number of (accidents with) persons KSI • Data period • Depends on variables included • Largest range: 1974 – 2004, usually shorter • Data extensions • Calendar variables • Trading day variable  interesting effects! • Heavy traffic indicator • Measure of exposure

  11. Explanatory models on monthly dataData issues • Measure of exposure (1986-2004), based on • Monthly fuel sales (metric tons), transformed to litres • Calculated average fuel economy by fuel type based on vehicle park • Correction factor per year based on official statistics

  12. Explanatory models on monthly dataModels overview

  13. Explanatory models on monthly dataTopics • Layered structure is not always present (only in model 5) • Exposure measure • Content, Quality and Effects vary… not only in Belgium! • Without exposure, no risk… but this is no problem if prediction is the purpose (then calendar variables suffice) • Curvature of relation between road safety and exposure? • Positive and less than proportional effects, larger for lightly injured • Forecasting introduces extra difficulties • Predictions of explanatory variables are needed

  14. Overview • IMOB: Transportation Research Institute • Macroscopic Road Safety Models in Belgium • Explanatory models on monthly data • Aggregated models on yearly data • Subset models on yearly data • Conclusions • Future directions

  15. Aggregated models on yearly data • Objectives • Explore the long-term evolution in the number of fatalities • Assess quantitative long-term objectives • Less focus on explanations • Models considered • A starting point: the Oppe model • Fatalities Ft = Vt×Rt • Logistic (S-shaped) exposure Vt • Exponentially decreasing risk Rt • Extending the Oppe approach • Richards curve for exposure (S-form) • Constant term for risk • Autoregressive residuals

  16. Aggregated models on yearly data • Models considered (continued) • Alternative risk models • Exposure is treated as explanatory variable • Extra parameter for exposure • Testing laws on seat belt, speed and alcohol • Unobserved components models • Stochastic trend models • No functional (logistic, exponential) forms • Unobserved, time varying component for risk • Stochastic latent risk models • Multivariate model for exposure and fatalities • Unobserved components for exposure and risk • Natural approach towards the decomposition

  17. Aggregated models on yearly data • Example: Multivariate State space model for exposure and risk (Latent Risk Model)

  18. Overview • IMOB: Transportation Research Institute • Macroscopic Road Safety Models in Belgium • Explanatory models on monthly data • Aggregated models on yearly data • Subset models on yearly data • Conclusions • Future directions

  19. Subset models on yearly data • Objectives • Analyse road safety for a subgroup of the total aggregated number of accidents or their consequences • Still at an aggregated level, but for subsets of the system • Use of (yearly) time series • Data issues • Models on yearly data  less explanatory • Data are usually available • Interesting outputs • What is the parameter for exposure (proportionality)? • How is risk changing over time? • Show risk indices and relative risk curves • Show level and slope components in state space models

  20. Subset models on yearly data • Age and gender groups of road users • 10 ARMA regression models • Exposure = population data • Types of road users (cars, trucks, motorcycles) • Multivariate state space model • Exposure = official yearly statistics • Crashes between two types of road users • 4 multivariate latent risk models • Exposure = official yearly statistics • Types of roads (motorways, provincial, local roads) • Multivariate state space model • Exposure = official yearly statistics

  21. Example outputs

  22. Overview • IMOB: Transportation Research Institute • Macroscopic Road Safety Models in Belgium • Explanatory models on monthly data • Aggregated models on yearly data • Subset models on yearly data • Conclusions

  23. Conclusions • The models provide insight in the relation between road safety, risk and exposure in Belgium, at various levels of aggregation • The models are strategic devices, with a bird’s-eye view to the problem • Data availability and quality remain points of interest, but what we have up to now is useful • The combination of the road safety-exposure-risk triad and flexible state space modelling is promising • Road safety, risk and exposure… • The relation is changing over time, and previous results are not always valid anymore • Results depend on the length and time window of the data • Results depend on the level of aggregation or the “subsets” considered

  24. Future directions • Concerning the DRAG structure • Full DRAG model for Belgium • Application of state space methods (latent risk models) in a DRAG structure • Application of recent econometric developments in strategic road safety models • State space methods • Cointegration and Error Correction Models • Exploration of the role of exposure • How exposure is influencing frequency, severity, risk? • Exploration of shifting effects of exposure on road safety • Effect of exposure on risk? • Further elaboration of subset models • Added value of new data collection techniques for exposure • Perhaps outside time series framework

  25. References • Van den Bossche, F., Wets, G. (2003), Macro Models in Traffic Safety and the DRAG Family: Literature Review. Steunpunt Verkeersveiligheid, RA-2003-08. • Van den Bossche, F., Wets, G. (2003), A Structural Road Accident Model for Belgium. Steunpunt Verkeersveiligheid, RA-2003-21. • Van den Bossche F., Wets G., and Brijs T. (2004), A regression model with ARMA errors to investigate the frequency and severity of road traffic accidents. In: Proceedings of the 83rd Annual Meeting of the Transportation Research Board, Washington D.C, USA, January 11-15. • Van den Bossche, F., Wets, G., & Brijs, T. (2005). Role of Exposure in Analysis of Road Accidents: A Belgian case study. Transportation Research Record, 1908, 96-103. • Van den Bossche, F., Wets, G., & Brijs, T. (2005). The use of travel survey data in road safety analysis. European Transport Safety Council (ETSC) Yearbook 2005, ISBN: 90-76024-19-7, 64-75. • Van den Bossche, F., Wets, G., & Brijs, T. (2006). Predicting road crashes using calendar data. Paper presented at 85th Annual Meeting of the Transportation Research Board, Washington D.C., USA. • Hermans, E., Wets, G., & Van den Bossche, F. (2006). The Frequency and Severity of Road Traffic Accidents Studied by State Space Methods. Journal of Transportation and Statistics, 9. • Van den Bossche, F. (2006). Road Safety, Risk and Exposure in Belgium: an econometric approach (Doctoral dissertation). Diepenbeek, Belgium: Hasselt University. • Van den Bossche, F., Wets, G. and Brijs, T. (2007), Analysis of road risk per age and gender category: a time series approach. Forthcoming in Transportation Research Record.

  26. Contact Thank you! Filip A.M. Van den Bossche IMOB - Transportation Research InstituteHasselt UniversityWetenschapspark 5 bus 63590 Diepenbeek – Belgium filip.vandenbossche@uhasselt.be