Modeling national or regional road safety performance the state of the national models for belgium
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Modeling National or Regional Road Safety Performance The state of the national models for Belgium. Filip A.M. Van den Bossche IMOB - Transportation Research Institute Hasselt University Diepenbeek - Belgium. Overview. IMOB: Transportation Research Institute

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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


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


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


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)


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


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


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


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


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


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


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


Explanatory models on monthly dataModels overview


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


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


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


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


Aggregated models on yearly data

  • Example: Multivariate State space model for exposure and risk (Latent Risk Model)


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


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


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


Example outputs


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


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


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


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.


Contact

Thank you!

Filip A.M. Van den Bossche

IMOB - Transportation Research InstituteHasselt UniversityWetenschapspark 5 bus 63590 Diepenbeek – Belgium

filip.vandenbossche@uhasselt.be


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