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 performance the state of the national models for belgium

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

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


Overview1

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

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)


Overview2

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

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


Overview3

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 data objectives and modeling techniques

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 data data issues

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 data data issues1

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 data data issues2

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 data models overview

Explanatory models on monthly dataModels overview


Explanatory models on monthly data topics

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


Overview4

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

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 data1

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 data2

Aggregated models on yearly data

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


Overview5

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

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 data1

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

Example outputs


Overview6

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

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

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

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

Contact

Thank you!

Filip A.M. Van den Bossche

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

[email protected]


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