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  1. 2nd European Workshop, Modelling National or Regional Road Safety Performance 30-31 May 2007, Arcueil, France Prospects for a Danish model: human factors Ivanka Orozova-Bekkevold iob@dtf.dk

  2. Prospects for a Danish model: human factors Road transport system  production system Road accident: a failure of the transport system Road accident – interaction of three principal elements • The human being (the active road user); • The vehicle; • The surroundings – infrastructure, weather, time of the day. Vehicles and infrastructure – man-made  controllable The road user  important, complex, difficult to deal with Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  3. Prospects for a Danish model: human factors Human aspects of road accidents: • Mistakes; • Lapses of attention; • Misjudgements; • Medical conditions (stroke); • Risky behaviour: speeding, drink driving etc. Main road safety tasks related to the human factor • Preventing accident occurrence (active safety) • Mitigating / Limiting the consequences (passive safety) Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  4. Prospects for a Danish model: human factors The questions we ask: Are there any specific socio-economic groups in the population who are more prone to provoke an accident than others? Can we quantify the eventual risk of these groups? The goal: Specific road safety interventions for specific groups Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  5. Prospects for a Danish model: human factors Available personal data in Denmark National Registries (all individuals above 15 years of age): • Age, Gender, Origin, Family Status, nr Kids, etc.; • Income, Profession, Employment, etc.; • Criminal record: violations, sanctions, court sentences; • Driving license registry (with some limitations); • Police records on road accidents with injury National Transport Survey (about 15,000 randomly selected) • Type and purpose of the last trip (the day before); • The means of transport used; • Travelled distance; • Road user type (driver, pedestrian, bicyclist etc); • Availability of cars in the family Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  6. Prospects for a Danish model: human factors NOTA BENE: All the personal data are anonymous! Time Span (for the moment) • Registry data: from 1993 to 2003 • Survey data: from 1992 to 2004 Wishes and possibilities for additional data: • National Hospital Registry; • Accidents recorded by the insurance companies; • Car ownership Registry; • Individual’s exposure (?) Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  7. Prospects for a Danish model: human factors What we have done until now Using only Registry data In total: 5,072,871 different individuals (aged > 15) 156, 277 were involved in road accidents from 1993 to 2003 5993 (3.8%) were in 2; 446 (0.3%) were in 3 or more accidents For about 1,881,000 (~37%) we were able to trace the parents (family history on accidents, crime etc.) Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  8. Prospects for a Danish model: human factors Some descriptive statistics Age and number of accidents of active road users* (passengers excluded): In total 150,264 individuals (86.57% of all in accidents) Nr Acc Nr Obs Mean Age STD 1 143,583 38.08 17.96 2 5,933 34.53 14.85 3 405 31.85 12.46 4 50 32.08 11.88 5 5 31.00 7.44 * Was an active road user in all accidents Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  9. Prospects for a Danish model: human factors Some descriptive statistics Age and number of accidents of passengers* In total 23,320 individuals (13.43% of all in accidents) Nr Acc Nr Obs Mean Age STD 1 22,925 31.00 22.22 2 193 27.08 14.56 3 3 21.33 2.08 * Was passenger in all accidents Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  10. Prospects for a Danish model: human factors Some descriptive statistics Number of accidents and employment Minors Unemployed Other Employed (< 18 y) (St/Pens/Leave) (% of pop) 0.6 4.0 36.3 59.2 Nr Acc Number (% of the group) 1 848 (0.6) 5,707 (4.1) 39,755 (28.4) 93,661 (67.0) 2 83 (1.5) 250 (4.4) 1,522 (26.6) 3,858 (67.5) 3 8 (2.1)21 (5.6) 132 (35.2) 214 (57.1) 4 3 (6.5)6 (13.)20 (43.5) 1 5 17 (37.0) 4 Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  11. Prospects for a Danish model: human factors Some descriptive statistics Number of accidents and Income (average over FUP) Nr Acc Nr Obs Mean (DKK) 1 139,967 199,135 2 5,713 200,259 3 375 174,498 4 46 140,423 5 5 265,547 All 146,110 199,099 Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  12. Prospects for a Danish model: human factors Modelling: !!! TENTATIVE !!! VERY FIRST TRY !!! Assumption Since only people above 15 years of age are considered, who supposedly move independently in the traffic, I assume they all are equally exposed to the risk to be involved in a road accident. The responsibility for the accident is not investigated here, neither the type of road user (active, passive), but only the odds to be in an accident. In this case, INVOLVEMENT = REGISTRATION in the police accident reports Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  13. Prospects for a Danish model: human factors Modelling: !!! TENTATIVE !!! VERY FIRST TRY !!! Model: Multivariate logistic regression; “Static” (any time dependency not considered for the moment) The person as whole – Max Education, Max Income over FUP Independent variables (classes): Gender, Origin, Income, Education, Criminal behaviour, [Parents – Crime, Accidents] Dependent variable (binary): being involved in accident (yes/no) at least once during the study period. Accident risk: expressed as Odds Ratio (O.R.) Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  14. Prospects for a Danish model: human factors Modelling Groups according to: • Gender [M ; F]; Parent (having kids) [yes,no] • Max Education: Basic (9 y), medium (12 y), bachelor, master, unknown • Origin: Dane, Non-Dane, Descendent (born in DK); • Max Income: low (< 0.5*mean), average, high (> 2*mean); • Criminal behaviour: none, only traffic-related, only non-traffic, both traffic and non-traffic; • [Parental involvement in accidents & crimes: yes/no] Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  15. Prospects for a Danish model: human factors Modelling: !!! TENTATIVE !!! VERY FIRST TRY !!! Test A: Without information on parental involvement in Acc & Crime Independent variables: Gender, Origin (3 groups), Crime Type (4 levels), Max Income (3 levels), MaxEdu (5 levels), Parent (yes/no) Nr of records with all non-missing values: 4,999,304 (98% of all) Test B: With information on parental involvement in Acc & Crime Independent variables: Gender, Origin (3 groups), Crime Type (4 levels), Max Income (3 levels), MaxEdu (5 levels), Parent (yes/no), MotherAcc (yes/no), MotherCrime(yes/no), FatherAcc(yes/no), FatherCrime(yes/no) Nr of records with non-missing values: 1,880,998 (37% of all) NB: Here many non-Danes and old people are excluded, because info on their parents is missing Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  16. Prospects for a Danish model: human factors Preliminary results: !!! VERY FIRST TRY !!! TEST A (n=4,999,304)TEST B (n=1,880,998) Variable O.R. 95% CI O.R. 95% CI Gender: M vs F 1.362 1.35-1.381.360 1.34-1.38 Origin: Non vs DK 0.765 0.75-0.78 0.824 0.78-0.87 Desc vs DK 0.967 0.91-1.03 ns 0.826 0.77-0.89 CrimType : Road vs Non 4.564 4.50-4.63 3.622 3.55-3.69 Crime vs No 2.255 2.21-2.30 2.011 1.96-2.06 Road+Cr vs No 8.199 8.06-8.34 6.769 6.62-6.92 Income: Low vs Middle 0.726 0.71-0.74 0.769 0.75-0.79 High vs Middle 0.721 0.71-0.73 0.689 0.67-0.71 MaxEdu: Unkn vs Middle 0.679 0.66-0.70 1.228 1.15-1.32 Basic vs Middle 1.132 1.12-1.151.199 1.18-1.22 Bache vs Mid 0.944 0.93-0.96 0.867 0.85-0.89 Master vs Mid 0.868 0.85-0.89 0.784 0.76-0.81 Parent: Yes vs No 0.769 0.76-0.78 0.759 0.74-0.78 MotherAcc: Yes vs No 1.715 1.64-1.79 MotherCrim: Yes vs No 1.081 1.05-1.11 FatherAcc: Yes vs No 1.341 1.30-1.39 FatherCrim: Yes vs No 1.133 1.11-1.53 Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  17. Prospects for a Danish model: human factors Data mining test: Alternate Decision Tree Sample with parental information: 1,880,998 persons 10% persons extracted randomly. Used variables: 18 in total (FU; Origin; CrimeTYPE; MaxEdu; Income; Gender; Parent; Mother&Father-ACC,Crime; FRAK(y/n); nr Sanctions for: FERDALK, FERDVEL,FERDGEN,SERLOV, STRAFLOV,UOPLLOV The proportions of the classes (Accident & Crime, Gender etc.) is preserved. Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  18. Prospects for a Danish model: human factors Data mining test: Alternate Decision Tree No initial hypothesis Input file: 188,100 obs; 18 variables Response variable (been in accident): FU (yes=1, no=0) 66% of the data – for training, 34% of the data – for testing Results: 95.53% - correctly classified Mean abs error: 0.186 RMS error: 0.233 Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  19. Prospects for a Danish model: human factors -1.541 (negative: FU=0; positive: FU=1) Data mining test: Alternate Decision Tree CrimeType MotherAcc MaxEdu None Crime Any Crime yes no Basic Other FU=0 (-0.275) FU=1 (0.476) FU=1 (0.340) FU=0 (-0.009) FU=1 (0.056) FU=0 (-0.026) Gender NrTrafGeneric FRAK Income FatherCrime M K 0 >0 yes no Middle Other yes no FU=1 (0.093) FU=0 (-0.086) FU=0 (-0.179) FU=1 (0.332) FU=1 (0.386) FU=0 (-0.105) FU=1 (0.021) FU=0 (-0.094) FU=1 (0.078) FU=0 (-0.024) CrimeType NrTrafAlcohol Both: Traffic+Other Other than both 0 > 0 FU=1 (0.224) FU=0 (-0.028) FU=0 (-0.025) FU=1 (0.526) Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  20. Prospects for a Danish model: human factors Final remarks - 1 For the moment, the models presented here should be considered preliminary; they must be refined. I just wanted to show what data are available and what can be done. Data mining can be used as prescreening and hypothesis generating tool. Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  21. Prospects for a Danish model: human factors Final remarks -2 In order to account for the human factor in road safety, other approaches and techniques (data mining  pattern recognition) could and should be combined with econometric modelling. The human factor can be modelled also numerically. The DK register data  rich possibilities for cross-sectional & longitudinal studies, as well as time-series analysis. Register data can be linked easily to survey data. Ivanka Orozova-Bekkevold, DTF, Denmark [ Topic – slide to proper position ]

  22. Thank you for your attention! Ivanka Orozova-Bekkevold iob@dtf.dk