# MORE EFFECTIVE ROAD SAFETY WORK WITH PROPER TOOLS

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3 Peltola, H & Rajamäki, R. 17th IRF World Meeting & Exhibition 3/12 FIGURE 1 Evaluation of traffic safety effects of road improvements, using TARVA (Peltola et al., 2013a). 1.2 Practical use of the tool The databases for all the TARVA versions are updated yearly. Hence, up-to-date prediction of expected numbers of accidents for any part of the network is always available and safety effects can be estimated using the latest information. The minimum input needed for estimation of safety effect is (1) what the measures are and (2) where they are implemented. There are almost 100 predetermined measures in the both highway safety evaluation versions. Additionally the user can define his/her own measures, provided reliable CMF estimates are available. The results of the calculations include (1) the expected safety situation on the modified network if no measures are implemented and (2) the safety effects of selected improvements (yearly injury accidents and fatalities). Estimates of yearly-avoided injury accidents and fatalities caused by road improvements are used to calculate the benefit in accident costs. Because the tool estimates the costs of measures as well, results can be used to calculate what kinds of measures are most effective regarding safety, and where those measures pay off most effectively. Implementation costs can be entered while performing the evaluations, but the average costs for measures (per km or per measure) are used if these values are not entered. A more thorough description of the practical calculations is presented by Peltola et al. (2013a). 2 WHY TOOLS WITH PROPER ACCIDENT PREDICTIONS ARE SO IMPORTANT To be able to implement cost-effective road safety improvements, reliable safety ranking must be performed in order to allocate the measures optimally. Additionally, proper safety estimates for alternative road improvements are needed and these must be based on expected number of accidents if no measures are implemented. While preparing the evaluation tool we found out, that huge estimation errors were done especially in estimating the current safety situation.

8 Peltola, H & Rajamäki, R. 17th IRF World Meeting & Exhibition 8/12 Basic safety figures: Roads, AADT, Mileage, Casualty accidents Fatalities Severity km veh/day Mkm/y /year rate 2) density 2) /year rate 2) density 2) Killed/100 acc. Comparison 1) ,7 21,6 10,1 35,3 4,90 2,29 22,7 Study ,2 27,4 12,8 39,8 5,52 2,58 20,2 Distributon of accidnts among accident type Casualty accidents (number/5 years) Fatalities (number/5 years) Ped Bic Coll Over Other All Ped Bic Coll Over Other All Comparison 1) Study Difference, % 11 % 55 % 35 % 17 % 24 % 27 % 9 % 54 % 27 % -11 % -13 % 13 % 40 Casualty accident rate 35 Casualty acc/100 M veh km Killed /100 M veh km Pedest Bicycle Collision Overturn Obstacle Other Stat veh Animal Comparison 16,8 8,8 34,0 23,0 13,4 3,0 0,5 0,5 Study 14,7 10,8 36,2 21,3 13,1 2,9 0,4 0,6 Fatality rate Pedest Bicycle Collision Overturn Obstacle Other Stat veh Animal Comparison 25,5 9,5 33,3 16,0 12,6 2,3 0,5 0,3 Study 24,6 13,1 37,7 12,6 10,6 1,5 0,0 0,0 The list below shows which areas are included in the study and which ones are in the comparison group. HINT: To change study and comparison areas, add/remove checks on the Data_Study and Data_comparison -sheets for the filter of cell B2. Study Alytaus - Kauno - Klaip dos - Marijampol s Panev žio - Šiauli - Taurag s - Telši - Utenos - Vilniaus - Comparison Alytaus Kauno Klaip dos Marijampol s - Panev žio Šiauli Taurag s Telši Utenos Vilniaus FIGURE 3 Summary of the comparison of Lithuanian road districts ( ), Marijampol s vs. rest of Lithuania (Peltola et al., 2013b).

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