Ny metode for beregning av effekten av fartsreduserende tiltak – eksempel SATK - Olesen, Svenn Fjeld (Proceedings from the Annual Transport Conference at Aalborg University, 2013)
Vi studerer effekten fartsendringer har på personskadeulykker. Den velkjente Powermodellen sier at en gitt prosentuell nedgang i gjennomsnittsfart gir en bestemt forventet prosentuell nedgang i ulykker, uavhengig av hva initial gjennomsnittsfart er. I en helt ny artikkel viser Elvik basert på alle tilgjengelige undersøkelser at en eksponentialmodell passer minst like bra som Powermodellen. Eksponentialmodellen viser seg å ha en helt annen egenskap, like enkel å formulere, nemlig at en gitt nominell nedgang i gjennomsnittsfart gir en bestemt forventet prosentuell nedgang i ulykker uavhengig av hva initial gjennomsnittsfart er. I denne artikkelen bruker vi konsekvent relativ risiko i forhold til fartsgrensen, eksemplifisert til 80 km/t. Alle beregninger av risiko innen en fartsfordeling og mellom fartsfordelinger før og etter fartsreduserende tiltak vil være allmenngyldige. Ved bruk av eksponentialmodellen vil den totale relative risikoen i en fartsfordeling med gjennomsnitt <img src="data:image/png;base64,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" alt="page1image2388322800" width="5.999985" height="12.500039">være . For et vilkårlig fartsintervall med gjennomsnittsfart <img src="data:image/png;base64,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" alt="page1image2387944256" width="8.999985" height="14.499960">er den relative risikoen, Rj , lik . Vi viser at det multiplikative risikobidraget fra intervallet er , der nj er antall målinger i intervallet, N er totalt antall målinger og nj/N dermed er andel målinger i intervallet. Det vil si at når vi deler opp skalaen i M ikke-overlappende og tilstøtende intervall så har vi den svært anvendelige sammenhengen <img src="data:image/png;base64,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" alt="page1image2444786080" width="215.999954" height="17.049961"> Den totale relative risikoen er produktet av et risikobidrag fra hvert intervall, hvor risikobidraget er den relative risikoen til intervallet vektet ved å opphøye i andel målinger i intervallet. Ettersom Powermodellen ikke har slike multiplikative egenskaper, har ikke denne faktoriseringen vært mulig. Dette gir oss nøyaktig informasjon om «risikoprofil» i fartsfordelinger, hva risikobidraget er blant de som kjører mellom 80 og 90 km/t, blant de over 100 km/t og så videre. Vi kan også studere bidraget fra det som defineres som ekstremhastigheter.