Why is it so difficult to explain the decline in traffic fatalities?

In many highly motorised countries, the number of traffic fatalities has gone down by about 80 percent since the peak number, which was reached around 1970. What explains this decline? Is it principally the result of road safety policy, or have other factors made a larger contribution? This paper argues that it is difficult to give a scientifically rigorous explanation of the decline in traffic fatalities. There are five main problems: (1) There are very many potentially relevant explanatory variables. (2) Some of the relevant explanatory variables change slowly at an almost constant rate. (3) Data are incomplete or missing about many potentially relevant variables. (4) Some variables are affected by measurement errors or discontinuities in time series. (5) Many of the explanatory variables are very highly correlated with each other and with time. These problems are illustrated using Norway as an example. It is shown that the problems listed above can result in models that are non-sensical although they pass formal tests of model quality. The lesson is that one should never judge how good a model is merely in terms of formal criteria. Some strategies for developing more meaningful models are discussed

Why is it so difficult to explain the decline in traffic fatalities?

In many highly motorised countries, the number of traffic fatalities has gone down by about 80 percent since the peak number, which was reached around 1970. What explains this decline? Is it principally the result of road safety policy, or have other factors made a larger contribution? This paper argues that it is difficult to give a scientifically rigorous explanation of the decline in traffic fatalities. There are five main problems: (1) There are very many potentially relevant explanatory variables. (2) Some of the relevant explanatory variables change slowly at an almost constant rate. (3) Data are incomplete or missing about many potentially relevant variables. (4) Some variables are affected by measurement errors or discontinuities in time series. (5) Many of the explanatory variables are very highly correlated with each other and with time. These problems are illustrated using Norway as an example. It is shown that the problems listed above can result in models that are non-sensical although they pass formal tests of model quality. The lesson is that one should never judge how good a model is merely in terms of formal criteria. Some strategies for developing more meaningful models are discussed

Hva kan forklare nedgangen i antall drepte eller hardt skadde i trafikken i Norge etter 2000? Artikel

Fra 2000 til 2019 gikk antall drepte eller hardt skadde i trafikken i Norge ned med mer enn 50 %. Antall drepte gikk ned med nesten 70 %. Mange faktorer har bidratt til nedgangen. De tre viktigste er tiltak på vegnettet, sikrere biler og lavere fart. Økt bruk av bilbelter, økt bruk av automatisk trafikkontroll og økt bruk av sykkelhjelm har også bidratt. Nedgangen i antall drepte eller hardt skadde fra 2000 til 2019, beregnet med en trendlinje, var på 800 personer (fra 1479 til 679). Til sammen kan de faktorer det er mulig å beregne virkninger av, forklare 59 % av denne nedgangen. Det vil si at antall drepte eller hardt skadde i 2019 ville ha vært 1149 (i stedet for 679) dersom disse faktorene ikke hadde bidratt. Det har ikke lykkes å forklare hele nedgangen i antall drepte eller hardt skadde. Andre faktorer enn dem denne studien har identifisert, må derfor også ha bidratt til den gunstige utviklingen. Det kan ikke utelukkes at rapporteringen av hardt skadde i trafikken er redusert i den perioden studien omfatter

A comprehensive and unified framework for analysing the effects on injuries of measures influencing speed

This paper proposes a comprehensive and unified framework for analysing the impacts on traffic injury of measures influencing speed. The key tool for analysis is a specification of the speed distribution, which in most cases closely approximates a standard normal distribution. The speed distribution can be represented, for example, by twelve intervals each comprising one half standard deviation. The exponential model of the relationship between speed and the number of injured road users is applied to estimate the expected injury rate for drivers travelling at the mean speed of any part of the distribution. The relationship between individual driver speed and accident involvement is then incorporated into the speed distribution. A speed distribution specified this way represents both the mean speed of traffic and the variation in speed-related risk between drivers. Impacts of changes in speed that can be modelled include: (1) Shifting the whole speed distribution, (2) Compressing the upper end of the speed distribution, (3) Enlarging or reducing the variance of the speed distribution, (4) Selective changes in specific regions of the speed distribution. Examples are given of how knowledge of the impacts of measures on speed can be translated into expected changes in the number of injured road users by relying on the analytic framework.acceptedVersio

Which is the more important for road safety— road design or driver behavioural adaptation?

Elvik, R. (2022). Which is the more important for road safety—road design or driver behavioural adaptation?. Traffic Safety Research, 2, 000009.Studies consistently show that sharp horizontal curves increase accident rate. One would therefore expect roads with many sharp curves to have a higher accident rate than roads with few sharp curves. This is not the case. The differences in road safety between roads with different profiles of horizontal road alignment are quite small. There are even studies suggesting that areas having roads with many curves have a lower number of accidents than otherwise identical areas with less curvy roads. The question arises: How can it be true both that sharp curves increase accident rate and that areas with roads with many sharp curves do not have a higher accident rate than areas with less demanding alignment? The answer is likely to be found in behavioural adaptation among drivers. The accident rates both in curves and on straight sections are strongly influenced by how drivers adapt behaviour to the number of curves per kilometre of road. This paper shows how behavioural adaptation can be quantified by means of the ‘human feedback parameter’ proposed by Evans. This parameter takes a value of -1 if drivers adapt behaviour so as to completely eliminate a risk factor. Values close to -0.7 for horizontal curves were estimated on the basis of micro-level studies. Thus behavioural adaptation reduces the increase in risk to about 30% of what it would have been without behavioural adaptation. In addition, a high frequency of curves leads to lower speed on the straight sections between curves.publishedVersio

The more (sharp) curves, the lower the risk

The risk of accident in horizontal curves is a complex function of at least the following characteristics of the curve: the radius of the curve; the length of the curve (and the resultant deflection angle); the presence of a spiral transition curve; the super-elevation of the curve; the distance to adjacent curves; and whether the curve is on a flat road, a straight gradient or a vertical curve. The interactions between these characteristics in determining accident risk in horizontal curves is only beginning to be understood. This paper summarises the results of studies that have investigated the interaction between the radius of a horizontal curve and the distance to adjacent curves. The shorter the mean distance between curves, the lower is the increase in risk for a given curve radius. The sharper neighbouring curves are, the lower is the increase in risk for a given curve radius. Thus, overall risk may not be higher on a road consisting mostly of sharp curves than on a road consisting mostly of straight sections with a few curves located far apart from each other.acceptedVersio

Can the impacts of connected and automated vehicles be predicted? : Artikel

A huge research effort is going on in order to develop connected and automated vehicles. Small-scale trials of automated vehicles in real traffic are already taking place. Can the societal impacts of a transition to fully connected and automated vehicles be predicted? This question has been studied in the Horizon2020 project Levitate. To predict the impacts of connected and automated vehicles, one must first identify and describe potential impacts. A list of 33 potential impacts, classified as direct, systemic and wider was developed. A survey was made of methods that can be applied in order to quantify and predict these impacts. Not all potential impacts can be predicted with any confidence. There is, first of all, large uncertainty about when and how long the transition to connected and automated vehicles will be. It is also impossible to predict some potentially quite important impacts, e.g. whether the transition to automation will be associated with a transition to various forms of shared mobility or whether individual ownership and use of vehicles will continue at present levels. Another important aspect which is difficult to predict is whether automated vehicles will continue to have internal combustion engines or be electric or based on fuel cells. Several methods must be applied to predict the impacts of connected and automated vehicles. As far as impacts on traffic operations are concerned, various forms of traffic simulation have been widely applied. Broadly speaking, connected and automated vehicles are expected to lead to increased road capacity, fewer accidents and less emissions. Increased road capacity may in turn generate induced travel demand, which to some extent will fill up the new capacity. Road safety is likely to be improved, but there is large uncertainty about how non-automated road users and automated vehicles can interact in ways that maintain current safety levels or, preferably, improve safety

Can the impacts of connected and automated vehicles be predicted?

A huge research effort is going on in order to develop connected and automated vehicles. Small-scale trials of automated vehicles in real traffic are already taking place. Can the societal impacts of a transition to fully connected and automated vehicles be predicted? This question has been studied in the Horizon2020 project Levitate. To predict the impacts of connected and automated vehicles, one must first identify and describe potential impacts. A list of 33 potential impacts, classified as direct, systemic and wider was developed. A survey was made of methods that can be applied in order to quantify and predict these impacts. Not all potential impacts can be predicted with any confidence. There is, first of all, large uncertainty about when and how long the transition to connected and automated vehicles will be. It is also impossible to predict some potentially quite important impacts, e.g. whether the transition to automation will be associated with a transition to various forms of shared mobility or whether individual ownership and use of vehicles will continue at present levels. Another important aspect which is difficult to predict is whether automated vehicles will continue to have internal combustion engines or be electric or based on fuel cells. Several methods must be applied to predict the impacts of connected and automated vehicles. As far as impacts on traffic operations are concerned, various forms of traffic simulation have been widely applied. Broadly speaking, connected and automated vehicles are expected to lead to increased road capacity, fewer accidents and less emissions. Increased road capacity may in turn generate induced travel demand, which to some extent will fill up the new capacity. Road safety is likely to be improved, but there is large uncertainty about how non-automated road users and automated vehicles can interact in ways that maintain current safety levels or, preferably, improve safety.Det pågår omfattende forskning med sikte på å utvikle selvkjørende biler. Forsøk i virkelig trafikk i mindre skal finner allerede sted. Kan man forutsi de samfunnsmessige virkninger av selvkjørende biler? Dette spørsmålet står sentralt i det pågående Horizon2020 prosjekt Levitate, som denne artikkelen bygger på. Det er utarbeidet en liste over mulige virkninger av selvkjørende biler. Til sammen 33 mulige virkninger ble identifisert. Mulighetene for å kvantifisere og predikere virkningene ble undersøkt. Det er ikke mulig å predikere alle virkninger. For det første vet man ikke når de selvkjørende biler kommer på markedet og hvor lang tid det vil ta før de erstatter biler med fører. For det andre vet man ikkde om selvkjørende biler stort sett vil bli benyttet individuelt, eller om det vil skje en overgang til ulike former for delemobilitet. For det tredje vet man ikke om de selvkjørende biler vil ha forbrenningsmotor eller bli elektriske. For de virkninger der tallfesting og prediksjon er mulig, har en rekke mdetoder vært benyttet. Ulike former for trafikksimulering er en vanlig metode foir å studere de trafikale virkninger av selvkjørende biler. Det hersker enighet om at selvkjørende biler vil kunne utnytte vegkapasiteten bedre og redusere antall trafikkulykker og forurensende utslipp. Det er fortsatt stor usikkerhet knyttet til hvordan selvkjørende biler kan samhandle med fotgjengere og syklister

Driver mileage and accident involvement: A synthesis of evidence

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).The relationship between driver mileage and accident involvement has been a controversial topic for at least 20 years. The key issue is whether driver accident involvement rate increases in proportion to miles driven or has a non-linear relationship to miles driven. This paper presents a synthesis of evidence from studies of how the number of accidents per driver per unit of time relates to distance driven in the same period. Most studies of this relationship are methodologically weak and their results highly inconsistent and potentially misleading. Unre liable data and poor control for confounding factors characterise most studies. Only a few studies based on multivariate statistical models control for at least some of the confounding factors that may influence the relationship between distance driven and accident involvement. These studies consistently show that the number of accidents per driver per year increases less than in proportion to distance driven. A good approximation is that the number of accidents per driver per unit of time is proportional to the square root of distance driven. Potential methodological and substantive explanations of this finding are discussed.publishedVersio

Book reviews [1979, Vol. 6, no. 1]

Books reviewed are: Stephen A. Zeff, (ed.), Asset Appreciation, Business Income and Price-Level Accounting: 1918-1935 Reviewed by Louis Goldberg; The Chartered Accountant in Australia, Golden Jubilee Issue Reviewed by Robert H. Raymond; Rex Winebury, Thomson McLintock and Co. - The First Hundred Years Reviewed by J. C. Lehane; Bryce Lyon and A. E. Verhulst, Medieval Finance: A Comparison of Financial lnstitutions in Northwestern Europe Reviewed by Ernest Enke; Christiane Pierard, Les Plus Anciens Comptes De La Ville De Mons (1279-1356). Tome 1 Reviewed by Frederic M. Stiner, Jr.; Osamu Kojima, Studies in the Historical Materials of Accounting Reviewed by Kohhei Yamada; Reviewed by Kohhei Yamada Reviewed by Rosita S. Chen; Francis E. Hyde, Cunard and the North Atlantic 1840-1973, A History of Shipping and Financial Management Reviewed by Maureen H. Berry; Alfred Robert Roberts, Robert H. Montgomery: A Pioneer Leader of American Accounting Reviewed by Dale A. Buckmaster