Stated choice studies have been applied regularly to the valuation of time savings and other attributes of travelling as perceived by individuals. In such experiments, respondents often provide reference levels for the attributes and the hypothetical choices presented to them are pivoted around actual behaviour. However, most individuals are not able to provide reference levels for the number of casualties on the road they travel. Thus, if valuation of this important element is attempted, it is the researcher who must provide casualty risk reference levels to the respondents. Some studies have applied route choice experiments including a safety attribute but the majority have been limited to only one particular road section with a common baseline risk for all respondents.This study discusses the setting up and results of a more generalized route choice experiment including a safety attribute. Respondents provided, at an initial stage, their travel times and costs related to a recent trip by car. Then, expected numbers of casualties for different trip lengths were calculated based on travel distances and traffic densities. So, the calculated number of severe injuries and fatalities (casualties) per year, on the road section the respondent had travelled, entered as a third attribute in the choices, together with the reported travel times and costs. Route choice was analysed using multinomial logit and mixed logit models. From the latter models we obtained point estimates for the value of the statistical life ranging from € 7.3 million to € 19.1 million.

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Knowing the speed at which a cyclist travels is important in route and mode choice modelling. Empirical evidence suggests that it varies significantly in accordance with-among other things-infrastructure and topology. Despite this, in many networkbased transport models cycling speed is constant, making travel distance the predominant variable of cycling behavior. Motivated by the lack of a comprehensive speed model in the literature, we present models for bicycles and e-bikes estimated based on a large-scale collection of GPS data in the Oslo area. In the models, speed on a network link is described as a function of several characteristics of the infrastructure and topology, and differs by user segments such as gender, trip purpose and type of bicycle. Model parameters are estimated with regression models using data from close to 50 000 single cycling trips. The data indicate that, on average, men cycle at a faster rate than women, although the difference is significantly less in the case of e-bikes. There is a non-linear and non-monotonic relationship between speed and gradient, with speed increasing up to a gradient of-6%, but decreasing thereafter most likely due to safety concerns. Notable is the fact that cycling speed is significantly higher on routes where cyclists and pedestrians have their own dedicated space.

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