Stefan Flügel

Cycling and walking for transport: Estimating net health effects from comparison of different transport mode users' self-reported physical activity

BackgroundThere is comprehensive evidence of the positive health effects of physical activity, and transport authorities can enable this by developing infrastructure for cycling and walking. In particular, cycling to work or to school can be a relatively high intensity activity that by itself might suffice for maximum health gain. In this paper, we present estimates of net health effects that can be assumed for demand responses to infrastructure development. The estimation was based on comparing current cyclists/pedestrians against potential cyclists/pedestrians, applying the international physical activity questionnaire, which is a survey-based method for estimating metabolic equivalent task levels from self-reported types of physical activity, and their frequency, duration and level of intensity (moderate or vigorous).. By comparing between shares of individuals with medium or high intensity levels, within the segments of current cyclists/pedestrians and potential cyclists/pedestrians, we estimate the possible net health effects of potential new users of improved cycling/walking infrastructure. For an underpinning of the estimates, we also include the respondents assessments of the extent to which cycling/walking for transport replaces other physical activity, and we carry out a regression of cycling/walking activity levels on individual characteristics and cycle/walk facility features.ResultsThe estimated share of new regular cyclists obtaining net health gains was ca. 30%, while for new regular pedestrians this was only ca. 15%. These estimates are based on the assumption that the new users of improved cycle/walk facilities are best represented by self-declared potential users of such improved facilities. For potential cyclists/pedestrians, exercise was stated as the main motivation for physical active transport, but among current regular cyclists fast and flexible was just as important as exercising. Measured intensity levels from physically active transport increased with separate cycling/walking facilities, and were higher for those with higher education and living in urban areas, while they were lower for those with higher BMI and higher age.ConclusionsSince the share obtaining net health gains might have a huge impact on cost-benefit analysis of new or improved infrastructure for cyclists/pedestrians, it is of importance to estimate this share. A main limitation of our estimation is the cross-sectional design. There is a need for more case studies combining surveys and objective measurement of physical activity changes, preferably before and after the construction of new infrastructure.

Valuation of Cycling Facilities with and without Controlling for Casualty Risk

Barrier effects can impact cyclists’ travel time, level of comfort, and risk of accidents. When eliciting the valuation of these elements, simultaneous estimation is called for because the perceived level of comfort may depend on the accident risk. In this paper we present the results of a choice experiment in which cyclists traded off cycling time, separated tracks, intersections, and, in one additional choice experiment, casualty risk. We find that the utility of the two barrier-reducing attributes is almost halved when controlling for accident risk. We also translate the utility to a monetary scale, making the results applicable for cost-benefit analysis.

What Factors Affect Cross-modal Substitution? – Evidences From The Oslo Area

The vast majority of studies on urban travel demand focus on the effect on the demand of one travel mode given a change in the characteristics of that same transport mode, for example, own-elasticities. comparatively little is known about cross-elasticities of demand. In particular, there is a need for a better understanding of the underlying mechanisms of modal substitution, that is a better understanding of cross-modal diversion factors (Dfs) defined as the proportion of people who leave mode a and switch to mode b. The purpose of this article is to investigate what factors explain variations in Dfs across transport modes, submarkets and policy measures. using a recently developed empirical travel mode choice model for the Oslo area, we simulate over 10,000 different Dfs by systematically changing the underlying transport modes, submarkets and policies (size, direction and type of change). With descriptive statistics, we show how the Dfs vary on a general level. most results are immediately intuitive, for example that car drivers mostly substitute to walk for short-distance trips but that those Dfs diminish rapidly with increasing distance. Interestingly, we find rather high Dfs across different forms of public transportation. With successive regression analyses we show that the number of available alternatives and relative market shares significantly affect Dfs.

A comparison of bus passengers’ and car drivers’ valuation of casualty risk reductions in their routes

INTRODUCTIONnThe economic value of safety represents an important guide to transport policy, and more studies on individuals valuation of road safety are called for. This paper presents a stated preference study of the value of preventing fatal and serious injuries involving bus passengers and car drivers in road accidents.nnnOBJECTIVESnFormer valuation studies based on travel behaviour and route choice have involved primarily car drivers. Our study also included bus passengers, thus providing a comparison of two types of transport mode users. Moreover, the comparison was based on two different valuation methods.nnnMETHODOLOGYnAbout 600 bus passengers and nearly 2300 car users from different areas of Norway reported a recent trip, described by its distance and travel cost. Then they answered stated choice tasks that took a reference in the reported trip and involved trade-offs among travel time, fatal and seriously injured victims and travel costs. Afterwards, they faced a simple trade-off between travel costs, and fatal and seriously injured victims.nnnFINDINGSnPooling the data from the two stated preference formats, we derived values of a statistical life and of a statistical seriously injured victim. Regarding the value of statistical life, our point estimates were NOK 45.5 million and NOK 58.3 million for bus users and car users respectively.nnnDISCUSSIONnThe point estimates for bus passengers and car users were not statistically different given their confidence intervals. Thus, we recommend the use of a single value, identical for both modes of transport, for the prevention of a statistical fatality as well as for a statistical injury.