Despite different HIA methodologies being applied with distinctive assumptions on key parameters, AT can provide substantial net health benefits, irrespective of geographical context.

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Characteristics of users and usage of station-based car-sharing services have been discussed in various studies. First analyses of the free-floating car-sharing model DriveNow have shown that member composition and patterns of use are not very different from those of station-based car-sharing schemes. Nevertheless, free-floating car-sharing members were drawn from a new pool of travellers, they were not attracted by existing station-based car-sharing schemes. This paper goes beyond these analyses and looks not only at the usage of car-sharing services but at the overall travel behaviour of free-floating car-sharing members (FFCS). To the best of our knowledge, this is the first time that the specifics of this travel behaviour have been analysed based on substantial data that was collected specifically for this purpose with an innovative survey design based on a GPS tracking smartphone application. The goal of this study is to contrast the core group of members of the free-floating car-sharing model DriveNow (male, 25-45 years old) with people who do not use car-sharing. Key travel indicators are compared for FFCS and noncar-sharers (NCS) with a special emphasis on type and extend of multimodal travel behaviour within those two groups. The results show higher trip frequency for FFCS and differences in mode choice pattern. FFCS are more intermodal and multimodal in their behaviour. Shares of cycling are significantly higher, shares of private car trips are significantly lower for FFCS compared to NCS. The insights gained in this study can help & Johanna Kopp

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We conducted a health impact assessment (HIA) of cycling network expansions in seven European cities. We modeled the association between cycling network length and cycling mode share and estimated health impacts of the expansion of cycling networks. First, we performed a non-linear least square regression to assess the relationship between cycling network length and cycling mode share for 167 European cities. Second, we conducted a quantitative HIA for the seven cities of different scenarios (S) assessing how an expansion of the cycling network [i.e. 10% (S1); 50% (S2); 100% (S3), and all-streets (S4)] would lead to an increase in cycling mode share and estimated mortality impacts thereof. We quantified mortality impacts for changes in physical activity, air pollution and traffic incidents. Third, we conducted a cost-benefit analysis. The cycling network length was associated with a cycling mode share of up to 24.7% in European cities. The all-streets scenario (S4) produced greatest benefits through increases in cycling for London with 1,210 premature deaths (95% CI: 447-1,972) avoidable annually, followed by Rome (433; 95% CI: 170-695), Barcelona (248; 95% CI: 86-410), Vienna (146; 95% CI: 40-252), Zurich (58; 95% CI: 16-100) and Antwerp (7; 95% CI: 3-11). The largest cost-benefit ratios were found for the 10% increase in cycling networks (S1). If all 167 European cities achieved a cycling mode share of 24.7% over 10,000 premature deaths could be avoided annually. In European cities, expansions of cycling networks were associated with increases in cycling and estimated to provide health and economic benefits.