Some people feel the rain, others just get wet: An analysis of regional differences in the effects of weather on cycling

Abstract

Between cities and regions, not only cycling levels differ, but also the reactions of cyclists to adverse weather conditions. Using data from 122 automated bicycle counting stations in 30 German cities, and a composite index of adverse weather conditions that consists of air temperature, precipitation, wind speed, relative humidity, and cloud coverage, we calculate city-specific weather elasticities of the level of utilitarian cycling. The results show that these weather elasticities vary significantly between cities. Our next step is to analyze various determinants of weather elasticities, which reveals that the share of young inhabitants and the density of the cycle network have a positive impact on weather resilience. Based on the notion that resilience to adverse weather conditions reflects a revealed part of a city's bicycle culture, the weather elasticities can be used to create a ranking of bicycle cities. This ranking is positively correlated with a ranking based on the modal share of cycling, as well as with other rankings based on stated preference surveys or external conditions such as infrastructure or cycling safety. JEL: R49.

Analyzing the Behavior and Growth of Cycling in Four North American Cities Before, During, and After the COVID-19 Pandemic

“…The thresholds were calculated to be at 28°C, 28°C, 30°C, and 27°C in Montreal, NYC, Seattle, and Austin, respectively ( 25 – 28 ). This indicates that the magnitude of the weather effects on cyclist demand will vary from city to city, as also noted by Goldmann and Wessel ( 29 ). The magnitude of the effect also varies depending on the use of the bike route.…”

“…They then added each site’s classification as an independent categorical variable in a regression model of bike counts; this was not found to be significant. When determining the influence of weather variables, studies have used regression models such as linear and loglinear ( 24 , 29 ), square root ( 32 ), loglinear in absolute and relative models ( 25 ), and negative binomial with log identity ( 23 , 33 ). Although those models dealt with the nonlinear relationship of temperature by using squared terms, and of precipitation by categorizing that variable, other studies have opted to use general additive models ( 26 , 28 , 33 ) that, instead of assigning a constant coefficient, determined the influence of each variable by function.…”

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“…I am not the first to hypothesise that the responsiveness of mobility behaviour to climate and weather may differ across space. Most notable are two recent studies by Böcker et al (2019) and Goldmann and Wessel (2021). Böcker et al (2019) find heterogeneous responses to weather and daylight on mobility behaviours in four urban areas in the Netherlands (Utrecht), Norway (Oslo and Stavanger) and Sweden (Stockholm).…”

“…Paternoster et al, 1998). Goldmann and Wessel (2021) analyse data from bicycle counting stations in German cities and study the effect of an index for adverse weather conditions on cycling counts. Findings reveal substantial geographic variation: that the elasticity of adverse weather differs between cities.…”

Weather and exercise: A comparative review and the role of geographers

“…weather station data, journey counts) to model the relationship between journeys and weather parameters (e.g. An et al., 2019; Goldmann & Wessel, 2020; Thomas et al., 2013). Others bring together mixed methods (surveys, diaries, interviews, questionnaires) and weather data (e.g.…”