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Although several papers have shown that bike ridership is affected by actual weather conditions, this is the first study to comprehensively investigate the impact of forecasted weather conditions on bike ridership. The results show that both actual and forecasted weather conditions can be used as useful explanatory variables for predicting bicycle usage. Even incorrect weather forecasts can impact on bike ridership, which underlines the importance of weather forecast effects for traffic planners; for example, forecasted rain can reduce bike traffic by 3.6% in periods that turn out to be rain-free. Additionally, a digital image-processing method is used to calculate the darkness of the cloud coverage displayed on weather forecast maps. The results imply that bike ridership is significantly smaller in regions with darker forecasted clouds. It is also shown that weather forecasts have a stronger impact on recreational bike traffic than on utilitarian traffic. Furthermore, various lagging and leading effects of rain forecasts are outlined. Morning rain forecasts can, for example, reduce bike ridership in midday and afternoon hours that were predicted to be rain-free. To derive these results, hourly bicycle counts from 188 automated counting stations in Germany are collected for the years 2017 and 2018. They are linked to actual weather data from Germany's National Meteorological Service and with historical weather forecasts that are deduced from weather maps of Germany's most-watched television news program. Log-linear and negative binomial regression models are then used to estimate the weather forecast effects. [ABSTRACT FROM AUTHOR]

Customization design is a trend for developing a bicycle in recent years. Thus, the comfort of riding a bike is an important factor that should be paid much attention to while developing a bicycle. From the viewpoint of ergonomics, the concept of “fitting object to the human body” is designed into the bicycle frame in this study. Firstly, the important feature points of riding posture were automatically detected by the image processing method. In the measurement process, the best riding posture was identified experimentally, thus the positions of feature points and joint angles of human body were obtained. Afterwards, according to the measurement data, three key points: the handlebar, the saddle and the crank center, were identified and applied to the frame design of various bicycle types. Lastly, this study further proposed a frame size table for common bicycle types, which is helpful for the designer to design a bicycle. [ABSTRACT FROM AUTHOR]