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.

尽管有几篇论文表明自行车骑行会受到实际天气条件的影响，但这是首次全面研究预测的天气条件对自行车骑行的影响的研究。结果表明，实际和预测的天气状况都可以用作预测自行车使用情况的有用解释变量。甚至错误的天气预报也会影响骑车人，这突显了天气预报对交通规划人员的重要性；例如，在没有雨的时期，预计的降雨会导致自行车交通量减少3.6％。另外，使用数字图像处理方法来计算在天气预报地图上显示的云覆盖的黑暗度。结果表明，在乌云密布的地区，骑自行车的人数明显减少。研究还表明，天气预报对休闲单车交通的影响比对实用车的影响更大。此外，还概述了降雨预报的各种滞后和主要影响。例如，早上的降雨预报可以减少中午和下午的无雨骑车活动。为了得出这些结果，收集了2017年和2018年德国188个自动计数站的每小时自行车计数。这些计数与德国国家气象局的实际天气数据以及从德国最大的天气图推导出的历史天气预报相关联观看的电视新闻节目。然后使用对数线性和负二项式回归模型估算天气预报效果。