Assessing the weather conditions for urban cyclists by spatially dense measurements with an agent-based approach

Convincing commuters to use a bike is a timely contribution to reach sustainability goals. However, more than other modes of transportation, cycling is heavily influenced by the current meteorological conditions. In this study, we assess the weather conditions experienced on individual cycling routes through an urban environment and how weather observations and forecasts may give guidance to a better cycling experience. We introduce an agent-based model that simulates cycling trips in Hamburg, Germany, and a three-category traffic light scheme for precipitation, wind and temperature comfort. We use these tools to evaluate the cycling weather based on the commonly used single-station measurement approach versus spatially dense observations from an urban station network and radar measurements. Analysis of long-term data from a single station shows that most frequently discomfort is caused by temperature with a probability of 33%. Wind and precipitation discomfort occur only for about 5% of the rides. While temperature conditions can be well assessed by a single station, only one-third of critical precipitation events and less than 10% of critical wind events are captured. With perfect knowledge, temporal flexibility in start time of less than ±30 min reduces the risk of getting wet by 50%. For precipitation, nowcasting is able to predict 30% of the critical events correctly, which is significantly better than model forecasts. Operational ensemble forecast provides satisfactory guidance concerning temperature; however, the limited predictability of precipitation and wind renders these forecasts only useful for riders with a high risk-awareness and small sensitivity to false alarms.