  Shifting to cycling in urban areas reduces greenhouse gas emissions and
improves public health. Street-level bicycle volume information would aid
cities in planning targeted infrastructure improvements to encourage cycling
and provide civil society with evidence to advocate for cyclists' needs. Yet,
the data currently available to cities and citizens often only comes from
sparsely located counting stations. This paper extrapolates bicycle volume
beyond these few locations to estimate bicycle volume for the entire city of
Berlin. We predict daily and average annual daily street-level bicycle volumes
using machine-learning techniques and various public data sources. These
include app-based crowdsourced data, infrastructure, bike-sharing, motorized
traffic, socioeconomic indicators, weather, and holiday data. Our analysis
reveals that the best-performing model is XGBoost, and crowdsourced cycling and
infrastructure data are most important for the prediction. We further simulate
how collecting short-term counts at predicted locations improves performance.
By providing ten days of such sample counts for each predicted location to the
model, we are able to halve the error and greatly reduce the variability in
performance among predicted locations.
