  Cycling can reduce greenhouse gas emissions and air pollution and increase
public health. With this in mind, policy-makers in cities worldwide seek to
improve the bicycle mode-share. However, they often struggle against the fear
and the perceived riskiness of cycling. Efforts to increase the bicycle's
mode-share involve many measures, one of them being the improvement of cycling
safety. This requires the analysis of the factors surrounding accidents and the
outcome. However, meaningful analysis of cycling safety requires accurate
bicycle flow data that is generally sparse or not even available at a segment
level. Therefore, safety engineers often rely on aggregated variables or
calibration factors that fail to account for variations in the cycling traffic
caused by external factors. This paper fills this gap by presenting a Deep
Learning based approach, the Long Short-Term Memory Mixture Density Network
(LSTMMDN), to estimate hourly bicycle flow in Copenhagen, conditional on
weather, temporal and road conditions at the segment level. This method
addresses the shortcomings in the calibration factor method and results in
66-77\% more accurate bicycle traffic estimates. To quantify the impact of more
accurate bicycle traffic estimates in cycling safety analysis, we estimate
bicycle crash risk models to evaluate bicycle crashes in Copenhagen. The models
are identical except for the exposure variables being used. One model is
estimated using the LSTMMDN estimates, one using the calibration-based
estimates, and one using yearly mean traffic estimates. The results show that
investing in more advanced methods for obtaining bicycle volume estimates can
benefit the quality, mitigating efforts by improving safety analyses and other
performance measures.
