  Cyclists travelling in urban areas are particularly at risk of harm from
particulate emissions due to their increased breathing rate and proximity to
vehicles. In this paper we combine human respiratory models with models of
particulate inhalation to estimate the pollution risk an individual is
experiencing in real time given the local pollution level and their heart rate
for the first time. Using this model as a baseline, we learn a policy that
simultaneously optimises the route for a large number of cyclists with diverse
origins and destinations, to minimise overall pollution risk and account for
the detrimental impacts of congestion. We learn this policy using reinforcement
learning techniques on simulated data in different environments with varying
distributions of cyclist fitness. These findings establish that individualised
routing is effective in reducing pollution risk while cycling, improving the
net benefits of active commuting.
