  The rapid growth of population in urban areas is jeopardizing the mobility
and air quality worldwide. One of the most notable problems arising is that of
traffic congestion which in turn affects air pollution. With the advent of
technologies able to sense real-time data about cities, and its public
distribution for analysis, we are in place to forecast scenarios valuable to
ameliorate and control congestion. Here, we analyze a local congestion pricing
scheme, hotspot pricing, that surcharges vehicles traversing congested
junctions. The proposed tax is computed from the estimation of the evolution of
congestion at local level, and the expected response of users to the tax
(elasticity). Results on cities' road networks, considering real-traffic data,
show that the proposed hotspot pricing scheme would be more effective than
current mechanisms to decongest urban areas, and paves the way towards
sustainable congestion in urban areas.
