  The recent availability of data for cities has allowed scientists to exhibit
scalings which present themselves in the form of a power-law dependence with
population of various socio-economical and structural indicators. We propose
here a dynamical, stochastic theory of urban growth which accounts for some of
the observed scalings and we confirm these results on US and OECD empirical
data. In particular, we show that the dependence with population size of the
total number of miles driven daily, the total length of the road network, the
total traffic delay, the total consumption of gasoline, the quantity of $CO_2$
emitted and the relation between area and population of cities, are all
governed by a single parameter which characterizes the sensitivity to
congestion. Finally, our results suggest that diseconomies associated with
congestion scale superlinearly with population size, implying that, despite
polycentrism, cities whose transportation infrastructure rely heavily on
traffic sensitive modes are unsustainable.
