  The complexity of interactions between networks and territories has been
widely acknowledged empirically, in particular through the existence of
circular causal relations in their co-development, that can be understood as a
co-evolution. This contribution aims at investigating models that endogenize
this co-evolution, in the particular case of cities and transportation
networks. We introduce a family of models of co-evolution for systems of cities
at the macroscopic scale. Interactions between cities are the main driver of
population growth rates, capturing a network effect at the first order (direct
interactions). Network growth follows a demand-induced thresholded growth
scheme, that can occur at the global level or locally. The exploration of the
model on synthetic systems of cities shows the ability of the model to capture
co-evolutive patterns. We apply the model on the French system of cities, with
population data spanning 1831-1999 and a dynamical railway network (1850-2000).
The model is calibrated on successive time-windows, assuming local temporal
stationarity. We extract therein indirect knowledge on underlying processes and
find that the prediction for city populations are in some cases improved in
comparison to a static model.
