  City traffic is a dynamic system of enormous complexity. Modeling and
predicting city traffic flow remains to be a challenge task and the main
difficulties are how to specify the supply and demands and how to parameterize
the model. In this paper we attempt to solve these problems with the help of
large amount of floating car data. We propose a coarse-grained cellular
automata model that simulates vehicles moving on uniform grids whose size are
much larger compared with the microscopic cellular automata model. The car-car
interaction in the microscopic model is replaced by the coupling between
vehicles and coarse-grained state variables in our model. To parameterize the
model, flux-occupancy relations are fitted from the historical data at every
grids, which serve as the coarse-grained fundamental diagrams coupling the
occupancy and speed. To evaluate the model, we feed it with the historical
travel demands and trajectories obtained from the floating car data and use the
model to predict road speed one hour into the future. Numerical results show
that our model can capture the traffic flow pattern of the entire city and make
reasonable predictions. The current work can be considered a prototype for a
model-based forecasting system for city traffic.
