  The two main data categories of vehicular traffic flow, stationary detector
data and floating-car data, are also available for many Marathons and other
mass-sports events: Loop detectors and other stationary data sources find their
counterpart in the RFID tags of the athletes recording the split times at
several stations during the race. Additionally, more and more athletes use
smart-phone apps generating track data points that are the equivalent of
floating-car data. We present a methodology to detect congestions and estimate
the location of jam-fronts, the delay times, and the spatio-temporal speed and
density distribution of the athlete's crowd flow by fusing these two data
sources based on a first-order macroscopic model with triangular fundamental
diagram. The method can be used in real-time or for analyzing past events.
Using synthetic "ground truth" data generated by simulations with the
Intelligent-Driver Model, we show that, in a real-time application, the
proposed algorithm is robust and effective with minimal data requirements.
Generally, two stationary data sources and about ten "floating-athlete"
trajectories per hour are sufficient.
