  This paper presents a novel method for estimating the number of vehicles
traveling along signalized approaches using probe vehicle data only. The
proposed method uses the Kalman Filtering technique to produce reliable vehicle
count estimates using real-time probe vehicle estimates of the expected travel
times. The proposed method introduces a novel variable estimation interval that
allows for higher estimation precision, as the updating time interval always
contains a fixed number of probe vehicles. The proposed method is evaluated
using empirical and simulated data, the former of which were collected along a
signalized roadway in downtown Blacksburg, VA. Results indicate that vehicle
count estimates produced by the proposed method are accurate. The paper also
examines the model's accuracy when installing a single stationary sensor (e.g.,
loop detector), producing slight improvements especially when the probe vehicle
market penetration rate is low. Finally, the paper investigates the sensitivity
of the estimation model to traffic demand levels, showing that the model works
better at higher demand levels given that more probe vehicles exist for the
same market penetration rate.
