  Distributed acoustic sensing through fiber-optical cables can contribute to
traffic monitoring systems. Using data from a day of field testing on a 50 km
long fiber-optic cable along a railroad track in Norway, we detect and track
cars and trains along a segment of the fiber-optic cable where the road runs
parallel to the railroad tracks. We develop a method for automatic detection of
events and then use these in a Kalman filter variant known as joint
probabilistic data association for object tracking and classification. Model
parameters are specified using in-situ log data along with the fiber-optic
signals. Running the algorithm over an entire day, we highlight results of
counting cars and trains over time and their estimated velocities.
