  In an age of ever-increasing penetration of GPS-enabled mobile devices, the
potential of real-time "probe" location information for estimating the state of
transportation networks is receiving increasing attention. Much work has been
done on using probe data to estimate the current speed of vehicle traffic (or
equivalently, trip travel time). While travel times are useful to individual
drivers, the state variable for a large class of traffic models and control
algorithms is vehicle density. Our goal is to use probe data to supplement
traditional, fixed-location loop detector data for density estimation. To this
end, we derive a method based on Rao-Blackwellized particle filters, a
sequential Monte Carlo scheme. We present a simulation where we obtain a 30\%
reduction in density mean absolute percentage error from fusing loop and probe
data, vs. using loop data alone. We also present results using real data from a
19-mile freeway section in Los Angeles, California, where we obtain a 31\%
reduction. In addition, our method's estimate when using only the real-world
probe data, and no loop data, outperformed the estimate produced when only loop
data were used (an 18\% reduction). These results demonstrate that probe data
can be used for traffic density estimation.
