Fusing Loop and GPS Probe Measurements to Estimate Freeway Density

In an age of ever-increasing penetration of Global Positioning System-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 versus using loop data alone. We also present results using real data from a 19-mi freeway section in Los Angeles, CA, 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 (i.e., an 18% reduction). These results demonstrate that probe data can be used for traffic density estimation.