Fusing Loop and GPS Probe Measurements to Estimate Freeway Density

TL;DR

This paper introduces a Rao-Blackwellized particle filter method to fuse GPS probe and loop detector data for freeway density estimation, significantly improving accuracy over traditional methods.

Contribution

It presents a novel sequential Monte Carlo approach to combine probe and loop data, enhancing real-time freeway density estimation accuracy.

Findings

30% reduction in density estimation error with fused data

31% error reduction using combined data in real-world case

Probe data alone outperforms loop data in density estimation

Abstract

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.