A Theoretical Framework for Traffic Speed Estimation by Fusing Low-Resolution Probe Vehicle Data

Probe vehicles with Global Positioning Systems (GPS) can provide accurate positions that enable spatial-average speed estimation. However, some probe vehicles cannot provide accurate positions but can provide location-specific information on when and where they are located at the segment or cell level. These topological position (TP) data with segment-or cell-level accuracy cannot provide the distance component that is necessary for traffic speed estimation. However, considering the wide availability of TP data in the existing telecommunications network, there is still hope and benefits to make use of the data for traffic state estimation. In this paper, an algorithm is proposed using low-resolution positioning data. The proposed method is capable of fusing low-resolution positioning data with other data sources, leading to more accurate and reliable speed estimation of relatively low bias. In addition, this method shows strong robustness and error tolerance and can reveal the magnitude of the estimation error, which is helpful for travel time prediction and traffic control.