Cross-validating traffic speed measurements from probe and stationary sensors through state reconstruction

Traffic speed on freeways can be measured by two types of technologies, i.e. probe sensors and stationary sensors. Cross-validation is critical to ensure the consistency between heterogeneous measurements. A challenge lies in the mismatch of probe and stationary measurements in space and time, especially when one of them is relatively sparse. Towards filling the gap, this paper presents a cross-validation method based on traffic state reconstruction. The proposed method is computationally simple and robust. This makes it ready to be implemented for large data sets without complicated tuning. We present analytical formulation of the proposed method and an analysis of its robustness property. We demonstrate the method using both simulation model and real-world freeway data. Results show that the method can effectively identify discrepancies between probe and stationary speed measurements.