Comparison of interpolation techniques for state estimation on urban networks

State estimation is an important instrument for understanding the daily urban system and its spatial and temporal dynamics. With these insights we are able to better predict future traffic states and improve the demand-supply match. If state estimation is performed real-time it can be used for short term prediction and virtual patrolling. Most earlier research focuses on highways, while less is known about urban networks because of less available measurements and urban networks are more complex. Therefore we compared two promising slightly adapted but relatively simple, scalable and fast spatial interpolation methods, respectively a simplified form of Variance-Based Interpolation (VBI) and Learning-Database Interpolation (LDI), for an urban network using floating car data based
on a micro simulation providing the ground truth. The performance of these methods was assessed depending on penetration rate compared with a reference situation of no interpolation. The results show that the VBI method performs reasonably well up to 9% coverage, but at higher penetration rates performs worse than the reference situation. The performance of LDI is much more promising, at low penetration rates it already shows large improvement and it continues to outperform the reference situation up to 40% of FCD coverage.