Evaluation of trade-offs between two data sources for the accurate estimation of origin–destination matrices

In this paper, we evaluate the trade-offs between loop detector data and floating car data (FCD) for the real-time estimation of origin–destination (OD) matrices in small networks. The proposed methodology is based on a bi-level optimisation using fuzzy logic theory. Here we demonstrate that it provides accurate results with low computational cost, while presenting several advantages over other existing algorithms (especially in terms of data requirements, computational complexity, and quality of adjustment). The methodology is illustrated with three examples covering two different locations in the city of Zurich, Switzerland. Results are used to evaluate the trade-offs between loop detector coverage and the penetration rate of FCD, and to determine minimum values for ensuring a given accuracy level on the estimated OD matrices. In general, the resulting error in OD estimation is affected by the data redundancy in the network.