Improving the reliability of demand estimation using traffic counts by including information on link flow observability

Demand estimation problems are traditionally solved by an optimisation problem, where some distance between measured and simulated flows is minimised. To limit the effects of solution under- determinedness in such problems, a-priori assumptions on the OD matrix structure are adopted. In this paper we study the impact of sensor locations on the quality of OD estimations. We show that linear correlations between link flow data may negatively affect the estimation reliability. By contrast, efficient sensor location models, able to identify sensor positions that collect linearly independent link flows allow improving the reliability of the estimation process, especially when no good prior information is available on the OD matrix structure. In addition, we show that by selecting sensor locations according to partial observability metrics introduced by the authors in recent research, the contribution of the additional information in the objective function also improves with respect to alternative metrics introduced in the literature