Quasi-dynamic estimation of o-d flows from traffic counts: Formulation, statistical validation and performance analysis on real data

The paper proposes a quasi-dynamic framework for estimation of origin-destination (o-d) flow from traffic counts, under the assumption that o-d shares are constant across a reference period, whilst total flows leaving each origin vary for each sub-period within the reference period. The advantage of this approach over conventional within-day dynamic estimators is that of reducing drastically the number of unknowns given the same set of observed time-varying traffic counts. Obviously, the gain in accuracy depends on how realistic is the underlying assumption that total demand levels vary more rapidly over time than o-d shares. Firstly, the paper proposes a theoretical specification of the quasi-dynamic estimator. Subsequently, it proposes empirical and statistical tests to check the quasi-dynamic assumption and then compares the performances of the quasi-dynamic estimator of o-d flows with both classical off-line simultaneous dynamic estimators and on-line recursive Kalman filter-based estimators. Experiments are carried out on the real test site of A4-A23 motorways in North-Eastern Italy. Results confirm the acceptability of the assumption of quasi-dynamic o-d flows, even under the hypothesis of constant distribution shares for the whole day and show that the quasi-dynamic estimator outperforms significantly the simultaneous estimator. Data also suggest that using the quasi-dynamic estimates instead of the simultaneous estimates as historical o-d flows improves significantly the performances of the Kalman filter, which strongly depends of the quality of the seed o-d flows. In addition, it is shown that the aggregation of quasi-dynamic o-d estimates across subsequent time slices represents also the most effective way to obtain o-d estimates for larger time horizons (e.g. hourly estimates). Finally, a validation based on an hold-out sample of link flows (i.e. counts not used as inputs in the o-d estimation/updating process) revealed the quasi-dynamic estimator to be overall more robust and effective with respect to the other tested estimators. (C) 2013 Elsevier Ltd. All rights reserved.