Matrix tools for general observability analysis in traffic networks
IEEE Transactions on Intelligent Transportation Systems
Bayesian Demand Calibration for Dynamic Traffic Simulations
Transportation Science
Dynamic OD matrix estimation exploiting bluetooth data in Urban networks
ACMIN'12 Proceedings of the 14th international conference on Automatic Control, Modelling & Simulation, and Proceedings of the 11th international conference on Microelectronics, Nanoelectronics, Optoelectronics
| Hi-index | 0.00 |
This paper examines two different approaches for real-time estimation/prediction of time-dependent Origin--Destination (O--D) flows. Both approaches lend themselves to formulation as state-space models. The first approach is an extension of previous work by the authors. The key idea in this approach is to define the state-vector in terms of deviations in O--D flows instead of the O--D flows themselves. We demonstrate that approximations to this model make the real-time estimation process computationally more tractable with little deterioration in quality of estimates. In the second approach, the state vector is defined in terms of deviations of departure rates from each origin and the shares headed to each destination. This approach attempts to capture the differential variation of departure rates and shares over time. Performance of the proposed models is evaluated using actual traffic data from different sources. Preliminary results indicate that the filtering procedure is robust and that, compared to the original model, a formulation based on departure rates and shares yields better predictions with some loss of accuracy in filtered estimates.