Article ID: | iaor20073366 |
Country: | United States |
Volume: | 52 |
Issue: | 1 |
Start Page Number: | 116 |
End Page Number: | 127 |
Publication Date: | Jan 2004 |
Journal: | Operations Research |
Authors: | Bierlaire M., Crittin F. |
Keywords: | programming: nonlinear |
The problem of estimating and predicting Origin–Destination (OD) tables is known to be important and difficult. In the specific context of Intelligent Transportation Systems (ITS), the dynamic nature of the problem and the real-time requirements make it even more intricate. We consider here a least-squares modeling approach for solving the OD estimation and prediction problem, which seems to offer convenient and flexible algorithms. The dynamic nature of the problem is represented by an autoregressive process, capturing the serial correlations of the state variables. Our formulation is inspired from Cascetta