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| Content Provider | Springer Nature Link |
|---|---|
| Author | Ben Akiva, Moshe Gao, Song Lu, Lu Wen, Yang |
| Copyright Year | 2014 |
| Abstract | Dynamic Traffic Assignment (DTA) models are important decision support tools for transportation planning and real-time traffic management. One of the biggest obstacles of applying DTA in large-scale networks is the calibration of model parameters, which is essential for the realistic replication of the traffic condition. This paper proposes a methodology for the simultaneous demand-supply DTA calibration based on both aggregate measurements and disaggregate route choice observations to improve the calibration accuracy. The calibration problem is formulated as a bi-level constrained optimization problem and an iterative solution algorithm is proposed. A case study in a highly congested urban area of Beijing using DynaMIT-P is conducted and the combined calibration method improves the fits to surveillance data compared to the calibration based on aggregate measurements only. |
| Starting Page | 559 |
| Ending Page | 581 |
| Page Count | 23 |
| File Format | |
| ISSN | 1566113X |
| Journal | Networks and Spatial Economics |
| Volume Number | 15 |
| Issue Number | 3 |
| e-ISSN | 15729427 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2014-04-27 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Dynamic traffic assignment Calibration Route choice model estimation Regional/Spatial Science Civil Engineering Operations Research/Decision Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Networks and Communications Software |
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