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Multiclass Probit-Based Origin-Destination Estimation Using Multiple Data Types
| 题名： | Multiclass Probit-Based Origin-Destination Estimation Using Multiple Data Types |
| 其他题名： | Al-Battaineh,O.,and Kaysi,I.A.(2007)."Genetically-optimized origin-destination estimation(GOODE)model:Application to regional commodity movements in Ontario."Can.J.Civ.Eng.,34(2),228-238. |
| 正文语种： | 英文 |
| 作者： | Qing Zhao |
| 关键词： | different；prediction；optimization；literature；formulation；probit；insights；class；sensor；multi |
| 摘要： | This paper proposes a bilevel optimization model for multiclass origin-destination (O-D) estimation using various types of data. The multiclass character of the model, a new feature and major contribution to the literature, is important because of increasing interest in simultaneous estimation of O-D tables for various classes of trucks and automobiles. The upper-level optimization is used to derive O-D table entries by minimizing the sum of squared differences between observations from different data sources and the predictions of those values. A probit model is assumed in the lower-level stochastic user equilibrium problem for flow prediction. Extensive experiments have been performed on a test network with different types of link count sensors and turning movements. The tests verify the problem formulation and solution algorithm and offer important insights into the multiclass O-D estimation process with the different types of available data. Adding turning movement data can improve O-D estimation by 71％. Furthermore, classification information is interchangeable among different types of sensors. |
| 出版年： | 2018 |
| 论文唯一标识： | P-72Y2018V144N06004 |
| 英文栏目名称： | TECHNICAL PAPERS |
| doi： | 10.1061/JTEPBS.0000135 |
| 期刊名称： | Journal of Transportation Engineering |
| 拼音刊名(出版物代码)： | P-72 |
| 卷： | 144 |
| 期： | 06 |
| 页码： | 25-33 |
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