Combined Multinomial Logit Modal Split and Paired Combinatorial Logit Traffic Assignment Model
To better address the route overlap problem of the multinomial logit model used in combined modal split and traffic assignment models in the literature, this study proposes a combined multinomial logit modal split and paired combinatorial logit traffic assignment (MNL-PCL) model. The PCL model can account for the route overlap problem using a similarity index for each pair of routes in the network. It requires fewer parameters to be calibrated using real-world data. Thereby, it circumvents parameter estimation issues associated with a cross-nested logit model. An equivalent mathematical programming problem is developed for the MNL-PCL model. Further, an analytical model is developed for sensitivity analysis of the MNL-PCL model. Several applications of the proposed MNL-PCL model are demonstrated using a numerical example by leveraging the results of sensitivity analysis. The study insights can assist decision-makers to design more effective strategies to promote “go-green” travel modes and reduce network congestion.
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Supplemental Notes:
- This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.
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Corporate Authors:
Transportation Research Board
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Authors:
- Wang, Jian
- Peeta, Srinivas
- He, Xiaozheng (Sean)
- Zhao, Jinbao
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Conference:
- Transportation Research Board 98th Annual Meeting
- Location: Washington DC, United States
- Date: 2019-1-13 to 2019-1-17
- Date: 2019
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 23p
Subject/Index Terms
- TRT Terms: Combinatorial analysis; Mathematical models; Modal split; Multinomial logits; Route choice; Sensitivity analysis; Traffic assignment; Travel demand
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01698224
- Record Type: Publication
- Report/Paper Numbers: 19-00828
- Files: TRIS, TRB, ATRI
- Created Date: Mar 1 2019 3:51PM