Name
Title | ||
|---|---|---|
A Revised Logit Model for Stochastic Traffic Assignment With a Relatively Stable Dispersion Parameter |
Abstract | ||
|---|---|---|
The optimal value of the embedded dispersion parameter in the logit-based stochastic traffic assignment (STA) model significantly impacts predicted network flows. It also varies dramatically for different systems. This prevents its application, especially when real-world network flow data are unavailable for model calibration. To address the problem, this article proposes a revised logit model. Ra... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MITS.2021.3083717 | IEEE Intelligent Transportation Systems Magazine |
Keywords | DocType | Volume |
Dispersion,Predictive models,Data models,Computational modeling,Calibration,Load modeling,Stochastic processes | Journal | 14 |
Issue | ISSN | Citations |
2 | 1939-1390 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |