scholarly journals A Study on Scale-Parameter of Stochastic User Equilibrium Assignment Models

2005 ◽  
Vol 22 ◽  
pp. 523-530 ◽  
Author(s):  
Yoshiyuki OKADA ◽  
Tomio MIWA ◽  
Takayuki MORIKAWA
Keyword(s):  
Scale Parameter ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Equilibrium Assignment ◽  
Assignment Models

Bounding the inefficiency of the C-logit stochastic user equilibrium assignment

2016 ◽  
Vol 29 (6) ◽  
pp. 1629-1649 ◽  
Author(s):  
Gui Yong ◽  
Haijun Huang ◽  
Tianliang Liu ◽  
Yan Xu
Keyword(s):  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Equilibrium Assignment

Congestion, Stochastic, and Similarity Effects in Stochastic: User-Equilibrium Models

2000 ◽  
Vol 1733 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Joseph N. Prashker ◽  
Shlomo Bekhor
Keyword(s):  
Flow Pattern ◽  
User Equilibrium ◽  
Physical Parameters ◽  
Stochastic User Equilibrium ◽  
Similarity Coefficients ◽  
Starting Point ◽  
Congestion Effect ◽  
Stochastic Assignment ◽  
Similarity Effect ◽  
Assignment Models

Different effects in stochastic user-equilibrium formulations are compared. The starting point is the logit assignment formulation for the stochastic user-equilibrium model. Recently, extended logit-based models were developed as solutions of equivalent stochastic user-equilibrium problems. These extended logit models are theoretically superior to the simple logit model, because they take into account the similarity among routes. The similarity indexes were defined based on physical parameters of the network, such as link lengths, and therefore did not depend on congestion. The assumption that similarity coefficients are independent of congestion means that the similarity effect and the congestion effect are treated separately. However, both similarity and congestion effects are taken into account in the equilibrium formulations. Simple network examples are presented to illustrate the sensitivity of the link flow pattern (and path flow pattern) to the three different effects in stochastic assignment models: the congestion effect, the stochastic effect, and the similarity effect. The relative influence of those effects on the flow patterns is discussed.

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