Modeling Demand Elasticity and Route Overlapping in Stochastic User Equilibrium through Paired Combinatorial Logit Model

2014 ◽  
Vol 2429 (1) ◽  
pp. 8-19 ◽  
Author(s):  
Seungkyu Ryu ◽  
Anthony Chen ◽  
Xiangdong Xu ◽  
Keechoo Choi
Keyword(s):  
Logit Model ◽  
User Equilibrium ◽  
Demand Elasticity ◽  
Stochastic User Equilibrium ◽  
Route Overlapping

Link-Nested Logit Model of Route Choice: Overcoming Route Overlapping Problem

1998 ◽  
Vol 1645 (1) ◽  
pp. 133-142 ◽  
Author(s):  
Peter Vovsha ◽  
Shlomo Bekhor
Keyword(s):  
Logit Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Discrete Choice Models ◽  
Nested Logit ◽  
Stochastic User Equilibrium ◽  
Nested Logit Model ◽  
Network Loading ◽  
Proposed Model ◽  
Route Overlapping

A new link-nested logit model of route choice is presented. The model is derived as a particular case of the generalized-extreme-value class of discrete choice models. The model has a flexible correlation structure that allows for overcoming the route overlapping problem. The corresponding stochastic user equilibrium is formulated in two equivalent mathematical programming forms: as a particular case of the general Sheffi formulation and as a generalization of the logit-based Fisk formulation. A stochastic network loading procedure is proposed that obviates route enumeration. The proposed model is then compared with alternative assignment models by using numerical examples.

Solving the Overlapping Problem in Route Choice with Paired Combinatorial Logit Model

2003 ◽  
Vol 1857 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Anthony Chen ◽  
Panatda Kasikitwiwat ◽  
Zhaowang Ji
Keyword(s):  
Mathematical Programming ◽  
Logit Model ◽  
Choice Model ◽  
Route Choice ◽  
Similarity Index ◽  
User Equilibrium ◽  
Transportation Systems ◽  
Programming Formulation ◽  
Stochastic User Equilibrium ◽  
Mathematical Programming Formulation

Recently, there has been renewed interest in improving the logit-based route choice model because of the importance of the route choice model in intelligent transportation systems applications, particularly the applications of advanced traveler information systems. The paired combinatorial logit (PCL) model and its equivalent mathematical programming formulation for the route choice problem have been studied. An algorithm based on the partial linearization method is presented for solving the PCL stochastic user equilibrium problem. Detailed examples are provided to explain how this hierarchical logit model resolves the overlapping problem through the similarity index while still accounting for both congestion and stochastic effects in the mathematical programming formulation.

Optimal Tolling Strategies and Concession Period Required for Build-Operate-Transfer Road Projects in Network with Demand Uncertainty by Vehicle Types

2012 ◽  
Vol 209-211 ◽  
pp. 977-986 ◽  
Author(s):  
Liang Xu
Keyword(s):  
Programming Problem ◽  
Demand Uncertainty ◽  
Consumer Surplus ◽  
User Equilibrium ◽  
Solution Algorithm ◽  
Demand Elasticity ◽  
Stochastic User Equilibrium ◽  
Traffic Demand ◽  
Concession Period ◽  
Upper Level

In view of uncertainty of traffic demand by vehicle types during the concession period, the toll charges of these BOT roads should be time-dependent and varied for different vehicle types by time of the concession period so as to maximize the system performance.It can be formulated as a bi-level programming problem. At the upper level, the objective is to maximize the social surplus combining of consumer surplus and the investor’s net profit. With taking account the demand elasticity in respect to the toll variability and incorporating the demand uncertainty into the revenue-cost constraint, the lower level is a multi-class reliability-based stochastic user equilibrium model. A genetic solution algorithm is adopted for solving the bi-level programming problem. A numerical example is presented to illustrate the applications of the proposed model and solution algorithm and some conclusions are drawn.

scholarly journals A Simplified C-Logit Stochastic User Equilibrium Model on Bimodal Transportation Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Bingquan Liu ◽  
Yonghong Zhang ◽  
Wei Du
Keyword(s):  
Logit Model ◽  
Transportation Network ◽  
Flow Distribution ◽  
User Equilibrium ◽  
Simplified Model ◽  
Stochastic User Equilibrium ◽  
Binary Logit Model ◽  
Mode Split ◽  
Binary Logit ◽  
Nonlinear Mathematical Programming

This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to satisfy the bimodal C-logit SUE conditions at its stationary point and can be solved by existing algorithms. So, the simplified model can be convenient to be used on the general bimodal transportation network.

Toll Choice and Stochastic User Equilibrium: Ticking All the Boxes

2019 ◽  
Vol 2673 (4) ◽  
pp. 930-940
Author(s):  
Jan C. Zill ◽  
Pedro Camargo ◽  
Naznin Sultana Daisy ◽  
Tim Veitch
Keyword(s):  
Logit Model ◽  
Real Life ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Demand Modeling ◽  
Modeling And Forecasting ◽  
Choice Sets ◽  
Equilibrium Assignment ◽  
Life Model ◽  
Choice Set Generation

Stochastic user equilibrium is a behaviorally realistic framework for strategic demand modeling and forecasting in cities/regions where there are multiple tolled facilities, especially when it comes to patronage forecasting for existing or planned tolled facilities. However, there is currently no algorithm available in the literature or in commercial software that provides a comprehensive approach for stochastic user equilibrium assignment that addresses the generation of route choice sets for tolled roads, path overlap, and high levels of convergence. This paper presents a novel choice set generation algorithm combined with the path-size logit model and the bi-conjugate Frank-Wolfe equilibrium assignment in a comprehensive algorithm for forecasting tolled road patronage, along with the results of its application to a real-life model in Brisbane, Australia.

scholarly journals A regret-based route choice model with asymmetric preference in a stochastic network

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879323
Author(s):  
Lei Zhao ◽  
Hongzhi Guan ◽  
Xinjie Zhang ◽  
Xiongbin Wu
Keyword(s):  
Loss Aversion ◽  
Equilibrium Model ◽  
Choice Model ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Regret Minimization ◽  
Route Network ◽  
Travel Decision ◽  
Gains And Losses ◽  
Random Regret Minimization

In this study, a stochastic user equilibrium model on the modified random regret minimization is proposed by incorporating the asymmetric preference for gains and losses to describe its effects on the regret degree of travelers. Travelers are considered to be capable of perceiving the gains and losses of attributes separately when comparing between the alternatives. Compared to the stochastic user equilibrium model on the random regret minimization model, the potential difference of emotion experienced induced by the loss and gain in the equal size is jointly caused by the taste parameter and loss aversion of travelers in the proposed model. And travelers always tend to use the routes with the minimum perceived regret in the travel decision processes. In addition, the variational inequality problem of the stochastic user equilibrium model on the modified random regret minimization model is given, and the characteristics of its solution are discussed. A route-based solution algorithm is used to resolve the problem. Numerical results given by a three-route network show that the loss aversion produces a great impact on travelers’ choice decisions and the model can more flexibly capture the choice behavior than the existing models.

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