Modeling and solving discrete network design problem with stochastic user equilibrium

2016 ◽  
Vol 50 (7) ◽  
pp. 1295-1313 ◽  
Author(s):  
Haoxiang Liu ◽  
David Z. W. Wang
Keyword(s):  
Network Design ◽  
Design Problem ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Stochastic User Equilibrium ◽  
Discrete Network Design Problem

Research on Uncertain Network Design Problem

2014 ◽  
Vol 505-506 ◽  
pp. 613-618
Author(s):  
Yang Wang ◽  
Jin Xin Cao ◽  
Ri Dong Wang ◽  
Xia Xi Li
Keyword(s):  
Stochastic Model ◽  
Network Design ◽  
Design Problem ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Construction Costs ◽  
Travel Costs ◽  
Uncertain Network ◽  
Level Model ◽  
Discrete Network Design Problem

In this study, a kind of uncertain network design problem, network design problem under uncertain construction cost, is researched.The discrete network design problem under uncertain construction costs deals with the selection of links to be added to the existing network, so as to minimize the total travel costs in the network. It is assumed that the value of the demand between each pair of origin and destination is a constant and the construction costs of each potential link addition follow a certain stochastic distribution. In this paper, a bi-level and stochastic programming model for the discrete network design problem is proposed. The construction costs of potential links are assumed as random variables and mutually independent with each other in this model. The upper-level model is a chance constrain model with the objective function of minimizing the total travel costs in the network, and the lower-level model is a user equilibrium model. The stochastic model is then transformed into a deterministic one. A branch-and-bound solution algorithm is designed to solve the deterministic model in an efficient way. At last, a computational experiment is conducted to illustrate the effectiveness and efficiency of the approach proposed in this paper. The results show that the stochastic model is more flexible and practical compared with the deterministic one.

scholarly journals Continuous Network Design Based on the Paired Combinatorial Logit Stochastic User Equilibrium Model

2014 ◽  
Vol 8 (1) ◽  
pp. 316-322
Author(s):  
Xuefei Li ◽  
Maoxiang Lang
Keyword(s):  
Network Design ◽  
Objective Function ◽  
Equilibrium Model ◽  
Design Problem ◽  
Programming Model ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Stochastic User Equilibrium ◽  
Level Model ◽  
The Impact

In order to design the traffic network more accurately, the bi-level programming model for the continuous network design problem based on the paired combinatorial Logit stochastic user equilibrium model is proposed in this study. In the model, the paired combinatorial Logit stochastic user equilibrium model which is used to characterize the route choice behaviors of the users is adopted in the lower level model, and the minimum summation of the system total costs and investment amounts is used in the upper objective function. The route-based self-regulated averaging (SRA) algorithm is designed to solve the stochastic user equilibrium model and the genetic algorithm (GA) is designed to get the optimal solution of the upper objective function. The effectiveness of the proposed combining algorithm which contains GA and SRA is verified by using a simple numerical example. The solutions of the bi-level models which use the paired combinatorial Logit stochastic user equilibrium model in the lower level model with different demand levels are compared. Finally, the impact of the dispersion coefficient parameter which influences the decision results of the network design problem is analyzed.

scholarly journals Discrete Network Design Problem

2020 ◽  
Author(s):  
Alireza Rahimi
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Network Design Problem ◽  
Variable Demand ◽  
Total Cost ◽  
Discrete Network Design Problem ◽  
The Impact ◽  
Budget Restrictions

The network design problem aims to minimize the travelers’ total cost under budget restrictions. This research provides a framework to incorporate variable demand assignment in the discrete network design problem. The findings emphasized the impact of considering variable demand in discrete network design problem.

Investment Selection Model for Multicommodity Multimodal Transportation Networks

1996 ◽  
Vol 1522 (1) ◽  
pp. 38-46 ◽  
Author(s):  
Carlos Felipe G. Loureiro ◽  
Bruce Ralston
Keyword(s):  
Network Design ◽  
Real Life ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Type Model ◽  
Stochastic User Equilibrium ◽  
Transportation Services ◽  
Life Problems ◽  
Multimodal Network ◽  
Main Component

A Multicommodity Multimodal Network Design (MCMND) model is presented; the model is to be used as a planning tool for determining investment priorities for freight intercity networks. The MCMND model is designed to select the best set of investment options for a multimodal regional network, given a limited investment budget. The main component of the model comprises the solution of a nonlinear bilevel network design problem formulated to choose investments that minimize both the transportation costs incurred by users and the environmental impacts of less efficient modes of transportation. Investment options to be considered by the model involve the addition of new physical links to the network, the improvement of existing links, and the location of intermodal transfer facilities at specified nodes of the network. The representation of the multimodal network is at a level of detail appropriate for strategic planning for a large region. The demand for transportation services is fixed and exogenous to the model. Mode choice in shipping freight is modeled in combination with flow assignment, assuming that goods are shipped at minimum total generalized costs. A new path-based stochastic user equilibrium assignment algorithm is proposed to distribute trips over the multimodal network according to a logit-type model. The Tietê-Paraná Valley in Brazil was selected for the development of a pilot application of the MCMND model to assess its efficiency when dealing with large networks. This application also served to emphasize the importance of an interface between the MCMND model and a geographic information system in solving real-life problems.

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