A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks

2021 ◽  
Author(s):  
Maëlle Zimmermann ◽  
Emma Frejinger ◽  
Patrice Marcotte
Keyword(s):  
Equilibrium Model ◽  
Traffic Assignment ◽  
Choice Model ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Traffic Equilibrium ◽  
Solution Algorithms ◽  
Present Solution ◽  
Imperfect Knowledge ◽  
Explicit Consideration

In the realm of traffic assignment over a network involving rigid arc capacities, the aim of the present work is to generalize the model of Marcotte, Nguyen, and Schoeb [Marcotte P, Nguyen S, Schoeb A (2004) A strategic flow model of traffic assignment in static capacitated networks. Oper. Res. 52(2):191–212.] by casting it within a stochastic user equilibrium framework. The strength of the proposed model is to incorporate two sources of stochasticity stemming, respectively, from the users’ imperfect knowledge regarding arc costs (represented by a discrete choice model) and the probability of not accessing saturated arcs. Moreover, the arc-based formulation extends the Markovian traffic equilibrium model of Baillon and Cominetti [Baillon JB, Cominetti R ( 2008 ) Markovian traffic equilibrium. Math. Programming 111(1-2):33–56.] through the explicit consideration of capacities. This paper is restricted to the case of acyclic networks, for which we present solution algorithms and numerical experiments.

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.

scholarly journals Additional Riderships Estimation under Different Configurations of Bus Rapid Transit System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Wu Lan ◽  
Chen Xuewu ◽  
Lu Tao
Keyword(s):  
Traffic Flow ◽  
Numerical Test ◽  
User Equilibrium ◽  
Bus Rapid Transit ◽  
Stochastic User Equilibrium ◽  
Rapid Transit ◽  
Traffic Equilibrium ◽  
Estimation Model ◽  
Transit System ◽  
Diagonalization Method

Different configurations of Bus Rapid Transit (BRT) system may cause different additional riderships. In this paper, in terms of network traffic equilibrium assignment principle, the additional riderships estimation model based on Variational Inequality (VI) model is presented. The bus frequency is related to variables including the travel time, the residence time in terminals, and the dwelling time at the stops. The additional riderships are translated into network additional traffic flow firstly. Given the bus frequency, VI model can be turned into Stochastic User Equilibrium (SUE) model to calculate the other variables. The similarity diagonalization method is used to calculate the elastic bus frequency and finally the network additional traffic flow can be computed. The additional riderships under different configurations of BRT system are compared in the numerical test. The results show that the additional riderships under different configurations have large differences and occupy a high percentage of the total ridership.

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