Doubly uncertain transportation network: Degradable capacity and stochastic demand

Siu, Barbara W.Y.; Lo, Hong Kam

doi:10.1016/j.ejor.2007.08.026

Cited by 140 publications

(85 citation statements)

References 29 publications

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“…Commuters, therefore, do not know their exact a priori travel times. Some commuters would depart earlier to allow for additional time, or add a travel time margin to the expected trip time, in order to avoid late arrivals [9]. In other words, commuters allow for a longer travel time budget to hedge against travel time variability.…”

Section: Different Choice Behaviorsmentioning

confidence: 99%

“…SZETO and LO [8] modeled commuters' route choice behavior under stochastic link capacity degradation by the notion of "travel time budget". SIU and LO [9] developed a methodology to model doubly uncertain transportation network with stochastic link capacity degradation and stochastic demand. SUN et al [10] proposed optimization models to design demand-driven timetables for a single-track metro service.…”

Section: Introductionmentioning

confidence: 99%

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Equilibrium analysis of mixed passengers in urban railway network

Zhang

Sun

2016

J. Cent. South Univ.

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A model is proposed to describe the passengers' route choice behaviors in urban railway traffic with stochastic link capacity degradation by considering two types of demand, sensitive and insensitive passenger. The insensitive passengers choose their route without paying much attention to congestion. To the contrary, sensitive passengers who consider route congestion choose travel route based on generalized cost. An equilibrium state is given by variational inequalities in terms of travel generalized cost, which is represented by the combinations of mean and variance of total travel time. The diagonalization algorithm is given to solve this programming. Results show that insensitive passengers have large effects on the path choice than sensitive ones, especially for the larger demand.

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Section: Different Choice Behaviorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Equilibrium analysis of mixed passengers in urban railway network

Zhang

Sun

2016

J. Cent. South Univ.

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“…One of the important approaches was the de nition of travel time reliability regarding the degradation state of a network in which travel time reliability is de ned as a function of the ratio of travel times under degraded and non-degraded state (Asakura 1999). Some travel time related aspects of transportation network reliability has recently been introduced taking into account 'travel time budget' like those by Lo et al (2006); Siu and Lo (2008), and 'schedule reliability' by Li and Huang (2005).…”

Section: Travel Time Reliabilitymentioning

confidence: 99%

An Approximate Reliability Evaluation Method for Improving Transportation Network Performance

Mohaymany

Babaei

2010

Transport

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Abstract. Considering the importance of maintaining network performance at desired levels under uncertainty, network reliability, as a new approach to assessing the performance of degradable urban transportation networks, has become increasingly developed in two recent decades. In this paper, a method for optimizing resource allocation to meet the required levels of transportation network reliability is proposed. e worked out method consists of two stages: at stage one, a method for computing the reliability of network connectivity based on the reliability of computing arc performance with an assumption that capacities are random variables for each arc is presented. ese random variables are assumed to be conformed to especial probability density functions which can be modi ed through investing to improve the performance reliability of the arcs. At stage two, a mixed integer nonlinear programming model is developed to optimize resource allocation in the network. Numerical results are also provided in a simple network to demonstrate the capability of the employed method.

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“…For the details of the proof, readers can refer to Lo and colleagues. [48][49][50] Therefore, the arc flow v a distribution can be expressed as normal distribution as follows…”

Section: Chance Constraintsmentioning

confidence: 99%

A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

Zhang

Huang

et al. 2015

Advances in Mechanical Engineering

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This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO 2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss-Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.

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Doubly uncertain transportation network: Degradable capacity and stochastic demand

Cited by 140 publications

References 29 publications

Equilibrium analysis of mixed passengers in urban railway network

Equilibrium analysis of mixed passengers in urban railway network

An Approximate Reliability Evaluation Method for Improving Transportation Network Performance

A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

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