Multi-objective α-reliable path finding in stochastic networks with correlated link costs: A simulation-based multi-objective genetic algorithm approach (SMOGA)

Ji, Zhaowang; Kim, Yong Seog; Chen, Anthony

doi:10.1016/j.eswa.2010.07.064

Cited by 82 publications

(38 citation statements)

References 28 publications

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“…The method needs to sample random variables from sampling space. It is easily workable to perform sampling experiments in engineering practice [25,26,27,28]. In the paper, we assume that the sampling space Ψ may be discretized and be known in advance.…”

Section: Mean and Variance Of Link Travel Timementioning

confidence: 99%

“…There are several methods solving the problem such as sensitivity analysis-based method [39,40], cutting constraint method [41] and intelligent optimization method (e.g. genetic algorithm [25,26,27,28,29], Particle Swarm Optimization algorithm [42]). In this paper, we adopt the sensitivity analysis-based conjugate sub-gradient projection method to solve the congestion pricing problem.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Multi-class time reliability-based congestion pricing model based on a degradable transportation network

Liu

Huang

2016

Applied Mathematical Modelling

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Please cite this article as: Bing-quan Liu, Chong-chao Huang, Multi-class time reliability-based congestion pricing model on degradable transportation network, Applied Mathematical Modelling (2015), Highlights• We propose a traffic assignment model according to day-to-day capacity degradations.• Robust effective path travel time is employed to capture travelers' path choice behaviors.• Contractive safety margin is adopted to approximate the actual margin along the path.• A time reliability-based congestion pricing model is formulated.• The pricing model reflects the robustness of travelers' economic benefit. AbstractThis paper presents a multi-class time reliability-based user equilibrium model with elastic demand on the degradable transportation network. Due to the uncertainties of path travel time induced by random capacity degradation, the model captures travelers' path choice behaviors in form of robust effective path travel time and is no need of known travel time probability distribution. Involving travelers' heterogeneous degrees of risk-aversion, the equilibrium model can be transformed into an equivalent variational inequality (VI) problem. Then a heuristic solution algorithm is proposed to solve the VI problem. Taking the VI model as a constraint, a multi-class time reliability-based congestion pricing model is formulated as a mathematical programming with equilibrium constraints (MPEC) problem, which can be solved by the sensitivity analysis-based conjugate sub-gradient projection method. Two numerical examples are provided to illustrate the applications of the presented models and efficiencies of the solution algorithms.

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Section: Mean and Variance Of Link Travel Timementioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Multi-class time reliability-based congestion pricing model based on a degradable transportation network

Liu

Huang

2016

Applied Mathematical Modelling

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“…The work in [11] was an extension of an earlier paper [12], which did not consider the spatial and temporal correlations. In another recent work, a multi-objective reliable path finding method in stochastic networks with correlated link costs were proposed in [13]. This simulation study considered multiple requirements of travel time reliability based on an α-reliable path finding method in [14].…”

Section: Introductionmentioning

confidence: 97%

Real time route guidance with correlated link cost

Dong

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Route finding issues have always been a significant research focus in intelligence transportation system. Many solution models have been proposed in the previous work and analyzed in detail. Successive link travel time correlation has been identified to play an important role in these models to realistically reflect the property of traffic flows. In this paper, we establish a framework to find the best route in a stochastic time-dependent network by considering link travel time indeterminacy and correlation between adjacent links with real time information. We provide explicit mathematical formulations to update the outing link travel time distributions according to the real time information and help the travelers to find the best route to their destination. A simple illustrative example is shown to demonstrate the effectiveness and advantages of the proposed method.

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“…Under the framework, the correlation between links is considered hence path travel time is nonadditive (Fan et al, 2005;Dong and Mahmassani, 2009). Path travel time can be derived from link travel time covariance matrix (Sen et al, 2001;Xing and Zhou, 2011;Shahabi et al, 2013), temporal and spatial correlations (Miller-Hooks and Mahmassani, 2003;Gao and Chabini, 2006), or simulation-based approaches (Ji et al, 2011;Huang and Gao, 2012;Zockaie et al, 2013;Zockaie et al, 2014). Some research also focuses on the penalties due to late arrival and the corresponding route choice (Watling, 2006;Chen and Zhou, 2010).…”

Section: Introduction and Backgroundsmentioning

confidence: 99%

A Strategic User Equilibrium for Independently Distributed Origin‐Destination Demands

Wen

Gardner

Dixit

et al. 2017

Computer aided Civil Eng

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This article proposes an extension of the strategic user equilibrium proposed by Waller and colleagues and Dixit and colleagues. The proposed model relaxes the assumption of proportional Origin‐Destination (O‐D) demand, as it accounts for users’ strategic link choice under independently distributed O‐D demands. The convexity of the mathematical formulation is proved when each O‐D demand is assumed to follow a Poisson distribution independently; link flow distributions and users’ strategic link choice are also proved to be unique. Network performance measures are given analytically. A numerical analysis is conducted on the Sioux Falls network. A Monte Carlo method is used to simulate network performance measures, which are then compared to the results computed from the analytical expression. It is illustrated that the model is capable of accounting for demand volatility while maintaining computation efficiency.

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Multi-objective α-reliable path finding in stochastic networks with correlated link costs: A simulation-based multi-objective genetic algorithm approach (SMOGA)

Cited by 82 publications

References 28 publications

Multi-class time reliability-based congestion pricing model based on a degradable transportation network

Multi-class time reliability-based congestion pricing model based on a degradable transportation network

Real time route guidance with correlated link cost

A Strategic User Equilibrium for Independently Distributed Origin‐Destination Demands

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