An improved origin-based algorithm for solving the combined distribution and assignment problem

Xu, Meng; Chen, Anthony; Gao, Ziyou

doi:10.1016/j.ejor.2007.04.047

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…Zhou et al (2009) provided alternative formulations, including mathematical programming (MP) formulation and variational inequality (VI) formulations, for the CTDM that integrates trip generation, trip distribution, modal split, and traffic assignment using the random utility theory framework. Several algorithms have also been proposed in the literature for solving various combined travel demand model formulations (e.g., the partial linearization algorithm by Evans (1976) for solving the combined distribution and assignment (CDA) problem as a constrained convex optimization problem; the origin-based algorithm by Bar-Gera and Boyce (2003) for solving the CDA problem; the improved origin-based algorithm by Xu et al (2008) by adopting the modified origin-destination flow update strategy proposed by Huang and Lam (1992) to enhance its computational efficiency; and the Block Gauss-Seidel decomposition approach coupled with the method of successive averages by Florian et al (2002) for solving the variational inequality formulation).…”

Section: Sequential Four-step and Combined Travel Demand Modelsmentioning

confidence: 99%

Sensitivity-based uncertainty analysis of a combined travel demand model

Yang

Chen

et al. 2013

Transportation Research Part B: Methodological

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Travel demand forecasting is subject to great uncertainties. A systematic uncertainty analysis can provide insights into the level of confidence on the model outputs, and also identify critical sources of uncertainty for enhancing the robustness of the travel demand model. In this paper, we develop a systematic framework for quantitative uncertainty analysis of a combined travel demand model (CTDM) using the analytical sensitivity-based method. The CTDM overcomes limitations of the sequential four-step procedure since it is based on a single unifying rationale. The analytical sensitivity-based method requires less computational effort than the sampling-based method. Meanwhile, the uncertainties stemming from inputs and parameters can be treated separately so that the individual and collective effects of uncertainty on the outputs can be clearly assessed and quantified. Numerical examples are finally used to demonstrate the proposed sensitivity-based uncertainty analysis method for the CTDM.

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Section: Sequential Four-step and Combined Travel Demand Modelsmentioning

confidence: 99%

Sensitivity-based uncertainty analysis of a combined travel demand model

Yang

Chen

et al. 2013

Transportation Research Part B: Methodological

Self Cite

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“…The combined distribution and assignment (CDA) model [39,40] was adopted to plan the hydrogenation infrastructure. This model was adopted by Ryu et al [41], Yao et al [42] to solve optimization problems.…”

Section: Decision Modelingmentioning

confidence: 99%

Location and Volume Determination of Hydrogen Refueling Stations Based on Oligopoly Equilibrium

Huang¹,

Liu²,

Zhao³

et al. 2021

IJHT

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The energy transformation in China is picking up speed, thanks to the mass development and utilization of various clean energies. As an emerging clean energy, hydrogen energy has been highly recognized, and gradually introduced to industry and transport. As a result, hydrogen fuel cell vehicles (HFCVs) attract a growing attention from the government and the public. However, the is a severe lack of hydrogen refueling stations (HRSs). Therefore, this paper explores into the determination of HRS location and volume. The HRS investment market was treated as an oligopoly market, due to the huge investment and few investors of HRSs. To ensure the long-term balanced development of hydrogenation, it is assumed that all investors fully analyze the possible investment behaviors of competitors and HFCV driver preference before making any investment decision. On this basis, a multi-agent optimization model was established to determine the location and volume of HRSs. The results show that, to avoid supply-demand imbalance induced by information asymmetry, the HRSs should be constructed along with an intelligent software platform, which provides HFCV drivers with rich information on hydrogenation service. Our model helps investors determine the core issues of the HRS, including location, size, and supply scale.

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“…It is noteworthy that the four-step model is inherently inferior to the CDA model. Treating the solution of one step as the initial condition of the next step, the four-step model ignores the correlation among the steps, which results in the inconsistency problem (e.g., the travel times and time costs assumed in the trip distribution step disagree with the results of the traffic assignment) [25,26]. This problem means the four-step model has a worse performance for forecasting modern urban transportation because destination choice and route choice are related more closely with more detailed and accurate trip information accessible to travelers (as the example in Section 1).…”

Section: Applications Of the Cda Modelmentioning

confidence: 99%

The Combined Distribution and Assignment Model: A New Solution Algorithm and Its Applications in Travel Demand Forecasting for Modern Urban Transportation

Tan

Jiang

et al. 2019

Sustainability

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With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers’ behavior at present, in which case it is difficult for the conventional four-step travel demand forecasting model to have good performance. In this study, we apply the combined distribution and assignment (CDA) model to forecasting travel demand for modern urban transportation, in which travelers may choose the destination and path simultaneously. Furthermore, we present a new solution algorithm for solving the CDA model. With the network representation method that converts the CDA model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm based on the gradient projection (GP) algorithm to solve the converted CDA model. The new solution algorithm is designed to find a more accurate solution compared with the widely used algorithm, the Evans’ two-stage algorithm. Two road networks, Sioux Falls and Chicago Sketch, are used to verify the performance of the new algorithm. Also, we conduct some experiments on the Sioux Falls network to illustrate several applications of the CDA model in consideration of the influences of ITS.

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An improved origin-based algorithm for solving the combined distribution and assignment problem

Cited by 16 publications

References 31 publications

Sensitivity-based uncertainty analysis of a combined travel demand model

Sensitivity-based uncertainty analysis of a combined travel demand model

Location and Volume Determination of Hydrogen Refueling Stations Based on Oligopoly Equilibrium

The Combined Distribution and Assignment Model: A New Solution Algorithm and Its Applications in Travel Demand Forecasting for Modern Urban Transportation

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