Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Du, Muqing; Jiang, Xiaowei; Cheng, Lin; Zheng, Changjiang

doi:10.1155/2017/9814909

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…We must consider the impact of uncertain demand patterns on critical link identification results. The highly saturated number is counted at each link whenever its V/C ratio is greater than 0.9 [23]. As a result, link saturation ratios are calculated as [total saturated number/number of samples].…”

Section: Critical Link Identification With Uncertain Demandmentioning

confidence: 99%

“…To consider the demand uncertainty in a traffic optimization model, we must introduce demand variables into the modeling process. Current uncertainty optimization theory consists of three main approaches: stochastic programming, robust optimization, and fuzzy programming [23]. To ensure that the system performance is within an acceptable range, these three methods consider all possible values of the uncertain parameters while seeking the optimal solution under the condition of constraints.…”

Section: Introductionmentioning

confidence: 99%

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An Assessment Scheme for Road Network Capacity under Demand Uncertainty

Zhou

et al. 2023

Applied Sciences

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Network capacity is a vital index with which to assess the operation of traffic networks. The majority of existing traffic network capacity models are formed as bi-level programming, which maximizes the traffic flows under equilibrium constraints and is extremely dependent on the current origin–destination (O–D) travel demand. However, an accurate O–D matrix is not easy to obtain in practice. This article aims to provide an assessment method for traffic network capacity under demand uncertainty. To consider the variation in real demand in traffic networks, the current travel demand is treated as unknown parameters that are defined inside a restricted set. Based on the hypothesis of different probability distributions, the road network capacity is calculated by repeated capacity loading experiments with a random sampling of uncertain parameters. To improve the efficiency of the repeated calculations of the traffic assignment model, a sensitivity-analysis-based (SAB) approximation method was developed to avoid the double calculation of the network capacity model for each random O–D matrix. The SAB method significantly improved the calculation efficiency while ensuring accuracy. Using simulation experiments, we researched the reliability of road network capacity and the probabilities of high congestion for each link under uncertain demand.

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Section: Critical Link Identification With Uncertain Demandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Assessment Scheme for Road Network Capacity under Demand Uncertainty

Zhou

et al. 2023

Applied Sciences

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“…We assume that only five charging facilities are allowed to be installed in the whole Nguyen-Dupuis network to charge the electric vehicle, and only one charging pile can be installed in each link, which obviously results in p = 5. Following the existing studies that also utilized BPR function [27], set the function coefficients α and β in travel time function (3) on link a as 0.15 and 4, respectively. The value of time ρ = 4, c g = 0.16 and c e = 0.04 are the component parameters of the generalized travel cost of gasoline and electric vehicle on link a separately.…”

Section: Figure 1 Nguyen-dupius Networkmentioning

confidence: 99%

Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network

Liu

Cao

Luo

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

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This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying this problem is that link capacity constraint is mostly insufficient or missing in the studies of traditional user equilibrium models, thereby resulting in the ambiguous of the definition of road traffic network status. Adding capacity constraints to the road network is a compromise to enhance the reality of the traditional equilibrium model. In this paper, we provide a two-layer model for evaluating the efficiency of the charging facilities under the condition of considering the link capacity constraint. The upper level model in the proposed bi-level model is a nonlinear integer programming formulation, which aims to maximize the captured link flows of the battery electric vehicles. Moreover, the lower level model is a typical traffic equilibrium assignment model except that it contains the link capacity constraint and driving distance constraint of the electric vehicles over the mixed road network. Based on the Frank-Wolfe algorithm, a modified algorithm framework is adopted for solving the constructed problem, and finally, a numerical example is presented to verify the proposed model and solution algorithm.

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“…Yin et al analyzed the influence of node capacity by establishing a cascading failure model of scale-free network based on node degree [5]. Similarly, there are some research flows focusing on the capacity and load distribution [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Invulnerability Simulation of Urban Agglomeration Passenger Transport Network under Incomplete Information Attack Strategy

Yang

Zhu

2021

Advances in Civil Engineering

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This paper explores the invulnerability of urban agglomeration transportation network under the incomplete information attack strategy. This approach employed the site mapping method to construct the urban agglomeration composite transportation network model, and the network is weighted based on the actual passenger flow. Then the nodes are defined according to the overload conditions. In addition, based on the capacity-load model, the cascading failure model of the urban agglomeration passenger transport network is constructed, and the incomplete information attack strategy and network invulnerability measure index are determined. Finally, the case of Hu-Bao-E-Yu urban agglomeration is simulated to quantify the effects of attack strategies with varied information level, node load factors, and capacity weight and distance weight of the residual connected edge. The results reveal that the network crash speed is positively related to the information span of the attacker unless the information span exceeds 0.9 or accuracy exceeds 0.6. When the information span is low, the information accuracy δ has a critical impact on the network crash speed. Moreover, in the presence of attack, high or low values of node load factor are conducive to the improvement of network invulnerability. As a scale-free network, urban agglomeration transportation network shows strong robustness to random attacks and exhibits vulnerability to deliberate attacks. The capacity weight value α and distance weight value β of residual connected edge have different effects on the network invulnerability under different information span strategies.

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Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Cited by 8 publications

References 20 publications

An Assessment Scheme for Road Network Capacity under Demand Uncertainty

An Assessment Scheme for Road Network Capacity under Demand Uncertainty

Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network

Invulnerability Simulation of Urban Agglomeration Passenger Transport Network under Incomplete Information Attack Strategy

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