A capacity related reliability for transportation networks

Chen, Anthony; Yang, Hai; Lo, Hong Kam; Tang, Wilson H.

doi:10.1002/atr.5670330207

Cited by 253 publications

(113 citation statements)

References 8 publications

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“…The first framework for analyzing degradable networks has been constructed by Du and Nicholson [23]. Then, capacity reliability was provided by Chen et al [24] and [25] as explained in Section I. Dianhai et al [26] presented a comprehensive survey about different kind of …”

Section: Literature Reviewmentioning

confidence: 99%

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An Efficient Imperialism Competitive Algorithm for a Reliable Hub Covering Location Problem

Sadeghi¹,

Tavakkoli‐Moghaddam²

2015

IJMLC

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Abstract-Hubs are critical elements that benefit networks by switching, distributing and concentrating flows. A hub location problem is common in a lot of industries, such as transportation networks, telecommunication networks, postal delivery networks and express shipment. Furthermore, a transportation network plays a vital role in each country. Nonetheless, during a day many factors such as traffic incidence, natural disasters cause some degradation in the transportation network. As a result, in this paper, we study a reliable hub covering location problem in a degradable transportation network to minimize the total transportation cost. For achieving near-optimal solutions for a real-sized problem an imperialism competitive algorithm (ICA) is proposed. Also the effectiveness of the proposed ICA is shown by some computational experiments. At last, conclusion is provided.

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Section: Literature Reviewmentioning

confidence: 99%

“…The definition of reliability in a transportation network is extracted from discipline of reliability engineering [8]. Road capacity reliability is proposed as a probability that assures the incoming flow to a road is less than its available capacity [9], [10].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Imperialism Competitive Algorithm for a Reliable Hub Covering Location Problem

Sadeghi¹,

Tavakkoli‐Moghaddam²

2015

IJMLC

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“…Cassir and Bell (2002) applied the same method as proposed in Bell et al (1993) and calculated the travel time reliability by assuming both stochastic O-D demand flow and stochastic link capacity. Chen et al (1999) analyzed network capacity reliability under stochastic link capacity. They applied the Monte Carlo simulation technique for calculating the network capacity reliability.…”

Section: Introductionmentioning

confidence: 99%

Estimating the value of travel time and of travel time reliability in road networks

Uchida

2014

Transportation Research Part B: Methodological

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This study proposes two network models which simultaneously estimate the value of travel time and of travel time reliability based on the risk-averse driver's route choice behaviour. The first model is formulated as a utility maximization problem under monotonic and separable link travel times, whereas the second model is formulated as a utility maximization problem under non-monotonic and non-separable link travel times. The proposed models have the same structure as a user equilibrium (UE) traffic assignment problem with elastic demand. It is shown that the first model, which addresses independent stochastic capacity, is formulated as an optimization problem with a unique solution and is solved by using an algorithm for a UE traffic assignment problem with fixed demand. The second model, which addresses both stochastic Origin-Destination (O-D) flow and stochastic link capacity, is formulated as a nonlinear complementary problem. O-D demand functions formulated in the proposed models are derived from the utility maximization behaviour of the driver in the network. Therefore, the network models proposed in this study are consistent with those of studies that address the value of travel time and of travel time reliability based on utility maximization behaviour without considering the driver's route choice. Numerical experiments are carried out to demonstrate the models presented in this study.Keywords: value of travel time, value of travel time reliability, road network INTRODUCTIONIn light of the need to evaluate travel time reliability in terms of its effect on mobility in transport networks, many studies have developed models that address uncertainties in the network. Uncertainties in the network can be categorized into the three main factors of supply, demand and travel behaviour. Studies on travel time reliability in the network began with those that address stochastic OriginDestination (O-D) demand flow. By assuming O-D demand flow that follows normal distributions, Asakura and Kashiwadani (1991) solved User Equilibrium (UE) traffic assignment problems several times by using a set of O-D demand flows that were sampled from normal distributions for the purpose of estimating travel time reliability. Clark and Watling (2005) proposed an equilibrium model that calculates travel time reliability under stochastic O-D demand flow when that flow follows a Poisson distribution. They employed probit-based Stochastic User Equilibrium (SUE) for the driver's route choice behaviour. They applied a method proposed by Isserlis (1918) for the purpose of calculating travel time reliability, i.e., variance of stochastic travel time. Nakayama and Takayama (2003) proposed an equilibrium model which assumes that O-D demand flow follows a binomial distribution. UE traffic assignment was employed for expressing the driver's route choice behaviour. They calculated travel time reliability by applying a moment-generating function. Zhou and Chen (2008) proposed an equilibrium model which assumes that O-D demand flow follows a...

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“…A more accurate measure in this sense is capacity reliability, which can be defined as "the probability that the network can accommodate a specific demand level" [5]. Capacity reliability, while accounts for the ability of a system to satisfy demand through adequate service, it does not exclusively indicate how well demand is satisfied.…”

Section: Introductionmentioning

confidence: 99%

A Qualitative Approach to Analyze the Security and Survivability of Critical Infrastructure Networks

Abdel-Rahim¹,

Ahmed²,

Ochoa-Huaman³

2014

IJET

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Abstract-The paper presents a qualitative approach to analyze the security and survivability of critical infrastructure networks. The analysis identifies threat mitigation strategies for each threat identified and provides suggestions for improved security and survivability. The proposed approach could be applied during the planning and design phase of new projects to evaluate different design alternatives or to assess the vulnerability and survivability of an existing system. The paper presents the results of an in-depth interview survey conducted to identify the normal usage scenarios and possible attack scenarios and their impact on the operations of transportation management centers. The proposed approach provides a significant addition to the current design practices by integration system security and survivability in the design process. A cost-benefit analysis of the proposed mitigation strategies should provide transportation officials with a decision making tool to evaluate different design alternatives and to improve the overall survivability of urban city networks. While the case-study presented in the paper focused on urban transportation networks, the proposed qualitative approach presented can be applied to any other critical infrastructure distribution, control, and monitoring networks.Index Terms-Infrastructure networks, security and survivability, transportation management, urban transportation networks.

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A capacity related reliability for transportation networks

Cited by 253 publications

References 8 publications

An Efficient Imperialism Competitive Algorithm for a Reliable Hub Covering Location Problem

An Efficient Imperialism Competitive Algorithm for a Reliable Hub Covering Location Problem

Estimating the value of travel time and of travel time reliability in road networks

A Qualitative Approach to Analyze the Security and Survivability of Critical Infrastructure Networks

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