Assessing Transport Reliability: Malevolence and User Knowledge

Nicholson, Alan; Schmöcker, Jan-Dirk; Bell, M.G.; Iida, Yasunori

doi:10.1108/9781786359544-001

Cited by 40 publications

(31 citation statements)

References 8 publications

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“…Robustness can be subdivided in five elements (prevention, redundancy, compartmentalization, resilience and flexibility) as is explained in section 2.4. Furthermore, as is explained by Nicholson et al (2001), if the user has a high level of information (i.e. information is provided well in advance, and route guidance is available once the trip has commenced), the range of available options is greater and the consequence of degradation is reduced.…”

Section: <Figure 2 About Here>mentioning

confidence: 99%

“…A node is vulnerable if the loss of substantial degradation of a small number of links results in a significant reduction in the accessibility of that node, as measured by a standard index of accessibility. It should be noted that the second definition of vulnerability ignores probability; this vulnerability is really a measure of the consequence of degradation (Nicholson et al, 2001). …”

Section: Definitionmentioning

confidence: 99%

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A framework for robustness analysis of road networks for short term variations in supply

Snelder

Zuylen

Immers

2012

Transportation Research Part A: Policy and Practice

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There is a growing awareness that road networks, are becoming more and more vulnerable to unforeseen disturbances like incidents and that measures need to be taken in order to make road networks more robust. In order to do this the following questions need to be addressed: How is robustness defined? Against which disturbances should the network be made robust? Which factors determine the robustness of a road network? What is the relationship between robustness, travel times and travel time reliability? Which indicators can be used to quantify robustness? How can these indicators be computed? This paper addresses these questions by developing a consistent framework for robustness in which a definition, terms related to robustness, indicators and an evaluation method are included. By doing this, policy makers and transportation analyst are offered a framework to discuss issues that are related to road network robustness and vulnerability which goes beyond the disconnected definitions, indicators and evaluation methods used so far in literature. Furthermore, the evaluation method that is presented for evaluating the robustness of the road network against short term variations in supply (like incidents) contributes to the problem of designing robust road networks because it has a relatively short computation time and it takes spillback effects and alternative routes into account.

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Section: <Figure 2 About Here>mentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

A framework for robustness analysis of road networks for short term variations in supply

Snelder

Zuylen

Immers

2012

Transportation Research Part A: Policy and Practice

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“…Clearly, the propagation and dissipation of congestion will be determined in part by the reactions of network users. Nicholson et al (2003) emphasize that the loss of performance following an incident also depends on the degree of malevolence involved in the incidents and on the level of user knowledge regarding malevolence.…”

Section: Literature Reviewmentioning

confidence: 99%

Attacker–defender models and road network vulnerability

Bell

Kanturska

Schmöcker

et al. 2008

Phil. Trans. R. Soc. A.

116

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The reliability of road networks depends directly on their vulnerability to disruptive incidents, ranging in severity from minor disruptions to terrorist attacks. This paper presents a game theoretic approach to the analysis of road network vulnerability. The approach posits predefined disruption, attack or failure scenarios and then considers how to use the road network so as to minimize the maximum expected loss in the event of one of these scenarios coming to fruition. A mixed route strategy is adopted, meaning that the use of the road network is determined by the worst scenario probabilities. This is equivalent to risk-averse route choice. A solution algorithm suitable for use with standard traffic assignment software is presented, thereby enabling the use of electronic road navigation networks. A variant of this algorithm suitable for risk-averse assignment is developed. A numerical example relating to the central London road network is presented. The results highlight points of vulnerability in the road network. Applications of this form of network vulnerability analysis together with improved solution methods are discussed.

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“…The source of uncertainty within the transport network can be classified into two main categories i) variations in the travel demand and ii) variations in the supply (10). The latter includes connectivity of nodes or links, variations of capacity or free-flow travel time, and others.…”

Section: Introductionmentioning

confidence: 99%

Reliable Network Design Problem: Case with Uncertain Demand and Total Travel Time Reliability

Sumalee

Watling

Nakayama

2006

Transportation Research Record: Journal of the Transportation R

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This paper deals with Reliable Network Design Problem (RNDP) in which the main sources of uncertainty are variable demand and route choice. The objective is to maximize the network total travel time reliability (TTR) which is defined as the probability of the network total travel time to be less than a threshold. The paper presents a framework of stochastic network model with Poisson distributed demand and uncertain route choice. The travelers are assumed to choose their routes to minimize their perceived expected travel cost following Probit Stochastic Users' Equilibrium (SUE) condition. The paper presents an analytical method for approximating the first and second moments of the total travel time. These moments are then fitted with a log-normal distribution. The paper then tackles the design problem in which the analytical derivative of the TTR is derived using the sensitivity analysis of the equilibrated path choice probability. This derivative is then supplied to a gradient-based optimization algorithm to solve the RNDP. The algorithm is tested with a small network example.

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Assessing Transport Reliability: Malevolence and User Knowledge

Cited by 40 publications

References 8 publications

A framework for robustness analysis of road networks for short term variations in supply

A framework for robustness analysis of road networks for short term variations in supply

Attacker–defender models and road network vulnerability

Reliable Network Design Problem: Case with Uncertain Demand and Total Travel Time Reliability

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