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The importance of the adverse impacts of network degradation has stimulated substantial international research interest in transport network reliability, that is, the ability of degraded transport networks to cope with travel demand. Most of the recent research effort has focused on the reliability of urban passenger transport networks, in terms of the probability that the network will deliver a required standard of performance. This situation is characterised by high levels of congestion, a dense road network, and quantifiable probability of degradation of the network. Outside major urban centres, the situation is very different. The main dominant consideration in transport network infrastructure provision is accessibility - linking urban centres, providing regional coverage, and basic levels of accessibility for the non-urban community and economy. The network is sparse, congestion is not a significant issue, and access to essential community services and to markets is the major driving force underlying network development. In this context, the vulnerability of the network is perhaps more important than ‘reliability’. This paper develops the concept of network vulnerability. It begins by reviewing the current state of research into network reliability, then proposes extensions and adaptations to the reliability concepts that are more appropriate for strategic-level multi-modal transport systems. Several alternative definitions for vulnerability are proposed. The paper also discusses the development of algorithmic and visualisation tools that may be used to identify specific ‘weak spots’ in a network, where failure of some part of the transport infrastructure would have the most serious effects on access to specific locations and on overall system performance. Finally, the paper describes potential applications of network vulnerability concepts, and proposes directions for further research.

The objective of this paper is to give an overview of various reliability concepts that have been developed in the last decades. The paper first summarises various indicators that have been developed in order to measure the reliability of a network and then looks at techniques to calculate these indicators. The usefulness and limitations of the different indicators is discussed. The paper suggests that there is no single perfect indicator but that the choice of indicator and technique depends on several factors, including the viewpoint of the analyst and the type and range of interventions being considered. In order to assess the impact of incidents the authors propose to distinguish between three types of intervention, namely “benevolent”, “neutral” or random, and “malevolent”. Also discussed is why the provision of up-to-date information to the traveller has a central role to play when trying to minimise the impact of an incident.

The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem (DCNDP) and dynamic critical links detection problem (DCLDP) in stochastic networks. DCNDP and DCLDP consist of identifying the subset of nodes and links, respectively, whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs, in the graph modeling the transport network. The identification of such nodes (or links) helps to better control the road traffic and predict the necessary measures to avoid congestion.

A Markovian decision process is used to model the shortest path problem under dynamic traffic conditions. Effective algorithms to determine the critical nodes (links) while considering the dynamicity of the traffic network are provided. Also, sensitivity analysis toward capacity reduction for critical links is studied. Moreover, the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.

The numerical results demonstrate that the use of dynamic shortest path (time dependency) as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.

The research in this paper also revealed several challenges, which call for future investigations. First, the authors have restricted our experimentation to a small network where the only focus is on the model behavior, in the absence of historical data. The authors intend to extend this study to very large network using real data. Second, the authors have considered only congestion to assess network’s criticality; future research on this topic may include other factors, mainly vulnerability.

Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks. This leads to design optimized smart transport plans particularly in disaster management, to improve the emergency evacuation effeciency.

The paper provides a novel approach to solve critical nodes/links detection problems. In contrast to the majority of research works in the literature, the proposed model considers dynamicity and betweenness while taking into account the stochastic aspect of transport networks. This enables the approach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

Global sourcing, just‐in‐time inventory control and the growth of the global marketplace have made intermodal transport an increasingly important aspect of distribution and logistics. Discusses intermodal system modelling from the perspective of intermodalism as an integrated transport service rather than as a technology. Reviews current modelling paradigms and implementations and proposes an innovative approach based on the abstract concept of logistical events and treating intermodalism as a sequence of linked events. Addresses some of the technical issues involved in embedding the proposed framework in a traditional network model.

This study explores the main factors considered when shippers and container shipping lines select their import/export and transshipment ports. In the present study, 38 container port selection indices were chosen from the previous research and field interviews. The scores of the 38 items were collected via survey to the three major maritime/port market players: shippers, shipping lines, and container terminal operators. In order to analyze the different priorities imposed on the port selection factors by the three market players, the ANOVA method has been employed. The empirical test shows the different perceptions about port selection attributes among service suppliers and demanders. In addition, the 38 items have been categorized into seven key factors through an exploratory factor analysis. The ANOVA technique was employed again to analyze the perspective differences for the port selection factors among the market players. The results show that there are significant differences among the players assessing the importance of the three port choice factors: liners and terminal operators give more weight to ‘hinterland and terminal basic conditions’ than shippers; terminal operators do not take ‘line operation’ as seriously as carriers and shippers; the factor of ‘terminal operation’ is more significantly considered by liners and terminal operators than by shippers.

Ports are the key elements of maritime transportation, which is crucial for world trade. Approximately 180 port facilities are located in Turkey. After 2007, 5 of the ports, which are formerly owned by Turkish Republic Railways Administration (TRRA), are privatized. The aim of the study is to evaluate the performance of these privatized ports by multi-criteria decision-making (MCDM) approach.

The application process is performed by a MCDM model. This model includes both criteria (dry bulk, liquid bulk, general cargo, container, RO-RO capacity, total port area, total berth, total berths length and depth) and alternatives (Mersin, Samsun, Bandirma, Iskenderun and Derince Ports). It determines the weights of the criteria by entropy and ranks the alternatives by ARAS and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods.

The results of entropy, ARAS and TOPSIS methods are compared. According to these results, “container” is the most important criteria while Mersin port has the best performance.

In the literature, most of the studies about this subject were analyzed by data envelopment analysis (DEA) and there are no studies had been taken into consideration ports that are owned by TRRA, in Turkey. Moreover, few of these studies used integrated MCDM models, and this is the first study that integrates entropy, ARAS and TOPSIS methods in this field.