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.
Modelling human behaviour in emergencies has become an important issue in safety engineering. Good behavioural models can help increase the safety of transportation systems and buildings in extreme situations like fires or terrorist attacks. Although it is well known that the interaction with other decision makers affects human behaviour, the role of social influences during evacuations still needs to be investigated. This paper contributes to fill this gap by analysing the occurrence of Herding Behaviour (HB) in exit choice. Theoretical explanations of HB are presented together with some modelling approaches used in different fields where HB is relevant. A discrete choice stated preference experiment is then carried out to study the role of HB in the decision-making process concerning exit choice during evacuation. A binary logit model is proposed showing that the occurrences of HB are affected by both environmental and personal factors. In particular, the model shows that the personal aptitude to HB can have a key role in selecting an exit.
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