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The purpose of this paper is to reveal the pattern of passengers' transferring on occasion of a large crowd being stranded at transportation hubs (such as a bus station, railway station, airport, etc.) in climate disasters, and then propose the proper policy recommendations for the government to evacuate stranded passengers.

A model is established based on Bayesian network and influence diagram to catch the features of a passenger's decision‐making process, and the transition probabilities of passengers are revised on the basis of the theory of herd behaviors in information to describe the influence of group behaviors on passenger individuals. Subsequently, a multi‐agent model is developed in Repast platform in Java language, and simulation and analysis are also made.

The results of simulation show that it is possible to apply the theory of herd behaviors and the multi‐agent method in analyzing the effectiveness of government policies on evacuating stranded passengers in climate disasters.

The research of this paper has important practical significance for the government to developing policies to evacuating stranded passengers in climate disasters, and is a useful exploration to open up new methodologies for emergency management.

Flights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.

Taking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.

Comparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.

Based on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station.

The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced.

The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation.

The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

Based on cost of quality (CoQ) research, this study aims to highlight the importance of incorporating the costs to customers in contributing to service quality when examining how customers respond to possible service failures [quality assurance behavior (QAB)]. Consequently, this study also aims to show how the CoQ framework can be a useful tool to the service industry in determining enhancements in quality and related expenditures.

Using the airline industry as a case example, this study empirically tests the impact of predicted service quality and its associated costs on an individual’s QAB (wait time spent at the departing airport) through revealed preferences. The study uses survey data from more than 4,000 passengers matched with travel-specific quality information archived by the US Department of Transportation (DOT).

This study finds that customers are willing to increase their level of QAB when informed of an increased probability of service failure. This study also finds that the level of QAB varies depending on anticipated customer costs of avoiding or responding to service failures.

Findings of this study emphasize the need for shared responsibility between service providers and their customers in making decisions on the provision of service quality, as helping customers adjust the appropriate level of QAB may result in greater efficiency and higher quality of service.

This study conceptualizes and empirically tests causal relationships between expected quality and customer efforts (QAB), thus contributing to operations literature examining CoQ in a service setting. This study argues that it is critical to consider shared responsibilities between co-producers (service providers and customers) in service operations studies.

The purpose of this study is to identify and understand the emotions behind a passenger’s airport experience and how this can inform digital channel engagements.

This study investigates the emotional experience of 200 passengers’ journeys at an Australian domestic airport. A survey was conducted which implemented the use of Emocards and an interview approach of laddering. The responses were then analysed into attributes, consequences and values.

The results indicate that across key stages of the airport (parking, retail, gates and arrivals) passengers had different emotional experiences (positive, negative and neutral). The attributes, consequences and values behind these emotions were then used to propose digital channel content and purpose of various future digital channel engagements.

By gaining emotional insights, airports are able to generate digital channel engagements, which align with passengers’ needs and values rather than internal operational motivations. Theoretical contributions include the development of the technology acceptance model to include emotional drivers as influences in the use of digital channels.

This paper provides a unique method to understand the passengers’ emotional journey across the airport infrastructure and suggest how to better design digital channel engagements to address passenger latent needs.