The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.
Abstract
Purpose
The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.
Design/methodology/approach
A narrative approach is taken in this review of the current body of knowledge.
Findings
Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.
Originality/value
The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.
目的
本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。
设计/方法
本文采用叙述性回顾方法对当前知识体系进行了评论。
研究结果
本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。
独创性
本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。
Objetivo
El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.
Diseño/metodología/enfoque
En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.
Resultados
Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.
Originalidad
Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.
Details
Keywords
Weiqiang Tang, Chengbin Wang, Liuwei Shan and Haiyan Gao
This paper aims to solve the uncertainty problem of hypersonic vehicle tracking control; an adaptive terminal sliding mode control (TSMC) method based on extended state observer…
Abstract
Purpose
This paper aims to solve the uncertainty problem of hypersonic vehicle tracking control; an adaptive terminal sliding mode control (TSMC) method based on extended state observer (ESO) is proposed. The combination of adaptive techniques, TSMC and ESO offers an effective approach for managing uncertain systems.
Design/methodology/approach
The dynamic model of a hypersonic vehicle is transformed into two control-oriented subsystems. The control system design incorporates an adaptive technique, an ESO and a TSMC. The ESO estimates the primary uncertainties, while the adaptive technique determines the upper limit of secondary uncertainties. These estimates are used for the design of the TSMC law. In addition, the filter is used to generate the reference trajectory to improve the dynamic performance of the system. The stability of the closed-loop system is proved by the Lyapunov stability theory.
Findings
A robust control system for hypersonic vehicles is developed with guaranteed stability and strong adaptability to various uncertainties such as parameter variations, external disturbances and actuator faults. Furthermore, the proposed system demonstrates enhanced dynamic performance compared to observer-based sliding mode control. Specifically, for the velocity and altitude tracking control, the settling time of the proposed sliding mode control is approximately 100 s and 70 s shorter than that of the observer-based sliding mode control, respectively.
Originality/value
Different from the single equivalent treatment, various uncertainties here are classified and treated with different strategies, which improves the disturbance rejection ability of the control system. This ability is of great significance for enhancing the autonomy, adaptability and reliability of hypersonic vehicles in extreme environments.
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Keywords
Fei Qi, Yiwei Ge and Xianjun Liu
This paper aims to present a kinematics performance analysis and control for a continuum robot based on a dynamic model to achieve control of the robot.
Abstract
Purpose
This paper aims to present a kinematics performance analysis and control for a continuum robot based on a dynamic model to achieve control of the robot.
Design/methodology/approach
To analyze the motion characteristics of the robot, its kinematics model is derived by the geometric analysis method, and the influence of the configuration parameters of the robot on workspace is investigated. Moreover, the dynamic model is established by the principle of virtual work to analyze the mapping relationship among the bending shape, the forces/torques applied to the robot. To achieve better control of the robot, a control strategy for continuum robot based on the dynamic model is put forward.
Findings
Results of the simulations and experiments verify the proposed continuum structure and motion model, the maximum position error is 5.36 mm when the robot performs planar bending motion and the average position error of the robot in spatial circular motion is 5.84 mm. The proposed model can accurately describe the deformation movement of the robot and realize its motion control with a few position errors.
Originality/value
The kinematics analysis and control model proposed in this paper can achieve precise control of the robot, which can be used as a reference for the motion planning and shape reconstruction of continuum robot.
Details
Keywords
Dijoy Johny, Sidhartha S. Padhi and T.C.E. Cheng
The purpose of this research is to address the challenges of selecting optimal drones for disaster response operations under uncertainties. Traditional static (deterministic…
Abstract
Purpose
The purpose of this research is to address the challenges of selecting optimal drones for disaster response operations under uncertainties. Traditional static (deterministic) models often fail to capture the complexities and uncertainties of disaster scenarios. This study aims to develop a more resilient and adaptable decision-making framework by integrating the best-worst method (BWM) with stratified multi-criteria decision-making (SMCDM), focusing on various uncertainty scenarios such as weather conditions, communication challenges and navigation and control issues.
Design/methodology/approach
The methodology involves identifying seven essential criteria for drone evaluation, guided by contingency theory. The BWM derives optimal weights for each criterion by comparing the best and worst alternatives. The SMCDM incorporates different uncertainty scenarios into the decision-making process. Sensitivity analysis assesses the robustness of decisions under various criterion weightings and operational scenarios. This integrated approach is demonstrated through a practical application to the Kerala flood scenario.
Findings
The integrated stratified BWM method proves to be highly effective in adapting to different uncertainty scenarios, enabling decision-makers to consistently identify the optimal drone for disaster response. The method’s ability to account for uncertain conditions such as weather, communication challenges and navigation issues ensures that the optimal drone is selected based on the situation at hand.
Research limitations/implications
The methodology fills critical gaps in the literature by offering a comprehensive model that incorporates various scenarios and criteria for optimal drone selection. However, there are certain limitations. The reliance on expert opinions for criterion weightings introduces subjectivity, potentially affecting the generalizability of the results. In addition, the study’s focus on a single case, the Kerala floods, limits its applicability to other geographic contexts. Integrating real-time data analytics into the decision-making process could also enhance the model’s adaptability to evolving conditions and improve its practical relevance.
Practical implications
This research offers a practical, adaptable framework for selecting optimal drones in disaster scenarios. By integrating BWM with SMCDM, the methodology ensures decision-makers can account for real-time uncertainties, such as weather or communication disruptions, to make more informed choices. This leads to better resource allocation and more efficient disaster response operations, ultimately enhancing the speed and effectiveness of relief efforts in various contexts. The method’s ability to adjust based on scenario-specific factors ensures that drones are optimally deployed according to the unique demands of each disaster.
Social implications
By incorporating SMCDM, the proposed methodology assists decision-makers in appropriately choosing drones based on their characteristics crucial for specific scenarios, thereby enhancing the efficiency and effectiveness of relief operations.
Originality/value
This study presents a unique integration of the BWM with SMCDM, creating a dynamic framework for drone selection that addresses the challenges posed by uncertain disaster environments. Unlike traditional methods, this approach allows decision-makers to adjust criteria based on evolving disaster conditions, resulting in more reliable and responsive drone deployment. The method bridges the gap in existing literature by offering a comprehensive tool for disaster response, providing new insights and practical applications for optimizing drone operations in complex, real-world scenarios.
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Keywords
Annie Singla and Rajat Agrawal
This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right…
Abstract
Purpose
This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.
Design/methodology/approach
iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.
Findings
The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.
Originality/value
iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.
Details
Keywords
Rehab Iftikhar and Mehvish Rashid
Knowledge loss and retention are common phenomena not only for organizations but also for interorganizational projects, where multiple organizations are involved. This paper sets…
Abstract
Purpose
Knowledge loss and retention are common phenomena not only for organizations but also for interorganizational projects, where multiple organizations are involved. This paper sets out to understand why knowledge loss occurs and how to retain knowledge, particularly in the context of interorganizational projects. For this purpose, the Orange Line Metro Rail Transit System in Lahore, the Bus Rapid Transit in Peshawar and the Green Line Metrobus in Karachi, all in Pakistan, were examined.
Design/methodology/approach
A multi-case study approach is employed in this paper. Empirical data were collected through semi-structured interviews and archival documents. To analyze the data, we used a three-step thematization procedure, which included data condensation, data presentation and conclusion.
Findings
The findings present the determinants of knowledge loss, including high time pressure, memory decay, lack of sharing of personal knowledge and tenuous relationships between salary and experience. For knowledge retention, the findings provide evidence of the transformation of the working environment, externalization, job shadowing, the hiring and rehiring individuals and the provision of incentives.
Originality/value
By examining knowledge loss and retention in interorganizational projects, this article contributes to the literature on knowledge-based theory.