Tourism is one of the activities with high benefits on the development and for many regions,enabling the integration of local populations and economies.In our natural laboratory,the Azores Island of São Miguel,an...Tourism is one of the activities with high benefits on the development and for many regions,enabling the integration of local populations and economies.In our natural laboratory,the Azores Island of São Miguel,an important share of tourists identifies adventure,leisure and touch with nature,as the main reasons for the visit.The use of footpaths can contribute to the satisfaction of tourists,promoting tourism and the region’s development during their movements on the tourism network,tourists appreciate different types of attractions and need the support of a set of facilities.Tourist decisions are not always done in a rational way,emotions add even more complexity to the human decision process.The movement of tourists within a destination depends on factors related to tourist characteristics,like the time budgets,preferences or destination knowledge,and destination features related to attractions characteristics or accessibility level.The existence of a mathematical model that incorporates the main factors that explain the movement of independent tourists within a destination,in a dynamic way,will make possible the creation of an adaptable software tool.This tool will meet the specific needs of tourists,allowing the use of the network in an optimal way by the different tourist profiles,and the needs of regional government and business,allowing better decisions and the offer of relevant tourism products.This article is based on the authors’previous research and identifies the relevance of tourism for regional development,finds the main tourists’mobility criteria on the study territory,using as main support for the footpath network,recognises the necessary modelling process and develops the foundation for the building of the mathematical model that explains the movement of tourists within the destination,making possible a future adaptable software tool.展开更多
This paper provides a tool to identify key aspects for an airport to achieve global hub status for a given airline and determines whether these factors are related to the facility’s infrastructure,its region,or both....This paper provides a tool to identify key aspects for an airport to achieve global hub status for a given airline and determines whether these factors are related to the facility’s infrastructure,its region,or both.Despite the frequent use of the term‘hub’,there is little academic consensus on its exact definition in air transport.Many define a hub based on passenger numbers rather than the concentration of flights and passengers from the main carrier.This study addresses this gap by analyzing the factors influencing the definition of a hub and the commonalities among global hubs.Data from 300 major airports,including internal variables(runways,terminals,gates and area)and external variables(economy,population,attractiveness),were collected.A Binary Logistic Regression(BLR)model identified key aspects influencing hub status,with the assistance of an Exploratory Factor Analysis(EFA)that grouped the variables into factors.The binary‘hub’variable was defined by the main carrier’s activity and the Global Airport Connectivity Index(GACI).The factor with the highest coefficient primarily involved internal variables and,to a lesser extent,global attractiveness and population.The factor with the lowest coefficient related to the region economy.The BLR correctly identified hub status in 93.3%of cases,with 68.3%accuracy for hub airports.Airports not correctly identified by the model mostly present a lack or underutilization of existing infrastructure.展开更多
Road safety modeling is a valuable strategy for promoting safe mobility,enabling the development of crash prediction models(CPM)and the investigation of factors contributing to crash occurrence.This modeling has tradi...Road safety modeling is a valuable strategy for promoting safe mobility,enabling the development of crash prediction models(CPM)and the investigation of factors contributing to crash occurrence.This modeling has traditionally used statistical techniques despite acknowledging the limitations of this kind of approach(specific assumptions and prior definition of the link functions),which provides an opportunity to explore alternatives such as the use of machine learning(ML)techniques.This study reviews papers that used ML techniques for the development of CPM.A systematic literature review protocol was conducted,that resulted in the analysis of papers and their systematization.Three types of models were identified:crash frequency,crash classification by severity,and crash frequency and severity.The first is a regression problem,the second,a classificatory one and the third can be approached either as a combination of the preceding two or as a regression model for the expected number of crashes by severity levels.The main groups of techniques used for these purposes are nearest neighbor classification,decision trees,evolutionary algorithms,support-vector machine,and artificial neural networks.The last one is used in many kinds of approaches given the ability to deal with both regression and classification problems,and also multivariate response models.This paper also presents the main performance metrics used to evaluate the models and compares the results,showing the clear superiority of the ML-based models over the statistical ones.In addition,it identifies the main explanatory variables used in the models,which shows the predominance of road-environmental aspects as the most important factors contributing to crash occurrence.The review fulfilled its objective,identifying the various approaches and the main research characteristics,limitations,and opportunities,and also highlighting the potential of the usage of ML in crash analyses.展开更多
基金Gratefully acknowledge financial support from FCT-Fundação para a Ciência e Tecnologia(Portugal),national funding through research grant(UID/SOC/04521/2013)to the ADVANCE-Advanced Research Centre in Management.
文摘Tourism is one of the activities with high benefits on the development and for many regions,enabling the integration of local populations and economies.In our natural laboratory,the Azores Island of São Miguel,an important share of tourists identifies adventure,leisure and touch with nature,as the main reasons for the visit.The use of footpaths can contribute to the satisfaction of tourists,promoting tourism and the region’s development during their movements on the tourism network,tourists appreciate different types of attractions and need the support of a set of facilities.Tourist decisions are not always done in a rational way,emotions add even more complexity to the human decision process.The movement of tourists within a destination depends on factors related to tourist characteristics,like the time budgets,preferences or destination knowledge,and destination features related to attractions characteristics or accessibility level.The existence of a mathematical model that incorporates the main factors that explain the movement of independent tourists within a destination,in a dynamic way,will make possible the creation of an adaptable software tool.This tool will meet the specific needs of tourists,allowing the use of the network in an optimal way by the different tourist profiles,and the needs of regional government and business,allowing better decisions and the offer of relevant tourism products.This article is based on the authors’previous research and identifies the relevance of tourism for regional development,finds the main tourists’mobility criteria on the study territory,using as main support for the footpath network,recognises the necessary modelling process and develops the foundation for the building of the mathematical model that explains the movement of tourists within the destination,making possible a future adaptable software tool.
基金funded through the programmatic funding-UIDP/04427/2020the research grant UI/BD/153356/2022 awarded by the Fundação para a Ciência e a Tecnologia(FCT)of Portugal to the Research Centre for Territory,Transports and Environment(CITTA).
文摘This paper provides a tool to identify key aspects for an airport to achieve global hub status for a given airline and determines whether these factors are related to the facility’s infrastructure,its region,or both.Despite the frequent use of the term‘hub’,there is little academic consensus on its exact definition in air transport.Many define a hub based on passenger numbers rather than the concentration of flights and passengers from the main carrier.This study addresses this gap by analyzing the factors influencing the definition of a hub and the commonalities among global hubs.Data from 300 major airports,including internal variables(runways,terminals,gates and area)and external variables(economy,population,attractiveness),were collected.A Binary Logistic Regression(BLR)model identified key aspects influencing hub status,with the assistance of an Exploratory Factor Analysis(EFA)that grouped the variables into factors.The binary‘hub’variable was defined by the main carrier’s activity and the Global Airport Connectivity Index(GACI).The factor with the highest coefficient primarily involved internal variables and,to a lesser extent,global attractiveness and population.The factor with the lowest coefficient related to the region economy.The BLR correctly identified hub status in 93.3%of cases,with 68.3%accuracy for hub airports.Airports not correctly identified by the model mostly present a lack or underutilization of existing infrastructure.
基金the Instituto Federal Goiano(IFGoiano)(Goiano Federal Institute)for the financial support it providedsupport from the Coordenagao de Aperfeigoamento de Pessoal de Nivel Superior-Brazil(CAPES)-Financing Code 001(Coordination of Improvement of Higher Education Personnel)the Fundagao para a Ciencia and Tecnologia-Portugal-(FCT)(Science and Technology Foundation)under the project"Mobilidade Urbana SustentaveleSegura"(Safe and Sustainable Urban Mobility)of which this research is a part。
文摘Road safety modeling is a valuable strategy for promoting safe mobility,enabling the development of crash prediction models(CPM)and the investigation of factors contributing to crash occurrence.This modeling has traditionally used statistical techniques despite acknowledging the limitations of this kind of approach(specific assumptions and prior definition of the link functions),which provides an opportunity to explore alternatives such as the use of machine learning(ML)techniques.This study reviews papers that used ML techniques for the development of CPM.A systematic literature review protocol was conducted,that resulted in the analysis of papers and their systematization.Three types of models were identified:crash frequency,crash classification by severity,and crash frequency and severity.The first is a regression problem,the second,a classificatory one and the third can be approached either as a combination of the preceding two or as a regression model for the expected number of crashes by severity levels.The main groups of techniques used for these purposes are nearest neighbor classification,decision trees,evolutionary algorithms,support-vector machine,and artificial neural networks.The last one is used in many kinds of approaches given the ability to deal with both regression and classification problems,and also multivariate response models.This paper also presents the main performance metrics used to evaluate the models and compares the results,showing the clear superiority of the ML-based models over the statistical ones.In addition,it identifies the main explanatory variables used in the models,which shows the predominance of road-environmental aspects as the most important factors contributing to crash occurrence.The review fulfilled its objective,identifying the various approaches and the main research characteristics,limitations,and opportunities,and also highlighting the potential of the usage of ML in crash analyses.
