This study explores the early travelers to Petra, Jordan, during the 20th century. To gain insights into the evolution of early travel experiences to Petra during this specific period, the researchers utilized narrative analysis and conducted in-depth interviews with 14 elderly inhabitants of Wadi Musa who resided in the area at that time. These interviews provided valuable information and served as a basis for visually representing the primary routes that emerged from the participants’ narratives. This study delves into the accessibility of early travelers to Petra in the 20th century by creating a comprehensive map that outlines the trails, byways, and roads used by these travelers to reach Petra. The study’s findings also revolve around the identified stages derived from the data gathered through these interviews.
Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.
China is currently at a critical juncture in implementing the rural revitalization strategy, with urbanization and tourism development as crucial components. This study investigates 41 counties (cities) in the Wuling Mountain area of central China, constructing an evaluation system for the coordinated development of these two sectors. The coupling coordination degree is calculated using a combination weighting method and the coupling coordination degree model. Spatio-temporal evolution characteristics are analyzed through spatial autocorrelation, while the geographic detector explores the driving factors of spatial variation. The findings reveal a significant increase in coupling coordination between urbanization and tourism, transitioning towards a coordinated phase. Spatially, urbanization and tourism exhibit positive correlations, with high-value clusters in the southeast and northwest and low-value clusters in the south. The geographical detector identifies industrial factors as the most critical drivers of spatial variation. This study offers novel insights into the dynamics of urbanization and tourism, contributing to the broader literature by providing practical implications for regional planning and sustainable development. The results are relevant to the Wuling Mountain area and serve as a reference for similar regions globally. However, the study has certain limitations, such as regional specificity and data availability, which should be considered in the context of this research.
Efficient access to tourist spots is necessary for enhancing the overall travel experience, especially in urban environments. This study investigates the accessibility of key tourist spots in Budapest through different transportation modes (e.g., walking, cycling, and public transport) across various time intervals. Using spatial-temporal travel time maps and detailed statistical analysis, the research highlighted significant differences in how these modes connect tourists to their attractions. Cycling stands out as the most efficient transportation option, providing rapid access to a wide range of tourist spots, while public transport ranks second. However, the study also reveals disparities in accessibility, with central areas being well-served, while outer ones, especially in the northwest, remain less accessible. These findings highlight the need for targeted transportation improvements to ensure that all areas of the city are equally reachable. The results offer valuable insights for urban planners and policymakers aiming to enhance tourism infrastructure and improve the visitor experience in Budapest.
