Search Results (8)

Balancing tourism seasonality remains a significant challenge in the management of tourist attractions. Despite existing research on the impact of seasonality from the perspectives of tourist intention cognition and spatial theory, gaps still exist in the relevant literature. This study examines 16 5A-level scenic spots in China with peak season, flat season, and off-season themes, utilizing 8385 tourist reviews from Ctrip.com as data. The LDA topic model is employed to analyze tourism destination image (TDI) under seasonality of destination, and the spatial level (SLs) model is combined to analyze the spatial hierarchy of these images. The findings reveal an association between TDI and SLs under seasonality of destination. For instance, peak season TDI themes (e.g., ‘viewing the scenery’) exhibit a support level of 0.789, while off-season themes (e.g., ‘relaxed itinerary’) reach 0.682, reflecting tourists’ prioritization of functional versus psychological dimensions across seasons. The proposed TDI-SLs correlation theory bridges supply-side spatial resource allocation with tourists’ perceptual dynamics, offering a novel framework to rebalance seasonal demand–supply gaps through strategic spatial planning and image recalibration. Practically, this framework guides destination managers to design season-specific strategies, such as optimizing crowd management in peak seasons or promoting immersive experiences in off-seasons. Full article

In the dynamic and evolving tourism industry, engaging with stakeholders is essential for fostering innovation and improving service quality. However, tourism companies often struggle to meet expectations for customer satisfaction through interactivity and real-time feedback. While new digital technologies can address the challenge of providing personalized travel experiences, they can also increase the workload for travel agencies due to the maintenance and updates required to keep travel details current. Intelligent chatbots and other generative artificial intelligence (GAI) tools can help mitigate these obstacles by transforming tourism and travel-related services, offering interactive guidance for both tourism companies and travelers. In this study, we explore and compare the main characteristics of existing responsive AI instruments applicable in tourism and hospitality scenarios. Then, we propose a new theoretical framework for decision making in the tourism industry, integrating GAI technologies to enable agencies to create and manage itineraries, and tourists to interact online with these innovative instruments. The advantages of the proposed framework are as follows: (1) providing a comprehensive understanding of the transformative potential of new generation AI tools in tourism and facilitating their effective implementation; (2) offering a holistic methodology to enhance the tourist experience; (3) unifying the applications of contemporary AI instruments in tourism activities and paving the way for their further development. The study contributes to the expanding literature on tourism modernization and offers recommendations for industry practitioners, consumers, and local, regional, and national tourism bodies to adopt a more user-centric approach to enhancing travel services. Full article

Tourism has become one of the world’s largest service industries. Due to the rapid development of social media, more people like self-guided tours than package itineraries planned by travel agencies. Therefore, how to develop itinerary recommendation systems that can provide practical tour suggestions for tourists has become an important research topic. This study proposes a novel tour recommendation system that considers the implicit and dynamic information of Point-of-Interest (POI). Our approach is based on users’ photo information uploaded to social media in various tourist attractions. For each check-in record, we will find the POI closest to the user’s check-in Global Positioning System (GPS) location and consider the POI as the one they want to visit. Instead of using explicit information such as categories to represent POIs, this research uses the implicit feature extracted from the textual descriptions of POIs. Textual description for a POI contains rich and potential information describing the POI’s type, facilities, or activities, which makes it more suitable to represent a POI. In addition, this study considers visiting sequences when evaluating user similarity during clustering so that tourists in each sub-group hold higher behavior similarity. Next, the Non-negative Matrix Factorization (NMF) dynamically derives the staying time for different users, time slots, and POIs. Finally, a personalized itinerary algorithm is developed that considers user preference and dynamic staying time. The system will recommend the itinerary with the highest score and the longest remaining time. A set of experiments indicates that the proposed recommendation system outperforms state-of-the-art next POI recommendation methods regarding four commonly used evaluation metrics. Full article

Background: Road transport companies utilize transport capacities as fixed compositions of tractors and semi-trailers, while the possibility of exchanging semi-trailers is considered ad hoc, after some unforeseen circumstances emerge on the route. Such an approach is a limiting factor in achieving optimal utilization of transport capacities, and consequently affects profitability. We proposed a new concept of vehicle fleet management where semi-trailers would not be permanently assigned to the tractors, but could be dynamically reassigned, to utilize optimally the vehicle fleet, on considering the planned itinerary, the driver’s working hours, and traffic conditions. Methods: We set the key performance indicators and developed a prototype application based on the concept of dynamic semi-trailer allocation. We simulated the use of the prototype application on the historical data collected in the case study on one of the leading transport companies in the cold chain, and evaluated the benefits that may be achieved thereof. Results: Simulation showed that implementing the proposed concept enabled the reduction of the vehicle workdays spent on-the-route, the reduction of the number of vehicle compositions needed to handle the transport demand, and improved the quality of service. Conclusions: The proposed concept is beneficial for transport companies that operate large vehicle fleets on long-haul routes, with several transport orders per route. The prototype application may be the basis for developing a fully functional application that can be integrated into the fleet management system. Full article

The integration of landscape-based approaches into regional and town planning policies is one of the main objectives of the European Landscape Convention. In the twenty-first century, the traditional discipline of city spatial-planning has gradually been incorporating two types of tactics linked to a landscape-based approach: nature-based strategies, which focus on sustainable goals; and people-based strategies, which integrate a social dimension into decision-making processes. A backbone of landscape-based spatial planning challenge consists of reshaping consolidated urban areas to improve quality of life, encouraging people’s physical activity, and supporting healthier urban lifestyles. This study assumes that physical activity is further encouraged by itineraries that incorporate both landscape features—i.e., natural assets and sense of place—and functional diversity associated with urban activities—i.e., public facilities. A methodology was elaborated to define a preliminary landscape-based spatial planning approach, centering on the analysis of walking-related activity in urban and peri-urban areas. For this purpose, geolocated digital traces are intertwined: official city routes, urban facility locations, users’ Wikiloc trails, and Google Places API data. Once applied to selected medium-sized European cities in the Mediterranean area, these data sources lead to the identification of intangible values and dynamics in places where landscape-based spatial planning solutions could be enhanced. As a result, the present work shows the suitability of interrelating these geolocated data sources, permitting to identify landscape features as key components of spatial planning, which permit balancing individual goals, the aims of local communities, and administrative functions. Full article

Sites of geo-cultural interest are often included in areas where multicultural contexts (geo and non geo) are present. Cultural heritage dissemination is sometimes mono-contextual, paying little attention to the possibility of inclusion in a wider multicultural context. When these different contexts are linkable to each other following a specific theme, multicultural heritage dissemination will be possible, and often the geo context can represent a fulcrum, a resilient tool in doing that. A portion of the Sinni river’s catchment area (Basilicata region, Southern Italy) has been chosen to test and verify the multi-level/disciplinary approach applicability. The area is located on the southeastern edge of the Pliocene to Pleistocene Sant’Arcangelo basin in the Southern Apennines chain of Italy. Here, both basic observations on the physical geography landscape evolution and specialized observations on river dynamics and on the hydrographic network have been carried out. Educational routes will be proposed with different educational levels along a path that will include the San Giorgio Lucano hypogea. This paper represents the results of a qualitative study providing an overview of the possibility, in a multicultural context, about whether, when, and how the geo context may act as a link between the different disciplines and what is the best way to make it. A relational database, organized in contexts, areas, and themes, is planned at different levels of detail, and is currently being developed in order to make final products easily available. Each level will be provided with basic concepts, territorial contextualization, and of activities/itineraries. The goal is to provide a versatile tool that enhances the territorial multi-cultural heritage to reach a greater number of end users interested in both geo and non geo contexts. Full article

In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with “Directed Acyclic Graph” (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time). Full article

In the context of smart cities, ridesharing in urban areas is gaining researchers’ interest and is considered to be a sustainable transportation solution. In this paper, we present SRide (Shared Ride), a multi-hop ridesharing system as a mode of sustainable transportation. Multi-hop ridesharing is a type of ridesharing in which a rider travels in multiple hops to reach a destination, transferring from one driver to another between hops. The key problem in multi-hop ridesharing is to find an optimal itinerary or route plan for a rider from an origin to a destination in a dynamic, online setting. SRide adopts a novel approach to finding itineraries for riders suited to the online nature of the problem. The system represents ride offers as a time-dependent directed graph and finds itineraries dynamically by updating the graph incrementally and decrementally as ride offers are updated in the system. The system’s distinguishing feature is its incremental and decremental operation, which is enabled by employing dynamic single-source shortest-path algorithms. We conducted two extensive simulation studies to evaluate its performance. Metrics, including the matching rate, savings in total system-wide vehicle-miles, and total system-wide driving times were measured. In the first study, SRide’s dynamic update algorithms were compared with their non-dynamic versions. Results show that SRide’s algorithms run up to thirteen times faster than their non-dynamic versions. In the second study, we used data from the travel demand model for metropolitan Atlanta in the US state of Georgia, to assess the benefits of multi-hop ridesharing. Results show that matching rates increase up to 68%, saving in total system-wide vehicle-miles of up to 12%, and reduction in the total system-wide driving time of up to 12.86% is achieved. Full article