Search

Personalized travel itinerary recommendation algorithms are the focus of research in smart tourism and tourism GIS. Aiming to address issues present in travel itinerary recommendations for the increasingly popular "self-drive tour" mode, this study proposes an algorithm based on comprehensive tourist satisfaction to mitigate problems such as the neglect of important relevant factors and low degree of personalization. First, we construct a model of tourist satisfaction for travel itineraries by comprehensively considering factors including time utilization, the attractiveness of attractions, itinerary feasibility, and the diversity of attraction types. Unlike previous studies, we consider dining and accommodation time during the itinerary, the physical condition of tourists, and the diversity of attraction types, and establish penalty functions to flexibly constrain deviations from the expected conditions in itinerary planning. Then, with the optimization of comprehensive tourist satisfaction as the objective, we design a new algorithm to address the itinerary recommendation problem, supporting tourists in selecting must-visit attractions, restaurants, and hotels, as well as personalized preferences such as the sightseeing sequence. The experimental results demonstrate that our proposed algorithm outperforms two baseline algorithms, providing higher comprehensive tourist satisfaction while also exhibiting greater feasibility in itinerary planning. The proposed algorithm effectively addresses the issue of personalized travel itinerary recommendation, presenting an efficient, feasible, and practical solution. [ABSTRACT FROM AUTHOR]

In this modern hi-tech world, it has become a passion and trend to plan for a tour. It would be very helpful for the tourists if there exists a travel assistance app which can assist them to improve and complete their travel experience. Tour-Easy is an ML-Integrated mobile app which aims to give interested results for trip planning, enabling people to enjoy the trip. It is developed using React Native integrated with Google APIs to give a flawless and intuitive platform to plan and organize their journeys. With our app, one can have a smart plan which can save time and extend their travels according to the requirements of destination and time constraints. Through its service APIs, Tour Easy recommends the necessary outputs based on the user's choice. Also, the app uses APIs to recommend nearby hotels. Tour Easy is devoted to furnish a great client experience. It is an user-friendly platform with a well-designed interface which allow users to fluently plan their journey, and discover new places. [ABSTRACT FROM AUTHOR]

The field of immune based therapeutics for treatment of common chronic illnesses has exploded leading to significant improvement in the disease processes but a downstream impact on infections. Janus kinase (JAK) inhibitors and Bruton Tyrosine kinase (BTK) inhibitors are becoming more widely used in treatment of chronic inflammatory diseases and chronic leukemias because of simplicity of oral administration. As such, many patients will have improvement in their underlying illnesses and will have greater flexibility to travel. Thus, it is important for travel medicine practitioners to be aware of these agents, their adverse effects and impact on drug interactions and immunizations. [ABSTRACT FROM AUTHOR]

Personalized travel itinerary recommendation algorithms are the focus of research in smart tourism and tourism GIS. Aiming to address issues present in travel itinerary recommendations for the increasingly popular “self-drive tour” mode, this study proposes an algorithm based on comprehensive tourist satisfaction to mitigate problems such as the neglect of important relevant factors and low degree of personalization. First, we construct a model of tourist satisfaction for travel itineraries by comprehensively considering factors including time utilization, the attractiveness of attractions, itinerary feasibility, and the diversity of attraction types. Unlike previous studies, we consider dining and accommodation time during the itinerary, the physical condition of tourists, and the diversity of attraction types, and establish penalty functions to flexibly constrain deviations from the expected conditions in itinerary planning. Then, with the optimization of comprehensive tourist satisfaction as the objective, we design a new algorithm to address the itinerary recommendation problem, supporting tourists in selecting must-visit attractions, restaurants, and hotels, as well as personalized preferences such as the sightseeing sequence. The experimental results demonstrate that our proposed algorithm outperforms two baseline algorithms, providing higher comprehensive tourist satisfaction while also exhibiting greater feasibility in itinerary planning. The proposed algorithm effectively addresses the issue of personalized travel itinerary recommendation, presenting an efficient, feasible, and practical solution.

This research introduces an advanced Trip Planning Management System aimed at revolutionizing the conventional approach of manual travel itinerary creation. The system integrates four pivotal components, each catering to a distinct aspect of travel planning automation. The Location Planning module takes the guesswork out of destination selection by leveraging traveler preferences to suggest optimal categories. Complementing this, the Route planning module employs real-time data and algorithms to automatically determine the most efficient routes between recommended destinations, optimizing travel time. Simultaneously, the Hotel Planning component eliminates the painstaking process of sifting through accommodation options by offering tailored suggestions based on a variety of factors, including budget and location. Lastly, the Extra Activities Planning module adds a layer of enrichment to the travel experience by algorithmically recommending leisure activities that resonate with traveler interests. This innovative system redefines travel planning, making it more convenient, personalized, and enjoyable through strategic automation. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (drones) have gained a high level of practical confidence due to their ease of use in different applications as well as their advantages such as flexibility, ease of operation and low cost. In this paper, we propose the use of drones equipped with onboard sensors for monitoring the status of electrical distribution networks. Specifically, we formulate a multi-objective optimization approach for distribution networks’ situational awareness, which aims to minimize two conflicting objectives. These are the total annual cost and loss of observability. The proposed approach decides on the optimal number of drones, their batteries, the nodes to be visited, and the trip plan. In order to obtain a set of Pareto solutions, we utilize the non-dominated sorting genetic algorithm (NDSGA) in conjunction with branch-and-bound algorithm to minimize both objectives. To validate the performance of the proposed approach, we applied it to a practical distribution system under different scenarios of monitoring frequency and working hours. The obtained results prove the effectiveness of the proposed approach.

With the considerable growth in the information and communication technology (ICT), several smartphone-based mobility platforms have already sprung up and they have the potential of transforming the mobility ecosystem completely. However, there is close to no knowledge available about how ICT-based smartphone apps meant for day-to-day trip planning tasks are being used across various user groups and how they influence travel outcomes, especially in Indian cities. Therefore, this study is an effort to close this gap by gathering data from the city of Bhopal and carrying out an exploratory statistical analysis on the usage of smartphone apps for different types of trip planning purposes, as well as their influence on travel outcomes. The study provides empirical evidence of relationships between smartphone app usage for trip planning (such as departure time, destination selection, mode selection, route selection, communicating and coordinating trips, and performing tasks online rather than visiting) and the resulting travel outcomes, such as kilometres travelled by vehicle (for purposes, such as work/education, shopping, and recreation), social gatherings, new destinations, and group trips. The chi-square test has been used to test and interpret several socioeconomic variables that could influence this relationship, such as gender, age, income, etc. The study's findings provide important behavioural insights that may be useful in policy discussions. [ABSTRACT FROM AUTHOR]

Continuous research and development of novel tourism routes is necessary for tourism service providers to improve the tourist experience and industrial competitiveness. However, the route planning is cumbersome due to the time-consuming, extensive, and costly field study. Most of the existing route planning studies focus on recommending tourism routes for users based on attraction characteristics or tourist behavior features, which are generally unexplainable due to the black-box approaches they use. Other solutions allow users to customize itineraries through an interactive interface but often lack guidance from the aspect of route evaluation or destination image perception. In this paper, we thoroughly discuss the requirements and design tasks with domain experts and propose TriPlan, an interactive visual analytics system that provides intuitive planning guidance for tourism product developers. We design and improve multiple coordinated visualizations to facilitate analysis from the perspectives of overall route pattern and individual destination image. We also develop a hierarchical planning view to display the structural information of a plan. In addition, we introduce an automatic route optimization algorithm and multiple interactions to assist users in optimizing and adjusting the itineraries. Finally, we evaluate the usability and effectiveness of our system through three case studies and quantitative and qualitative interviews with the domain experts on real-world datasets. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things (IoT) devices has recently received significant attention. In this paper, we investigate the data collection process from a set of smart meters in advanced metering infrastructure (AMI) enabled by UAVs. The objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing, the charging locations as well as the data collection trip planning. This is achieved while considering the energy budgets of batteries of UAVs and the required amount of collected data. The problem is formulated as a mixed-integer nonlinear programming (MIN-LP), which is decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms (GAs), and then the optimum trip path is found using a traveling salesman problem (TSP) branch and bound algorithm. Simulation results show that the battery capacity or the number of UAVs increases as the coverage area or the density increases.

With the development of the Internet, technology, and means of communication, the production of tourist data has multiplied at all levels (hotels, restaurants, transport, heritage, tourist events, activities, etc.), especially with the development of Online Travel Agency (OTA). However, the list of possibilities offered to tourists by these Web search engines (or even specialized tourist sites) can be overwhelming and relevant results are usually drowned in informational "noise", which prevents, or at least slows down the selection process. To assist tourists in trip planning and help them to find the information they are looking for, many recommender systems have been developed. In this article, we present an overview of the various recommendation approaches used in the field of tourism. From this study, an architecture and a conceptual framework for tourism recommender system are proposed, based on a hybrid recommendation approach. The proposed system goes beyond the recommendation of a list of tourist attractions, tailored to tourist preferences. It can be seen as a trip planner that designs a detailed program, including heterogeneous tourism resources, for a specific visit duration. The ultimate goal is to develop a recommender system based on big data technologies, artificial intelligence, and operational research to promote tourism in Morocco, specifically in the Daraâ-Tafilalet region.

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency. [ABSTRACT FROM AUTHOR]

Focusing on four types of user-generated content (UGC) on Instagram for travel planning, this study examined the impacts of UGC format, distribution channel, and engagement on travelers' feelings of social presence, trustworthiness, and perceived enjoyment. These factors were then used to test an extended Technology Acceptance Model (TAM) on trip planning. Results showed response-to-marketer-action UGC hosted on official Instagram channel was an effective way to increase perceived enjoyment of travel-related UGC in a low engagement situation. Social presence, perceived trustworthiness, and perceived enjoyment were included as three external variables in an extended TAM for UGC-based travel planning. Compared to previous TAM studies, perceived ease of use and enjoyment, instead of perceived usefulness, were strong predictors of attitude and intention for UGC-based travel planning. Seeking fun with less effort is a main reason to process UGC information on Instagram for travel planning. [ABSTRACT FROM AUTHOR]

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists' intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders. [ABSTRACT FROM AUTHOR]

With the considerable growth in the information and communication technology (ICT), several smartphone-based mobility platforms have already sprung up and they have the potential of transforming the mobility ecosystem completely. However, there is close to no knowledge available about how ICT-based smartphone apps meant for day-to-day trip planning tasks are being used across various user groups and how they influence travel outcomes, especially in Indian cities. Therefore, this study is an effort to close this gap by gathering data from the city of Bhopal and carrying out an exploratory statistical analysis on the usage of smartphone apps for different types of trip planning purposes, as well as their influence on travel outcomes. The study provides empirical evidence of relationships between smartphone app usage for trip planning (such as departure time, destination selection, mode selection, route selection, communicating and coordinating trips, and performing tasks online rather than visiting) and the resulting travel outcomes, such as kilometres travelled by vehicle (for purposes, such as work/education, shopping, and recreation), social gatherings, new destinations, and group trips. The chi-square test has been used to test and interpret several socioeconomic variables that could influence this relationship, such as gender, age, income, etc. The study’s findings provide important behavioural insights that may be useful in policy discussions.

Geleneksel çizgelerin sunumu ve kullanımı, toplu taşımada etkin ve hızlı bir yönlendirme çözümü için oldukça önemlidir. Ancak, geleneksel çizge yaklaşımı, ön ve/veya son işlem olmaksızın yolcuların toplam seyahat süresi, asgari transfer sayısı, toplam seyahat mesafesi gibi isteklerini dikkate alamamaktadır. Dahası, geleneksel çizgelerde araçlar temsil edilememektedir. Bu çalışmada, farklı çizge türleri incelendikten sonra, toplu taşıma çizgesi olarak isimlendirilen yeni bir çizge önerilmiştir. Önerilen çizge etkin bir seyahat planlaması için toplu taşıma sistemini modellemekte ve mesafe, bekleme süresi, seyahat süresi, kendi kendine ulaşım ile transfer sayısını aynı anda göz önünde bulundurabilmektedir. Bu sayede ön ve son işlem olmaksızın yolcu istekleri karşılanabilmektedir. Ayrıca, önerilen çizgede araçlar da göz önünde bulundurulmuş ve gösterilmiştir.

The presentation and usage of traditional graphs is very important for effective and fast solution of routing in public transportation. However, the traditional graph approach is unable to consider the passenger requests such as total travel time, minimum number of transfer and total distance of travel without pre-processing and/or post-processing. Moreover, the vehicles are not represented on traditional graph. In this paper, after analyzing the different kind of graphs, we propose a novel graph named as public transportation graph. The proposed graph models the public transportation system and considers distance, waiting time, travel time, self-transportation and number of transfers simultaneously for efficient trip planning. In this way, passenger requests can be met without pre-processing and post-processing. In addition, the vehicles are also considered and demonstrated in the proposed graph.

Background: Tour recommendation and path planning are the most challenging jobs for tourists as they decide Points of Interest (POI). Objective: To reduce the physical effort of the tourists and recommend them a personalized tour is the main objective of this paper. Most of the time people had to find the places he wants to visit in a difficult way. It kills a lot of time. Methods: To cope with this situation we have used different methodology. First, a greedy algorithm is used for filtering the POIs and BFS (Breadth First Search) algorithm will find POI in terms of user interest. The maximum number of visited POI within a limited time will be considered. Then, the Dijkstra algorithm finds the shortest path from the point of departure to the end of tours. Results: This work shows its users list of places according to the user's interest in a particular city. It also suggests them places to visit in a range from the location of the user where a user can dynamically change this range and it also suggests nearby places they may want to visit. Conclusion: This tour recommendation system provides its users with a better trip planning and thus makes their holidays enjoyable.

Social media plays an increasingly important role in travel information seeking and decision-making. However, there is limited understanding of how a group of tourists use social media to plan trips collaboratively and the different practices between countries. In this study, we investigated the collaborative information seeking (CIS) and sharing behaviours of mobile social media users from Australia, Bangladesh and China. Specifically, we surveyed a total of 219 participants to explore the differences in CIS behaviours when people were planning a group trip. The findings suggest significant differences among three countries in terms of the motivations of using social media, CIS activities and social interactions outside the group. Key findings include Bangladeshi and Chinese travellers preferred known contacts on social media, while Australian tourists intended to use both known contacts and user-generated contents for seeking information. The findings also show that social interactions employed by individuals are considered as an important complement of and are interwoven with in-group CIS; both contribute to tourism information seeking. Finally, we propose a framework for CIS research in the tourism domain. [ABSTRACT FROM AUTHOR]

The hostel market urgently needs to reinvent itself and define strategies to increase its revenue, providing safety to its guests during the COVID‐19 pandemic. In the trip's planning, the results show that individuals attribute high levels of importance to cuisine, local wines and to meeting new people. The social networks of the different places to visit are the least utilized means to plan what to do during their stay in Oporto. Static street advertising and leaving the hostel to go outside are much more important to hostel customers. The most notable attractions are the Ribeira and the bridges over the river Douro. [ABSTRACT FROM AUTHOR]

This study examined tourists' smartphone use patterns for tourism information searches. We explored associations between respondents' demographic and socioeconomic characteristics and smartphone use for their most recent trip. Then, we compared differences in tourism information categories searched via smartphone before and during trips, respectively. Using data collected from tourists who visited coastal destinations in South Carolina, USA, a series of chi-square tests were conducted. Findings reveal that respondents' employment status and age were significantly associated with smartphone use during their most recent trip. Furthermore, results show significant differences in tourism information categories searched via smartphone before and during trips, respectively. Based on these results, theoretical and practical implications are provided to better understand tourists' information search behaviour via smartphones. [ABSTRACT FROM AUTHOR]

The main aim of this study was to examine the influence of social media sites on trip decision making process as well as to investigate the most used social media sites during trip planning through a review of literature. This study was based on an extensive review of literature from a range of peer reviewed papers on social media and tourist destination planning. A number of sources were accessed such as Science Direct, Google Scholar, Emerald Insights Journals, Institutional Repository, Scopus, JSTOR, Taylor & Francis, SAePublications, Sabinet, and ProQuest, while articles from tourism organisations such as the World Tourism Organisation were also consulted. The sources were thus used as part of data collection procedure and were critically examined for information related to social media and travel decision-making processes. The objectives of the study were achieved through literature findings which demonstrated that social media sites are used at each stage of trip planning but are predominantly used at the information search stage and after trip for experience sharing. The review of literature also shows that social media sites are now being considered as the most reliable sources of information following recommendations from family and friends. It is critical for tourism business owners to be present in social media sites to connect with prospective customers. They also need to understand the types of social media sites frequently used by tourists in different regions and to ensure that content regarding their attractions and services is always accurately posted on those sites.

Noting increasingly independent traveling by tourists, as well as changes on the tourist activity worldwide due to the 2008 economic crisis, this study analyzes the relationships between an economic crisis, tourist's self-organization in trip planning, and tourist's trip satisfaction. Data from a panel (2006–2011) on how residents in Spain complete their accommodation and transport bookings are used, including data on their trip satisfaction. The study identifies a negative relationship between the 2008 economic crisis and trip satisfaction, a positive relationship between self-organization and satisfaction, as well as positive moderating effects of the crisis on the relationship between self-organization and satisfaction. [ABSTRACT FROM AUTHOR]

Planning a personalized itinerary for an unfamiliar region requires much effort to design desirable travel plans. With the rapid development of location-based social network (LBSN) services, data mining techniques are utilized to retrieve useful information such as geographical features and social relationships. In this paper, we propose a personalized itinerary recommendation with time constraints (pirT) framework for the LBSN by exploiting geographical features and social relationships to recommend a personalized itinerary that satisfies user preferences (i.e., travel behaviors). In pirT, we have designed a user-based collaborative filtering with time preference (UTP) to explore user preferences by considering the visiting time of locations which the users have visited in our framework. UTP allows a tourist to find users with similar travel behaviors to those of the service requester in the past and to recommend interesting locations in the itineraries that these similar users have traveled to before. Subsequently, given a beginning location and a destination with a time constraint specified by the tourist, we devise the top-k A ∗ search-based recommendations and re-ranking itinerary candidate algorithms to efficiently plan the top k personalized itineraries. In the planning process, we simultaneously take account of the visiting time of locations, the transit time between locations, and the order of visiting locations. We conducted our experiments on the Gowalla dataset and demonstrated the effectiveness of our pirT framework comparing it with the personalized trip recommendation (PTR) framework. The results show that our pirT framework is superior to the PTR framework. [ABSTRACT FROM AUTHOR]

The presentation and usage of traditional graphs is very important for effective and fast solution of routing in public transportation. However, the traditional graph approach is unable to consider the passenger requests such as total travel time, minimum number of transfer and total distance of travel without pre-processing and/or post-processing. Moreover, the vehicles are not represented on traditional graph. In this paper, after analyzing the different kind of graphs, we propose a novel graph named as public transportation graph. The proposed graph models the public transportation system and considers distance, waiting time, travel time, self-transportation and number of transfers simultaneously for efficient trip planning. In this way, passenger requests can be met without pre-processing and post-processing. In addition, the vehicles are also considered and demonstrated in the proposed graph. [ABSTRACT FROM AUTHOR]

This paper explores the use of smartphone applications for trip planning and travel outcomes using data derived from a survey conducted in Halifax, Nova Scotia, in 2015. The study provides empirical evidence of relationships of smartphone use for trip planning (e.g. departure time, destination, mode choice, coordinating trips and performing tasks online) and resulting travel outcomes (e.g. vehicle kilometers traveled, social gathering, new place visits, and group trips) and associated factors. Several sets of factors such as socio-economic characteristics and travel characteristics are tested and interpreted. Results suggest that smartphone applications mostly influence younger individuals' trip planning decisions. Transit pass owners are the frequent users of smartphone applications for trip planning. Findings suggest that transit pass owners commonly use smartphone applications for deciding departure times and mode choices. The study also identifies the limited impact of smartphone application use on reducing travel outcomes, such as vehicle kilometers traveled. The highest impact is in visiting new places (a 48.8% increase). The study essentially offers an original in-depth understanding of how smartphone applications are affecting everyday travel. [ABSTRACT FROM AUTHOR]

Abstract Bike-Sharing System (BSS) is a convenient service deployed in many big cities to alleviate the last-mile problem. However, the explosion of users in BSS causes the inconvenience of bike utilization. Much efforts have been devoted to performing resources prediction, redistribution to help with BSS operation. But there are only a few works focus on trip planning, and none of them notice the complete bike trip in BSS composes of three segments: from user's start point to a start station, from the start station to a target station, and from the target station to user's terminal point. To study the case, this paper addresses a static trip planning problem by considering the bike utilization conflict in BSS so as to maximize the number of the served users and minimize their trip time. The problem is formulated as the weighted k -set packing problem. We then design a Greedy Trip Planning algorithm (GTP) and a Humble Trip Planning algorithm (HTP) to solve it. For comparison, we design a Random Trip Planning algorithm (RTP) as a benchmark. Extensive simulation results show that GTP and HTP outperform RTP, and reveal the impact of different factors on the performance of our algorithms. [ABSTRACT FROM AUTHOR]