Making Travel Plans Research Articles

A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction

Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for traffic flow prediction for making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Moreover, sensors to catch the information about traffic flow will be interfered with by environmental factors such as illumination, collection time, occlusion, etc. Therefore, the traffic flow in the practical transportation system is complicated, uncertain, and challenging to predict accurately. Motivated from the aforementioned issues and challenges, in this paper, we propose a deep encoder-decoder prediction framework based on variational Bayesian inference. A Bayesian neural network is designed by combining variational inference with Gated Recurrent Units (GRU) which is used as the deep neural network unit of the encoder-decoder framework to mine the intrinsic dynamics of traffic flow. Then, the variational inference is introduced into the multi-head attention mechanism to avoid noise-induced deterioration of prediction accuracy. The proposed model achieves superior prediction performance on the Guangzhou urban traffic flow dataset over the benchmarks, particularly when the long-term prediction.

Education Research: Virtual Residency Interviews and the Second Look: The Mayo Clinic Neurology Tri-Site Experience.

The 2023 Match cycle was the third virtual interview season for adult neurology residency programs. This recruitment cycle differed from years prior because an in-person second-look event was offered by some programs to complement the virtual interview day. We sought feedback from those who interviewed for adult neurology residency positions at Mayo Clinic in Arizona, Florida, and Minnesota to gain applicants' perspectives on such events and identify best practices to improve the interview experience for future applicants. Virtual interviews were conducted for adult neurology residency positions at Mayo Clinic in Arizona, Florida, and Minnesota for the 2023 Match. Each site hosted optional, in-person, no-stakes second-look events after program rank lists were finalized. After Match Day, interviewees at all 3 sites were invited to complete an anonymous electronic survey using Qualtrics software to gather feedback on the in-person events. Of the 240 interviewees for adult neurology residency positions at Mayo Clinic, 52 candidates participated in the in-person second-look event on the Arizona, Florida, or Minnesota campus in 2023. One-third of applicants (80/240) completed the anonymous survey, and of them, 34% (27/80) of respondents had attended a Mayo Clinic second-look event. The desire to step foot on campus, meet the residents in person, and visit the geographic area were the most important reasons driving attendance. Those who did not participate cited financial burden, lack of time, and insufficient notice to make travel plans as the most common factors influencing their decision not to attend. Nearly half of attendees who responded to the survey (10/21, 48%) changed their rank order list after the in-person event. Most of them who participated in a Mayo Clinic second-look event (19/21, 91%) would encourage future applicants to partake. While there are many advantages to virtual residency interviews, most applicants who participated in second-look events found that having an opportunity to visit the program in person was valuable when determining their rank list. Offering an optional, no-stakes, in-person visit allows interviewees to make more informed rank lists. Second-look events have the potential to complement the virtual interview day and may ultimately lead to greater satisfaction for matched applicants.

Do Internet Search Data Help Forecast Air Passenger Demand? Evidence From China's Airports.

Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users’ psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management.

Extracting Searching as Learning Tasks Based on IBRT Approach

With the rapid development of the World Wide Web and information retrieval technology, learning supported by searching engines (such as making travel plans) has boomed over the past years. With the help of search engines, learners can easily retrieve and find large amounts of information on the web. Recent research in the searching as learning (SAL) area has associated web searching with learning. In SAL processes, web learners recursively plan tasks, formulate search queries, obtain information from web pages, and change knowledge structures, to gradually complete their learning goals. To improve the experiences of web learners, it is important to accurately present and extract tasks. Using learning styles and similarity metrics, we first proposed an IBRT model to implement structured representations of the SAL process for each learner. SAL tasks were then extracted from the structures of IBRT. In this study, a series of experiments were carried out against assignment datasets from the Northeastern University (China) UWP Programming Course. Comparison results show that the proposed method can significantly improve the performance of SAL task extraction.

Influence of Rural Development of River Tourism Resources on Physical and Mental Health and Consumption Willingness in the Context of COVID-19

This study took the Three Gorges Dam as an example and discussed the influence of river regulation decisions on the sustainable development of surrounding villages. The study used mixed research methods, snowball sampling, and convenience sampling to obtain samples. The questionnaire samples were analyzed by basic statistical tests, t-test, and structural equation modeling (SEM). The respondents’ opinions were collected through semi-structured interviews and finally the results were discussed by multivariate analysis. The findings were that even though the villages were not well developed in terms of economy, environment, and natural ecology, as long as the community security could be stable, the living could be safe and convenient, people’s daily life patterns and leisure behaviors could be maintained, and people could stabilize their minds and emotions and maintain physical and mental health in order to meet their living needs and reduce the burden. There would be time and funds to invest in leisure, tourism activities, and consumption behavior. If the above consumption patterns are continued, people will gain positive perceptions, stimulating people’s willingness to invest in property purchases or to make travel plans again.

A Novel Hotel Selection Decision Support Model Based on the Online Reviews from Opinion Leaders by Best Worst Method

Hotel selection is an important decision in making travel plans. Since hotel selection is a typical non-expert decision, online reviews provide people with information about the hotels and travel destinations they never went to. Several studies construct the decision model based on online reviews with the subjective weights of criteria but ignore the objective weight of criteria derived by opinion leaders, which contributes to the review helpfulness. This study proposes a decision-making model based on online reviews for satisfactory hotel selection. Firstly, an RFMP model is proposed to extract the online reviews of opinion leaders, and the Word2vec method is used to extract the criteria from the online review of opinion leaders. Secondly, obtain the objective weight of criteria from the online reviews of opinion leaders by Word2Vec. Meanwhile, obtain the subjective weight of criteria by the best worst method(BWM) method. Thus, the weight of the hotel selection criteria can be obtained by a linear weighting of objective and subjective weight with a parameter. Thirdly, the hotel selection process based on TOPSIS is employed. Finally, a case study of 8 alternative hotels on Mafengwo.com is applied to verify the proposed model. Comparison experiments and sensitivity analysis are given to illustrate the reasonableness and advantage of the proposed model.

Between fearful homebodies and carefree travel lovers: identifying tourist segments during the Covid-19 pandemic in Finland, Germany, and Norway

ABSTRACT While an increase in risk sensitivity for travel in times of crisis has been widely studied, attention to changes in the perception of travel motives as a possible counterpart have fallen short. Hence, the research submitted here explores the interplay of negative and positive psychological reactions towards travel during the Covid-19 pandemic, taking three Northern European countries as an example. Data was collected via online consumer panels in Finland, Germany, and Norway (n = 4,649) during the first lockdown in May 2020. We identify tourist segments based on their perceptions of travel risk and the importance of travel for their own well-being. K-means cluster analysis reveals five different pandemic-related clusters that exemplify individual travel plans for the near future, their personal affectedness and sociodemographic aspects, such as nationalities. Results show that a high level of perceived importance of travel for well-being can offset a simultaneous high perception of travel risk: ‘Fearful travel lovers’ make travel plans despite the pandemic. Moreover, we find significant differences regarding the size of the five segments in the three countries. Findings provide important strategic implications for tourism managers seeking how to address relevant tourist groups in times of pandemics.

Addressing Common Questions About Traveling by Air After Orthopedic Surgery.

Many patients have questions about traveling by air after orthopedic surgery. The goal of this review was to provide a guide to addressing these issues to better prepare patients for air travel. A comprehensive literature review was conducted to address patient questions regarding metal detectors, as well as deep venous thrombosis risk with flying. Further, patient questions pertaining to specific airlines, airports, and Transportation Security Administration policies were answered through direct discussion with representatives, website review, and internet research. Ultimately, providers should be aware of the many challenges that orthopedic patients face during air travel, and patients should consult their providers before making travel plans. Airline passengers are likewise encouraged to equip themselves with the information presented in this article, to best advocate for themselves. This guide should be used as a reference tool, providing up-to-date information about air travel after orthopedic surgery to both patients and providers alike. [Orthopedics. 2021;44(6):e719-e723.].

Examining framing effect in travel package purchase: An application of double-entry mental accounting theory

Based on the double-entry mental accounting theory, we explore the effect of option framing on travelers' purchase decisions regarding customized travel packages through three experiments. The results confirm that subtractive framing leads to higher purchase intentions and willingness to pay than additive framing. However, in the distant future, additive framing leads to higher purchase intention. Pleasure attenuation mediates the option framing effect on travel package purchase decisions, especially for travelers who make travel plans for the near future and those with higher price sensitivity. Pain buffering only plays a mediator role in student samples. The findings provide implications for travel agencies to adopt subtractive framing for customized travel packages and improve the hedonic experiences embedded in packages.

TripAdvisor Ziyaretçi Yorumlarının İçerik Analizi: Göbeklitepe Örneği

Recent technological developments have made easier for consumers to share their experiences and thoughts online. In addition, online comments are suitable when making travel plans. Moreover, the awareness of comments on a tourist site is a benefit for both visitors and managers. In this context, the purpose of the study is to evaluate the comments made by visitors about Gobeklitepe on the TripAdvisor site. In the study, the data were analyzed by content analysis method. All 518 comments made until 19 February 2021 have been analyzed. Reviews are for the period 2013-2020. Expressed positive thoughts are considerably higher than negative thoughts. Cinevision show, impressiveness, grandioseness and extraordinary historical setting, etc. concepts are mentioned among positive impressions. The most important negative opinion is the applied wage policy. Visitors state that both the entrance fee and the prices of some products offered for sale are high. Visitors complain about insufficient parking, lack of tourists guides for individual travelers, ongoing excavations, inability to meet humanitarian needs in the distance between the entrance and the excursion area. These complaints cause negative experiences.

Research on the construction of tourism sign system under the background of all-for-one tourism——Take Helong City, China as an example

The tourism sign is a medium between tourists and scenic spots, and a window to open tourist spots. The tourism sign system is mainly to enable tourists within a certain tourism area to obtain corresponding tourism information in a timely manner, including tourism positioning, scenic spot information, play routes and other content, to provide tourists with comprehensive play services. This paper takes Helong City, Jilin Province, China as an example to discuss the planning and setting methods of tourism sign system in minority cities to aim to provide theoretical and practical basis for the application of tourism sign. According to the tourism development framework of “The all-for-one tourism planning of Helong City”, it follows the linkage radiation ring of three groups in one belt, cultural relics and ecological farms. Utilize the tourism resources of Helong City to construct a complete tourism sign system based on points, lines and surfaces to achieve a comprehensive three-dimensional network-style boutique tourism route guidance. The perfect tourism sign system provides a series of services, which can effectively help tourists make travel plans and improve the quality of tourists’ play.

Interaction Design Research Of Suitable For Student Travel APP

In recent years, with the use of mobile phone APPs, more and more people like to use tourism APPs when traveling, and the tourism industry also enters a new era of wireless tourism. College students are a huge tourist group. This paper will summarize an APP suitable for students’ tourism through the analysis of tourism APPs and tourist groups. This APP will make travel plans based on the student’s time, including travel time, travel residence, travel strategy, travel expenses, tour groups, etc. It also includes working with schools and local police to keep students safe. There should be such an APP for student travel to make it convenient for students to travel, enrich their university experience, and make student travel safer and more convenient.

Collaborative Filtering and Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19 Pandemic

Nowadays, Dynamic industries like tourism is a enhance to boost many countries economy in recent years. The hotel sector leads to a significant role among all aspects of the tourism industry. Online travel platforms, in association with hotel management, are a part of hotel E-tourism that helps users to make travel plans online, suggest precise recommendations in consideration with the earlier feedbacks upon hotel stay. From the past few months, the user roaming ratio falls rapidly due to the COVID-19 Pandemic across the globe. In a business concern, this Pandemic affects all tourism sectors, specifically in the hotels’ occupancy. To retain business positions and attract the users in these problematic scenarios, hotel and travel platform management concerns about past preferences of users, which have a significant impact on gratification for a comfortable stay. The current market trends show that user reviews are playing a vital role to select a hotel based on a safe location. Our work tries to find and recommend the dimensions that contribute more towards higher satisfaction levels of users in Asian continental top tourism city hotels listed by the Master and VISA Inc survey with item collaborative filtering and regression techniques based on Tripadvisor reviews from social media in recent times. The outcome of the work is helpful for Asian continental hotel management to concentrate more on the dimensions for obtaining better reviews. From an online travel platform perspective, this work suggests the contexts for the travel recommender system to gain things effectively from users.

PrePCT: Traffic congestion prediction in smart cities with relative position congestion tensor

Traffic congestion prediction is a vital part of Intelligent Transportation Systems in smart cities. Effective methods for traffic congestion prediction can help people make travel plans reasonably with Advanced Traveler Information Systems. Most of the existing methods for traffic congestion prediction was designed for a specific location. The parameters need to be modified when applying these methods to different locations. Other studies on the traffic network require sophisticated data pre-processing such as map matching. In this paper, we build a model named Relative Position Congestion Tensor and propose a Predictor for Position Congestion Tensor for traffic congestion prediction. First, we design a novel approach to construct congestion matrix on region traffic networks using the concept of relative locations for road nodes and convert matrices into three-dimensional spatio-temporal tensors. Then, we propose a method based on convolutional long-short term memory network to predict congestion at all locations of the road network in the near future. The experiments show that in all locations where congestion often occurs, the proposed method significantly outperforms baseline models including Linear Regression, Autoregressive Integrated Moving Average, Support Vector Regression, Random Forest, Gradient Boosting Regression, Long-Short Term Memory and generally outperforms the Convolution-based deep Neural Network modeling Periodic traffic data. Furthermore, we study the internal structure of the Predictor for Position Congestion Tensor model to analyze the interpretability of the model for congestion prediction. The results show that the proposed model can accurately capture the temporal and spatial characteristics of traffic.

Bus Arrival Time Prediction Using Wavelet Neural Network Trained by Improved Particle Swarm Optimization

Prediction of bus arrival time is an important part of intelligent transportation systems. Accurate prediction can help passengers make travel plans and improve travel efficiency. Given the nonlinearity, randomness, and complexity of bus arrival time, this paper proposes the use of a wavelet neural network (WNN) model with an improved particle swarm optimization algorithm (IPSO) that replaces the gradient descent method. The proposed IPSO-WNN model overcomes the limitations of the gradient-based WNN which can easily produce local optimum solutions and stop the training process and thus improves prediction accuracy. Application of the model is illustrated using operational data of an actual bus line. The results show that the proposed model is capable of accurately predicting bus arrival time, where the root-mean square error and the maximum relative error were reduced by 42% and 49%, respectively.

Uji Validitas dan Uji Reliabilitas Terhadap Faktor-Faktor yang Mempengaruhi Wisatawan Terhadap Pemilihan Wisata Halal

Finding a right strategy to attract tourists is important for tourism businesses. Seeing that the majority of Indonesian population is Muslim, making tourism businesses hold a halal tour. However, it is also necessary to know the factors of tourists choosing a tourist destination so that tourism actors can make travel plans that make tourists choose them. So as to support this research, a questionnaire has been distributed to 50 potential tourists whose contents consist of respondent profiles, market conditions, marketing mix which consists of: products offered, prices, travel locations, promotions carried out, employees, programs offered, and cooperation. Questionnaires were processed with SPSS, based on the validity and reliability tests showed that respondents gave positive results for 27 variabels with Cronbach’s Alpha about 0.841 and KMO 0.725.

Introduction

In the nineteenth century, transmitting knowledge of the world became one of the most important tasks for education. As the last blank spots on the map were filled in, geographical knowledge assumed particular importance. In order to do business, assess political situations, and make travel plans, young adults had to learn more than etiquette, manners, religion and history. This knowledge of the world was conveyed to minors primarily via pedagogical instruction (for example, by way of geography textbooks), but also via adventure stories and travel novels.

Empowering the traveler: an examination of the impact of user-generated content on travel planning

ABSTRACTIn order to understand the role of user-generated content (UGC) in travel planning, this study integrates Psychological Empowerment with the Technology Acceptance Model (TAM), to develop a model of UGC-enabled empowerment and its impact on intention to use UGC when making travel plans. Survey data from 268 backpacker tourists revealed that perceived empowerment and perceived usefulness are significant drivers of attitude and intention to use UGC for travel planning. The findings provide the travel industry with a better understanding of how travelers’ perceptions of and use of UGC is empowering them to take greater control of the travel planning process.

CrowdTravel: scenic spot profiling by using heterogeneous crowdsourced data

Traveling is one of the most important entertainments in the modern society. In general, before traveling to an unfamiliar city, one of the possible ways of travel planning is to search travel information from travel-related websites. With the advances of mobile social networks, increasingly more people are willing to record and share their travel experience via social media, which provides abundant information for people who are going to make travel plans. Textual reviews and travelogues with large scale of photos are two kinds of popular social travel sharing. They are complementary to each other in terms of structure and content, forming a large amount of fragmented travel knowledge. Moreover, the ever-increasing reviews and travelogues may impose a huge burden on gaining and reorganizing travel knowledge. To address these issues, this paper proposes CrowdTravel, a multi-source social media data fusion approach for multi-aspect tourism information perception, which can provide travelling assistance for tourists by crowd intelligence mining. We first study the problem of discovering popular scenic spots over crowd contributed data. Second, we propose a cross-media multi-aspect correlation method to connect fragmented travel information. Then we mine popular travel routes from travelogues based on Sequential Pattern Mining Algorithm. Finally, we achieve cross-media information relevance based on the similarity between the reviews and image contexts. We conduct experiments over a dataset of several popular scenic spots in Beijing and Xi’an, which is collected from two major online travel websites, namely Dazhongdianping and Mafengwo. The results indicate that our approach attains fine-grained characterization for the scenic spots and delivers excellent performance.

Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints

Location-based social networks (LBSNs) provide people with an interface to share their locations and write reviews about interesting places of attraction. The shared locations form the crowdsourced digital footprints, in which each user has many connections to many locations, indicating user preference to locations. In this paper, we propose an approach for personalized travel package recommendation to help users make travel plans. The approach utilizes data collected from LBSNs to model users and locations, and it determines users’ preferred destinations using collaborative filtering approaches. Recommendations are generated by jointly considering user preference and spatiotemporal constraints. A heuristic search-based travel route planning algorithm was designed to generate travel packages. We developed a prototype system, which obtains users’ travel demands from mobile client and generates travel packages containing multiple points of interest and their visiting sequence. Experimental results suggest that the proposed approach shows promise with respect to improving recommendation accuracy and diversity.