Short-term Travel Research Articles

Educational Travel and Personal Development: Deconstructing the Short-Term Study Abroad Experience

ABSTRACT Taking an explanatory mixed-method approach, this research examines how participation in short-term educational travel abroad affects young adults’ personal development. Different from previous research investigating the study abroad trip as one holistic concept, the current research deconstructs study abroad experiences into travel and education components and examines each component’s effect on undergraduate students’ post-trip developmental benefits. Through a quantitative survey-based study and a follow-up qualitative focus-group study, specific travel- and education-related components of study abroad experience are identified as salient factors promoting young travelers’ personal development progress, including participation in group excursions and experiential learning activities, self-management of travel logistics, and engagement in diverse and authentic social/cultural interactions. The research findings provide insights into how learning occurs through travel and highlight short-term study abroad as an impactful special-purpose tourism product. This research contributes to a niche tourism topic regarding travel experience design with the purpose of fostering personal development.

Public transport congestion detection using incremental learning

In the past decade, intelligent transportation systems have emerged as an efficient way of improving transportation services, while machine learning has been the key driver that created scopes for numerous innovations and improvements. Still, most machine learning approaches integrate paradigms that fell short of providing cost-effective and scalable solutions. This work employs long short-term memory to detect congestion by capturing the long-term temporal dependency for short-term public bus travel speed prediction to detect congestion. In contrast to existing methods, we implement our solution as incremental learning that is superior to traditional batch learning, enabling efficient and sustainable congestion detection. We examine the real-world efficacy of our prototype implementation in Pécs, the fifth largest city of Hungary, and observed that the incrementally updated model can detect congestion of up to 82.37%. Additionally, we find our solution to evolve sufficiently over time, implying diverse real-world practicability. The findings emerging from this work can serve as a basis for future improvements to develop better public transportation congestion detection.

A city-level transport vision for 2050: Reimagined since COVID-19

Transformative changes are needed in the transport sector to limit global warming. Radical transport disruptions experienced during the COVID-19 pandemic, such as greater Working from Home (WFH) and active travel, present a unique opportunity to reimagine more sustainable transport systems. The aim of the current study was to develop a 2050 transport vision and identify short term priorities for Melbourne (Australia) based on in-depth stakeholder interviews. To the best of our knowledge, this is the first backcasting study since COVID-19. As the city with the ‘longest lockdown’, Melbourne has valuable lessons for the rest of the world. Overall, participants reported that they were uncertain about the future of the central business district. Participants envisaged that the transport system would be carbon-neutral or carbon-positive. However, private motor vehicles (including electric and automated) were not considered the solution for handling the scale of trips anticipated with the projected population size. Instead, participants perceived that in Melbourne by 2050, there will be less work-related travel due to greater job flexibility and WFH. More localised neighbourhood living (20-minute cities), with most short trips undertaken by active travel, and longer trips by public transport. Furthermore, it was projected that regional centres will grow and the transport system will be for the whole state of Victoria and not just Melbourne. Finally, the study identified short term (2021–2030) travel behaviour priorities and eight immediate actions, including: urban design focusing on inspiring active travel; reallocating road space to prioritise active and public transport modes; planning for micromobility urban freight; improving public transport services; expanding public transport networks; installing electric vehicle charging infrastructure; supporting WFH to encourage trip avoidance; and encouraging political consensus when building major transport projects.

Pulmonary cystic echinococcosis acquired during a short-term tourist travel

Pulmonary cystic echinococcosis acquired during a short-term tourist travel

Human Health during Space Travel: State-of-the-Art Review

The field of human space travel is in the midst of a dramatic revolution. Upcoming missions are looking to push the boundaries of space travel, with plans to travel for longer distances and durations than ever before. Both the National Aeronautics and Space Administration (NASA) and several commercial space companies (e.g., Blue Origin, SpaceX, Virgin Galactic) have already started the process of preparing for long-distance, long-duration space exploration and currently plan to explore inner solar planets (e.g., Mars) by the 2030s. With the emergence of space tourism, space travel has materialized as a potential new, exciting frontier of business, hospitality, medicine, and technology in the coming years. However, current evidence regarding human health in space is very limited, particularly pertaining to short-term and long-term space travel. This review synthesizes developments across the continuum of space health including prior studies and unpublished data from NASA related to each individual organ system, and medical screening prior to space travel. We categorized the extraterrestrial environment into exogenous (e.g., space radiation and microgravity) and endogenous processes (e.g., alteration of humans' natural circadian rhythm and mental health due to confinement, isolation, immobilization, and lack of social interaction) and their various effects on human health. The aim of this review is to explore the potential health challenges associated with space travel and how they may be overcome in order to enable new paradigms for space health, as well as the use of emerging Artificial Intelligence based (AI) technology to propel future space health research.

Improving short-term bike sharing demand forecast through an irregular convolutional neural network

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial–temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a “matrix-format” city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to leverage the hidden linkage among “semantic neighbors”. The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that “thinking beyond spatial neighbors” can further improve short-term travel demand prediction of urban bike sharing systems.

An attention-based recurrent learning model for short-term travel time prediction.

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

Periodic Attention-based Stacked Sequence to Sequence framework for long-term travel time prediction

Travel time analysis and prediction are keystones for building intelligent transportation systems in the new era, which has gained wide attention from the research community. Over the last few years, deep learning-based approaches have demonstrated the capability to predict travel time with big data. However, the existing works focused on predicting short-term travel time for paths and roads. In fact, knowing a period of long-term future travel time is a demand with important applications like traffic management and schedule routing planning. Nevertheless, studies on long-term traffic prediction are still very limited due to the complicated underlying factors. In this paper, we propose a novel deep learning-based framework named Periodic Attention-based Stacked Sequence to Sequence (PASS2S), which aims to address the long-term traffic prediction problem. PASS2S consists of two main components, namely periodic segment generation and attention-based stacked sequential prediction. To extract periodic information, we design a periodic segment generation component to capture historical periodic segments from the traffic data. To reduce the error propagation and improve long-term prediction accuracy, we propose an attention-based stacked prediction component to model long short-term and short-term dependencies from the periodic segments. We conducted a series of experiments on a real-world travel time dataset and the experimental results show that our proposed approach outperforms the state-of-the-art competing methods in terms of various metrics like MAE, RMSE, and SMAPE. To the best of our knowledge, this is the first work that considers attentive periodic historical information for solving the long-term travel time prediction with a period of the future, which has not been well studied in the research community.

Affective learning in short-term educational travel abroad: An exploratory mixed-method study

Employing an exploratory mixed-method approach, this research explores young adults' affective learning outcomes derived from their short-term educational travel abroad experiences. Different from previous travel research mostly focusing on the educational benefits of cognitive knowledge and technical skills, the current research highlights the prominent effect of educational travel on young adults' personal growth and attitudinal/emotional development by investigating the understudied domain of affective learning and how it manifests among college students having short-term study abroad experiences. Through a systematic review and a follow-up survey-based comparison study, five salient affective learning variables were identified—perspectives on global interdependence, intercultural attitudes, openness to diversity and challenge, environmental attitudes, and general self-efficacy. Furthermore, this research found that travelers’ lower-order affective learning shows significant progress after the short-term educational overseas travel. This study contributes to a niche topic in tourism research and provides implications to promote educational travel as an effective transformative learning approach.

Risk Factors Associated with Passengers with Imported Dengue Fever at International Airports in Taiwan.

Dengue fever (DF) is a mosquito-borne disease prevalent in the tropics (e.g., sub-Saharan Africa, Asia, and Central and South America) and a common cause of febrile illness in travelers. The high incidence of imported DF in Taiwan has led to a domestic outbreak. This study explored the risk factors associated with individuals given diagnoses of imported DF at international airports in Taiwan. The results may serve as a reference for DF prevention. In this retrospective study, data from the symptom notification system database of the Taiwan Centers for Disease Control (TCDC) were used. These data concerned travelers who returned to Taiwan from DF-endemic areas with suspected DF symptoms. The epidemiological characteristics of the cases were analyzed, and 28 variables related to DF infection were included in the multivariate logistic regression analysis. In 2018–2019, there were 8656 cases (451 positive and 8205 negative cases). The results revealed DF symptoms and a 16–30-day stay in endemic areas to be independent risk factors and the presence of three respiratory symptoms and <10 days of short-term travel to be protective factors. These results may enable the accurate assessment of symptoms in travelers with DF as well as the risk factors associated with imported DF, lowering the risk of indigenous DF outbreaks caused by imported DF.

Constructing Cooperative Intelligent Transport Systems for Travel Time Prediction With Deep Learning Approaches

Drivers and traffic system planners require accurate forecasting of future travel times. To alleviate traffic congestion on Taiwan’s Provincial Highway No. 61, this study considers temporal (weekdays, weekends or continuous holidays) and spatial characteristics (road types), and establishes short-term and long-term travel time prediction models. Data pre-processing is accomplished using the Google Maps API and floating car method to verify travel time comparisons, finding that post-processing travel times are reliable, credible and reasonable. This study develops a collaborative intelligent transportation system (CITS) based on 9 different algorithms for the prediction of current and future travel-time. The results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN-R provides the most accurate short-term predictions, with average prediction error within 20 seconds per kilometer. For long-term forecasting, SARIMAX and fbProphet provide the most accurate results for weekday and continuous holiday modes. Travel time prediction can assist traffic management agencies in the timely implementation of appropriate traffic management measures. CITS also provides real-time traffic query and travel time prediction functions to help drivers avoid traffic congestion.

Malaria in long-term travelers: Infection risks and adherence to preventive measures – A prospective cohort study

BackgroundChemoprophylaxis and anti-mosquito measures are key to preventing malaria in travelers. Long-term travelers are at higher risk than short-term travelers, but their adherence to preventive measures is lower. Our aim was to determine malaria exposure risks and predictors for adherence to malaria-preventive measures in long-term travelers. MethodsLong-term travelers (>12 weeks) completed a weekly questionnaire about preventive measures, symptoms, and malaria treatment abroad. Blood samples were tested for seroconversion to Plasmodium falciparum anti-circumsporozoite (PfCSP) antibody. Adherence to preventive measures was defined as number of weeks of their usage divided by number of weeks in malaria-endemic areas. ResultsOf 561 travelers, the median travel time was 20 weeks (IQR 16–25). Eighteen were treated for malaria, all in sub-Saharan Africa. Sixteen PfCSP seroconversions were found, of whom only 3 had traveled to high-endemic areas. Of the 18 travelers treated for malaria, only one seroconverted. No associations were found between covariates and seroconversion. Neither treatment abroad nor seroconversion were reliable predictors for exposure. ‘Full adherence’ to chemoprophylaxis was reported by 52% (218/417) and was associated with travel to Africa, use of mefloquine, lack of prior travel history, shorter duration of travel, and use of DEET. ConclusionsThe risk of malaria in this long-term travelers cohort was low. Our data confirm that anti-PfCSP seroconversion is not a reliable method to retrospectively identify incident infection, or probably exposure. Prevention efforts should focus on more experienced travellers and longer travel duration, for whom mefloquine should be considered as the first-choice chemoprophylaxis.

Short-Term Holiday Travel Demand Prediction for Urban Tour Transportation: A Combined Model Based on STC-LSTM Deep Learning Approach

Short-term prediction of holiday travel demand is a complex but key issue to the planning and management of tour transportation system in big cities. This paper develops an improved spatial and temporal correlation long short-term memory model (STC-LSTM) to forecast short-term holiday travel demand based on deep learning approach. Analysis results show six kinds of tourist flow correlations appears in different sets of tourist attractions, and 27.94 percent of the tourist attractions have mid- or high- positive tourist flow correlation with others, meaning that a positive synchronization mechanism exists between suburban tourist attractions in Beijing. The proposed model predicts the holiday travel demand on the basis of the historical data of the spatial and temporal related tourist flows, and the auxiliary data including meteorological data, temporal data, and Internet search index. Based on actual case study with tourist flow data of the suburban tourist attraction in Beijing, the proposed STC-LSTM is carried out to compared with other conventional prediction approaches. Results show that the proposed approach can improve the prediction accuracy and well capture the different spatial and temporal correlations of tourist flows.

Voluntary Underclass?: Globalism, Temporality, and the Life Choices of Japanese Working Holiday Makers in Australia

Abstract The working holiday (wh) program is a cultural exchange program that offers youth ‘global experiences’ including short-term employment, travel, and/or study. Australia is the most popular destination for Japanese working holiday makers (whm s): over 10,000 Japanese whm s were in Australia before the pandemic hit the country in March 2020. Despite the fact that most had held secure white-collar jobs in Japan, they moved to Australia and voluntarily took precarious jobs in agriculture, hospitality, and childcare. What made them decide to accept such significant downward mobility and acquiesce to unfavorable conditions such as underpayment, non-payment, and sometimes even harassment? How did the covid-19 pandemic affect their lives in Australia? Based on data from surveys and qualitative interviews, this article analyses the relationship between globalism and a willingness for Japanese youth migrants to join the ‘underclass’ in Australia.

Evaluation of Spatial and Temporal Performance of Deep Learning Models for Travel Demand Forecasting: Application to Bike-Sharing Demand Forecasting

Deep learning approaches are widely employed for forecasting short-term travel demand to respond to real-time demand. Although it is critical for demand forecasting to be evenly distributed in the spatial and temporal views to support real-time mobility service operations, in related studies, the predictive performance of models has been evaluated only in terms of aggregated errors. Therefore, the present study was conducted to investigate the distribution of errors to explore spatiotemporal correlations. Six deep learning models with the same architecture, except for the base module, consisting of three stacked layers, were constructed. These models were used to forecast demands for a station-based bike-sharing service in Seoul, South Korea. To attain our goals, global and local Moran’s I of the errors was introduced to evaluate the spatial and temporal performances of the deep learning approaches. The results showed that the model with convolutional long short-term memory layers, which are effective at predicting spatiotemporal data, outperformed the other models in terms of aggregated performance. However, the global Moran’s I of the errors in the model reflects spatial dependency over the regions. This suggests that the best predictive performance of the model does not necessarily imply that it performs well in demand forecasting in all regions. Furthermore, cluster and outlier analyses of the errors indicated that excessive or insufficient predictions were clustered or dispersed throughout the regions. These results can be used to enhance the model by introducing the spatial correlation index into the loss function or by incorporating additional features for handling spatial correlations.

Open Innovation—Opportunities or Nightmares for the Shared Transport Services Sector?

Shared transport services, including short-term vehicle rentals (bike-, car-, moped-, scooter-sharing) and travel sharing systems (ride-sharing, ride-hailing), have become more and more popular forms of mobility in recent years. Their increasing availability has made them an alternative to individual transport in cities. Along with the development of the systems, a growing number of operators offering sharing services started to appear. Many of them ended their activities after the pilot period or closed the company shortly after launching the system. There are many reasons for this, from management to technical issues, but the aspects of open innovation, open data and transparency were often overlooked. This begs the question whether it is the openness of systems that is the recipe for the market success or failure of shared mobility services. The aim of this work was to evaluate the approaches to data openness and innovation in companies representing the shared mobility market. A research proposal was submitted to diagnose the approach to open innovations in the market of shared mobility services. An expert survey was carried out among representatives of shared mobility services operating in major Polish cities. The expert survey was conducted using the CAWI technique. Research results show that open innovation is a problematic aspect for operators. On the one hand, they are interested in it, but this interest does not translate into real practices. Among the entire shared transport sector, the micro-mobility service providers are the most committed to open innovation. Research indicates that in order to increase the dynamics of the development of open innovation in the shared transport industry, there is a need for education in the field of open innovation, especially in the era of the development of digitization of urban transport systems and the pursuit of sustainable transport.

Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects

The accurate short-term travel demand predictions of ride-hailing orders can promote the optimal dispatching of vehicles in space and time, which is the crucial issue to achieve sustainable development of such dynamic demand-responsive service. The sparse demands are always ignored in the previous models, and the uncertainties in the spatiotemporal distribution of the predictions induced by setting subjective thresholds are rarely explored. This paper attempts to fill this gap and examine the spatiotemporal sparsity effect on ride-hailing travel demand prediction by using Didi Chuxing order data recorded in Chengdu, China. To obtain the spatiotemporal characteristics of the travel demand, three hexagon-based deep learning models (H-CNN-LSTM, H-CNN-GRU, and H-ConvLSTM) are compared by setting various threshold values. The results show that the H-ConvLSTM model has better prediction performance than the others due to its ability to simultaneously capture spatiotemporal features, especially in areas with a high proportion of sparse demands. We found that increasing the minimum demand threshold to delete more sparse data improves the overall prediction accuracy to a certain extent, but the spatiotemporal coverage of the data is also significantly reduced. Results of this study could guide traffic operations in providing better travel services for different regions.

Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model

In recent years, online ride-hailing has become an indispensable part of residents’ travel mode. Therefore, the prediction of online ride-hailing travel demand has become extremely important. In the era of big data, the application of big data in the field of transportation is becoming more extensive. Based on the open data of ride-hailing trips in Haikou City, Hainan Province, provided by the Didi platform and combined with the rainfall data of Haikou City, this paper proposes a gate recurrent unit (GRU) model considering rainfall factors and rest days factors for short-term trip demand prediction. The K-fold cross-validation method is adopted to adjust the parameters of the model to the optimal ones through the training set. The improved GRU model is compared with the original GRU model and other classic models, and the model is evaluated by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score indexes. Finally, it is proved that the GRU model proposed in this paper greatly improves the prediction accuracy of short-term online ride-hailing travel demand.

Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction

Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation.

Military and Civilian Sector Practice Patterns for Short-Term Travelers' Diarrhea Self-Treatment in Adults.

ABSTRACT.The Deployment and Travel Medicine Knowledge, Attitude, Practice and Outcomes Study investigates the various clinician and traveler contributions to medical outcomes within the U.S. Military Health System. Travelers’ diarrhea is among the most common travel-related illnesses, making travelers’ diarrhea self-treatment (TDST) important for traveler health. A cohort of 80,214 adult travelers receiving malaria chemoprophylaxis for less than 6 weeks of travel were identified within the U.S. Department of Defense Military Health System Data Repository. Associated prescriptions for TDST medications within 2 weeks of chemoprophylaxis prescriptions were identified. Prescription patterns were compared by service member versus beneficiary status and site of care, military facility versus civilian facility. At military facilities, medical provider demographics were analyzed by clinical specialty and categorized as travel medicine specialists versus nonspecialists. Overall, there was low prescribing of TDST, particularly among civilian providers and military nonspecialists, despite guidelines recommending self-treatment of moderate to severe travelers’ diarrhea. This practice gap was largest among service member travelers, but also existed for beneficiaries. Compared with nonspecialists, military travel medicine specialists were more likely to prescribe a combination of an antibiotic and antimotility agent to beneficiaries, more likely to provide any form of TDST to service members, and more likely to prescribe azithromycin than quinolones when using antibiotics. Our study suggests that enhancing provider knowledge and use of travelers’ diarrhea treatment recommendations combined with improved access to formal travel medicine services may be important to increase the quality of care.