Short-term Travel Research Articles

Short-Term Travel Volume Prediction and Delivery Volume Prediction of Shared Bicycles Built on Different Sites

Short-Term Travel Volume Prediction and Delivery Volume Prediction of Shared Bicycles Built on Different Sites

Perceived Impact of Short-Term International Experiences on Personal & Professional Beliefs of Health Care Practitioners: A Longitudinal Study

Abstract Date Presented 03/23/24 This phenomenological study used semistructured interviews to understand the perceived long-term impact of short-term international travel as graduate students on current professional practices. Results support use of community-based global learning (CBGL) as an ethical framework. Primary Author and Speaker: Valerie Strange Additional Authors and Speakers: Brittany Balch, Marynell Disman, Bobbi Dynice, Cecil Huang

Exploring the Impact of Short Term Travel on Gut Microbiota and Probiotic Bacteria Mediated Stability.

Although travelers are frequently accompanied by abdominal discomfort and even diarrhea, not every trip can cause this issue. Many studies have reported that intestinal microbes play an important role in it. However, little is known about the reason for the dynamics of these intestinal microbes. Here, we delved into the effects of short-term travel on the gut microbiota of 12 healthy individuals. A total of 72 fecal samples collected before and after one-week travel, alongside non-traveling controls, underwent amplicon sequencing and a series of bioinformatic analyses. We found that travel significantly increased intra-individual gut microbiota fluctuations without diarrhea symptoms. In addition, the initial composition of the gut microbiota before travel emerged as a crucial factor in understanding these fluctuations. Travelers with stable microbiota exhibited an enrichment of specific probiotic bacteria (Agathobaculum, Faecalibacterium, Bifidobacterium, Roseburia, Lactobacillus) before travel. Another batch of data validated their predictive role in distinguishing travelers with and without the gut microbial disorder. This work provided valuable insights into understanding the relationship between gut microbiota and travel. It offered a microbiota-centric perspective and a potential avenue for interventions to preserve gut health during travel.

Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques

In the evolving field of Intelligent Transportation Systems (ITSs), accurate and reliable traffic prediction is essential in enhancing management and planning capabilities. Accurately predicting traffic conditions over both short-term and long-term intervals is vital for the practical application of ITS. The integration of deep learning into traffic prediction has proven crucial in advancing traffic prediction beyond traditional approaches, particularly in analyzing and forecasting complex traffic scenarios. Despite these advancements, the existing methods are unable to effectively handle both short-term and long-term traffic patterns given their complex nature, revealing a need for more comprehensive forecasting solutions. To address this need, we propose a new approach named the Short-Term and Long-Term Integrated Transformer (SLIT). SLIT is a Transformer-based encoder–decoder architecture, designed for the effective prediction of both short-term and long-term travel time durations. The architecture integrates the Enhanced Data Preprocessing (EDP) with the Short-Term and Long-Term Integrated Encoder–Decoder (SLIED). This harmonious combination enables SLIT to effectively capture the complexities of traffic data over varying time horizons. Extensive evaluations on a large-scale real-world traffic dataset demonstrate the excellence of SLIT compared with existing competitive methods in both short- and long-term travel time predictions across various metrics. SLIT exhibits significant improvements in prediction results, particularly in short-term forecasting. Remarkable improvements are observed in SLIT, with enhancements of up to 9.67% in terms of all evaluation metrics across various time horizons. Furthermore, SLIT demonstrates the capability to analyze traffic patterns across various road complexities, proving its adaptability and effectiveness in diverse traffic scenarios with improvements of up to 10.83% in different road conditions. The results of this study highlight the high potential of SLIT in significantly enhancing traffic prediction within ITS.

Periodic Transformer Encoder for Multi-Horizon Travel Time Prediction

In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traffic analysis, the dynamic nature of traffic patterns frequently challenges the forecasting capabilities of existing models, especially when forecasting both immediate and future traffic conditions across various time horizons. Additionally, the area of long-term travel time forecasting still remains not fully explored in current research due to these complexities. In response to these challenges, this study introduces the Periodic Transformer Encoder (PTE). PTE is a Transformer-based model designed to enhance traffic time predictions by effectively capturing temporal dependencies across various horizons. Utilizing attention mechanisms, PTE learns from long-range periodic traffic data for handling both short-term and long-term fluctuations. Furthermore, PTE employs a streamlined encoder-only architecture that eliminates the need for a traditional decoder, thus significantly simplifying the model’s structure and reducing its computational demands. This architecture enhances both the training efficiency and the performance of direct travel time predictions. With these enhancements, PTE effectively tackles the challenges presented by dynamic traffic patterns, significantly improving prediction performance across multiple time horizons. Comprehensive evaluations on an extensive real-world traffic dataset demonstrate PTE’s superior performance in predicting travel times over multiple horizons compared to existing methods. PTE is notably effective in adapting to high-variability road segments and peak traffic hours. These results prove PTE’s effectiveness and robustness across diverse traffic environments, indicating its significant contribution to advancing traffic prediction capabilities within ITS.

Campervans Community Lifestyle in Bandung: A Case Study in Environmental Communication

Travel has evolved from mere tourism to mass tourism, focusing on leisure and recreational experiences. Nature-based tourism has become popular. Indonesia ranks 32 out of 117 in the Travel and Tourism Development Index, but limited accommodation options hinder its growth. The Ministry of Tourism plans to introduce campervans in 2022 to improve tourism quality. Therefore, research titled "Campervans Community Lifestyle in Bandung: A Case Study in Environmental Communication" is made, aiming to know the lifestyle of campervans community in Bandung as a case study in environmental communication. The research explores the campervan lifestyle in Bandung, a unique mode of transportation with various options for short-term and long-term travel. Despite its origins outside Indonesia, the lifestyle benefits society, and its adopters. However, the government in Bandung does not fully acknowledge or encourage it, leading to a demand for expedited permits and improved amenities

Border effects for domestic travel in China during COVID-19 pandemic

While numerous studies have evaluated the role of international borders, few efforts have been made to assess the influence of domestic borders on travel during COVID-19. In China, the nearly three-year travel control, starting in early 2020, has caused considerable fluctuations in public perceptions and sentiments. Domestic travel behaviors were disrupted, and in some regions, even reshaped. This study evaluates border effects for domestic travel in China from 2019 to 2021. Utilizing short-term intercity travel flow data obtained from Baidu Map, we configured and performed a series of modified gravity models to reveal provincial border effects at three levels of detail. Several major conclusions can be drawn from the results: First, there is a consistent border effect on domestic travel within China, both pre-pandemic and during the pandemic. Second, border effects are directional, as they vary for inbound and outbound travel and depend on the origin and destination. Third, border effects exhibit clear spatial patterns that are associated with location and travel distance. Fourth, the impacts of COVID-19 on border effects are predominantly temporary; however, in some instances, they may be long-term. The results highlight the necessity of considering borders when estimating travel flows using spatial interaction models. This study also sheds light on related policymaking, as restricting or loosening travel controls at borders can have profound impacts on economic activities and disease transmission.

Introducing a Novel Framework for the Analysis and Assessment of Transport Projects in City Regions

A profound appraisal framework has been developed and refined in transport economics and planning literature for decades, mainly characterised by welfare economic theory, cost–benefit analysis, and transport demand modelling. In summary, the appraisal methodology and its applications have concentrated on single infrastructure measures, marginal impacts identified through ceteris paribus comparisons, forecasts based on trends from the past, and monetary assessments of all quantifiable impacts. However, this framework has been continuously contested in transport planning literature, for instance, for its focus on travel demand and short-term travel time savings. Therefore, we suggest a novel approach for planning and assessing transport schemes in city regions, combining accessibility analyses, quantitative target indicators, and cost-effectiveness analysis. We develop and test this approach by assessing a proposed underground rail project in the Munich city region, the U5 southeast extension. In this case, we define an accessibility target level and estimate the potential for push measures along with the U5 project. We find modest impacts on quantitative targets in the Munich city region: Even when the U5 southeast extension is bundled with push measures in selected transport cells, the contribution to passenger transport-related carbon dioxide emission targets and primary energy consumption targets is low. Nevertheless, we demonstrate that the proposed assessment framework can support strategic transport planning in city regions. We argue for a change in perspective towards supply-side-oriented urban transport planning. Our proposed methodology is a first step in a different direction towards a sustainable mobility planning paradigm.

Incidence and risk factors for travellers' diarrhoea among short-term international adult travellers from high-income countries: a systematic review with meta-analysis of cohort studies.

Travellers' Diarrhoea (TD) continues to be the most common travel-related medical event in international travellers. Updated incidence and risk factor data will improve pre-travel medical advice for travellers from high-income countries (HIC), providing an opportunity for disease prevention, and appropriate disease management. A systematic search for cohort studies of TD incidence published between 1 January 1997 and 2 March 2023 was performed using Ovid Medline, SCOPUS, and Google Scholar databases. Study quality was assessed with a modified Newcastle-Ottawa scale (NOS). We extracted incidence data for adults travelling less than 100days from HIC and available risk factor data. The overall random-effects pooled incidence and the corresponding 95% confidence intervals (95% CI) were estimated. Heterogeneity was assessed using the I2 statistic, tau and the 95% prediction intervals. Subgroup analyses were conducted to identify sources of heterogeneity. Risk factor studies were reviewed qualitatively and described. Ten studies were included in the meta-analysis, containing 8478 participants. Two of the studies measured as high quality and eight as good quality as assessed by the modified NOS. The TD incidence was 36.1% (95% CI 24-41%; I2 94%), with a prediction interval ranging from 20.3% to 55.8%. The pooled incidence of mild, moderate, and severe TD was 23.6%, 8.1% and 2.9%, respectively. Subgroup analysis showed that the incidence increased with increasing average data collection period. Risk factors for TD in travellers from HIC identified include younger age, longer travel periods, low and middle-income destinations, travelling for tourism, backpacking travel styles and pre-travel health status. It is estimated that between 20 to 56% of international travellers can expect to develop TD in travel of under 100days. While most cases are mild, approximately 3% of all travellers will experience a disease that prevents usual activities or requires medical attention.

Global Short-Term Business Travel Compliance and Its Impact on Job Stress of Employee

Global Short-Term Business Travel Compliance and Its Impact on Job Stress of Employee

Assessing the Long-Term Impact of COVID-19 on Travel Behavior: The United Arab Emirates Perspective

The unprecedented COVID-19 pandemic has caused significant short-term travel disruptions and profound changes in daily travel routines. Yet, the long-term impact on travel behavior and mobility remains unclear. The present study examines the enduring effects of COVID-19 on travel patterns in the United Arab Emirates (UAE) through a comprehensive survey assessing long-term impacts and participants' travel behavior changes during different stages of the pandemic. The survey covered travel modes, frequency, duration, and distance, unveiling a pandemic-driven shift towards increased personal vehicle usage due to public transportation concerns. Nevertheless, as the pandemic gradually recedes, individuals are slowly reverting to their pre-pandemic travel habits. Moreover, the results indicate that travel patterns vary depending on the distance traveled. Short-distance travel has largely returned to pre-pandemic levels, while medium-distance travel continues to lag. Intriguingly, post-pandemic levels of long-distance travel have exceeded the pre-pandemic figures, indicating a potential alteration in travel behaviors. These observed changes were substantiated by a paired t-test, demonstrating a significant difference across nearly all periods examined. In summary, this research sheds light on the long-term consequences of COVID-19 on travel behavior and mobility in the UAE. The survey data uncovers a noteworthy transformation in travel patterns during the pandemic, with subsequent gradual reversion to pre-pandemic norms. Notably, the findings suggest potential shifts in travel preferences across different distances, with long-distance travel surpassing pre-pandemic levels. These insights contribute to a deeper understanding of the lasting impacts of COVID-19 on transportation and travel choices.

Computational Architecture of an Integrated Urban Model Considering Physical-Virtual Activity Spaces

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households’ and individuals’ long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model’s flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond.

ОСОБЛИВОСТІ ПРАВОВОГО СТАТУСУ ЗАСУДЖЕНИХ ДО ДОВІЧНОГО ПОЗБАВЛЕННЯ ВОЛІ

The article addresses to the study of the legal status peculiarities of those sentenced to life imprisonment. The authors indicate that peculiarities regarding the legal status of persons sentenced to life imprisonment differ greatly, but they believe that the most successful is the basic model of the structure, which is called traditional in science: subjective rights, legal interests and duties of convicts. The rights of those sentenced to life imprisonment are set out in Art. 107 and Art. 151 of the Criminal and Executive Code of Ukraine. While analyzing the petition of Luzhynetskyi A.O. to the Constitutional Court of Ukraine regarding the granting of the right to short-term travel outside the prisons to those sentenced to life imprisonment, the authors came to the conclusion that it is not appropriate to grant such a right. The author points out that the institution of punishment in the form of life imprisonment is undergoing significant changes, however, in the opinion of the authors, it is not appropriate to grant this category of convicts the right to short-term departures from prisons: First, those sentenced to life imprisonment enjoy all the rights provided for those sentenced to imprisonment for a certain period of time, with the exception of those restrictions prescribed for them based on the gravity of the crime committed and its criminal activity. Second, those sentenced to imprisonment do not have the right to leave the prison for a short time. This permission can be granted only by the head of the institution, under exceptional circumstances, if the convict is not inclined to continue his illegal activities and proves his correction. This category of convicts is the most dangerous in connection with the severity of the crime committed, the severity of the punishment, and the impossibility of applying a more severe punishment than that provided for by the previous criminal activity. Third, in the case of correction of a person sentenced to life imprisonment, such elements of the progressive system of execution of punishments may be applied to him, such as replacing life imprisonment with imprisonment for a certain period, and in the future, conditional early release. Key words: convict, life imprisonment, deprivation of liberty, legal status, material, household and health care support, vacation, short-term departure, penitentiary institution, change of conditions of detention, prevention.

The Impact of Infectious Disease Syndromes on Activities During Military Travel.

We evaluated the impact of infectious disease (ID) syndromes on US active duty (AD) servicemembers returning from overseas deployment (DEP), military training exercises (EXR), or short-term military travel (eg, temporary assignment of duty [TDY]). We conducted a survey-based assessment of US AD servicemembers returning from DEP, EXR, or TDY between 2015 and 2019. Subjects completed a post-travel survey capturing symptoms of travelers' diarrhea (TD), influenza-like illness (ILI), and febrile illness (FI). Risk factors associated with any ID syndrome (ie, either TD, ILI, or FI) that impacted daily activities were assessed using a logistic regression model with backward selection. One-third of servicemembers (654/1822) experienced an ID syndrome, and 26% (471/1822) reported a ≥50% reduction in activity level due to an ID syndrome (median duration, 3 days). TD was the most common ID syndrome experienced and accounted for 73% (346/471) of ID syndromes impacting daily activities. The greatest impact of ID syndromes was observed in servicemembers on DEP. Compared with servicemembers on EXR or TDY, those on DEP had a longer duration of travel and a delayed period of risk for ID syndromes. Multivariate analysis identified high-risk exposures (ie, environmental exposures, close contact with locals, consuming food from street vendors) and behaviors (ie, inability to sanitize hands before meals) that could be used to inform mitigation strategies. ID syndromes result in a significant loss of productivity during military travel. Addressing modifiable risk factors and access to TD self-treatment in high-risk settings may help mitigate the impact of ID threats during military travel.

Epidemiological and clinical analysis of polish short-term and long-term travelers returning from tropical countries

BackgroundThere is a lack of information about health problems after returning from tropical countries among travelers from Poland. The aim was to create characteristics of diseases imported to Poland from tropical regions and to determine the changes in the travel trends. MethodThis retrospective study is based on medical records of 2391 Polish patients >18 years old, hospitalized between 2006 and 2016 after returning from tropical areas. The analysis covered purpose, duration and travel destination, and health problems related to the travel. 1098 patients (short travel, <1 month, n = 345 vs long travel, >6 months, n = 753) were selected for further analysis. ResultsThe most frequently visited region was Sub-Saharan Africa. Tourists dominated among short-term and missionaries among long-term travelers. The most popular health problems in both groups were digestive system disease and febrile diseases. Diarrhoea of undetermined aetiology, dengue fever, malaria, fever of unknown aetiology and infectious mononucleosis were more likely to occur among short-term travelers whereas blastocystosis, giardiasis, schistosomiasis among long-term travelers. In the group of long-term travelers 363/753 (47,8%) were diagnosed with an infectious or parasitic disease in relation to a trip to a country with a hot climate. ConclusionsTropical diseases occur among Polish travelers so they should be taken into account in the context of prophylaxis when preparing for travel and in the diagnosis of diseases that occur after returning from a tropical zone. This first analysis of disease incidence among Polish travelers indicates a strong need for more research in this area.

Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling

This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem. The uncertainty is modeled directly as part of recurrent artificial neural networks, but using two fundamentally different approaches: one based on Deep Quantile Regression and the other on Bayesian neural network. Both approaches use a recurrent neural network to predict multiple time steps into the future, but handle the time-dependent uncertainty estimation differently. We present a novel sampling technique in order to aggregate quantile estimates for link level travel time to yield the multi-link travel time distribution needed for a vehicle to travel from its current position to a specific downstream stop point or transfer site.To motivate the relevance of uncertainty-aware models in the domain, we focus on the connection protection application as a case study: An expert system to determine whether a bus driver should hold and wait for a connecting service, thus ensuring the connection, or break the connection and reduce its own delay. Our results show that the proposed quantile sampling method performs overall best for the 80%, 90% and 95% prediction intervals, both for a 15 min time horizon into the future (t+1), but also for the 30 and 45 min time horizon (t+2 and t+3), with a constant, but very small underestimation of the uncertainty interval (1–4 pp.). However, we also show, that the Bayesian model still can outperform the DQR for specific cases. Lastly, we demonstrate how a simple decision support system can take advantage of our uncertainty-aware travel time models to prioritize the difference in travel time uncertainty for bus holding at strategic points, thus reducing the introduced delay for the connection protection application.

Floating Car Data–Based Short-Term Travel Time Forecasting with Deep Recurrent Neural Networks Incorporating Weather Data

The prediction of future traffic conditions represents a main building block for traffic management. With the advent of multiple traffic and environmental sensors, diverse data for predictions are available and models may incorporate not only traffic data but also additional aspects such as local weather conditions. A review of the state-of-the art methods in short-term traffic forecasting presented in this paper reveals that machine learning (ML) algorithms from the field of deep learning are occasionally used for forecasts based on historical traffic data, but not for traffic predictions including exogenous factors. Weather conditions represent an exogenous factor, which, for example, may affect travel times. This paper investigates how short-term travel time predictions may be improved by applying deep recurrent neural networks that incorporate weather data. Therefore, two hypotheses are formulated. Hypothesis 1 tests the prediction quality of the recurrent neural network (RNN) models, long short-term memory (LSTM), and gated recurrent unit (GRU) compared to the autoregressive moving average (ARMA) prediction method. The respective results indicate that the RNN models using historical traffic data and weather data show significant improvement compared to the ARMA model using only historical traffic data. Hypothesis 2 tests the prediction quality of LSTM and GRU compared to ML-based forecast models already in place in the field of traffic predictions, namely k-nearest neighbor (kNN), support vector regression (SVR), and neural networks (NN). In this context, the LSTM and GRU models using historical traffic data and weather data show significant improvement compared to the models kNN, SVR, and NN that also consider weather data. Despite the results presented in this work, there is still further potential for improvement. Thus, further research focusing on hyperparameter tuning of the RNN algorithms and the optimized selection of (additional) input variables with significant influence on travel times can contribute to further improvements of the forecast quality.

A deep-learning framework considering multiple motifs for traffic travel time prediction

How to accurately predict short-term traffic travel time (TTT) is an important problem in intelligent transportation systems. Traffic data usually exhibit high non-linearities and complex patterns, and predicting TTT is a challenge. Most previous studies have used the topological adjacency of road networks to explore spatial correlations. However, a real road network contains higher-order connectivity patterns that have different statistical significance. The topology adjacency cannot reflect these higher-order connectivity patterns. To obtain topological adjacency and higher-order connection pattern information, a novel deep-learning framework (multi-motif graph convolutional recurrent neural networks) for TTT prediction is proposed. The accuracy of TTT prediction was improved with this model. There are two blocks in each unit of the model: (a) spatial blocks, which capture spatial pattern information by the multi-motif graph convolution network and motif graph embedding, and (b) temporal blocks, which capture temporal pattern information by the combination of a long short-term memory network and a fully connected layer. To prove the effectiveness and accuracy of the prediction model, experiments were conducted on real-world TTT data sets.

Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus

The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus’s spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model’s performance.

“Home alone” no more: How does the internet of things (IoT) enhance travellers' subjective well-being

Although extensive research has recognized the importance of both IoT technologies and human well-being, the impact of Internet of Things (IoT) technologies on travellers' subjective well-being (SWB) in the information technology literature remains limited. This study aims to explore the factors and extent to which IoT technologies affect travellers' SWB during short-term trips. To this end, an integrated model was developed. The proposed model was empirically evaluated using survey data collected from a sample of 1028 British and 970 Chinese short-term travellers. A multigroup analysis has been conducted using Structural Equations Modelling (SEM) with Amos 26.0 software. The results reveal TTF has a positive indirect effect on consumers' SWB via perceived benefits in both groups and a positive direct impact in the case of the British group. However, no significant relationship was observed between TTF and travellers' SWB in the case of the Chinese group. Theoretically, this study first empirically explores and advances our knowledge of how the SWB of short-term travellers is affected by the use of IoT technologies across countries. From a practical viewpoint, this study may guide marketers toward a better understanding of how to use IoT technologies to enhance their consumers' subjective well-being in different countries.