Search Results (45)

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

Despite a substantial volume of literature on the consequences of the recent pandemic, the relationship between psychological constructs that affect individual mindset and confidence, as well as travel intention, is missing. This study seeks to examine the travelers’ behavioral intentions and psychological constructs. The study was conducted among 398 Malaysian medical students. The study was conducted from the end of 2022 to the middle of 2023. The study followed PLS-SEM to estimate relationships between variables and predict dependent variables. The results revealed a negative correlation between travel risk, severity, travel barriers, and travel intention, while COVID-19 vaccination effectiveness and self-efficacy positively influenced travel intention. The present study reveals that individuals with higher levels of self-efficacy or confidence in overcoming obstacles and coping with the challenges of new circumstances exhibit a stronger intention to travel (ITT). This study contributes to understanding the cognitive process of individuals’ intentions to travel and the coping mechanisms during the post-pandemic. Utilizing the health belief model, this study validates how individual health behavior regarding perceived risk affects travel decisions or intentions. This study provides valuable insight into consumer behavior for decision-making in the aviation and tourism industries and for policymakers after the global health crisis. Full article

Background. Length of stay, operationalized here as nights per booking (NPB), is a first-order driver of yield, labor planning, and environmental pressure. The COVID-19 pandemic and the rise of long-stay remote workers (often labeled “slomads”, a slow-travel subset of digital nomads) plausibly altered stay-length distributions, yet national, booking-weighted evidence for the United States remains scarce. Purpose. This study quantifies COVID-19 pandemic-era and post-pandemic shifts in U.S. Airbnb stay lengths, and identifies whether higher averages reflect (i) more long stays or (ii) longer long stays. Methods. Using every U.S. Airbnb reservation created between 1 January 2019 and 31 December 2024 (collapsed to booking-count weights), the analysis combines: weighted descriptive statistics; parametric density fitting (Gamma, log-normal, Poisson–lognormal); weighted negative-binomial regression with month effects; a two-part (logit + NB) model for ≥28-night stays; and a monthly SARIMA$(0,1,1){(0,1,1)}_{12}$ with COVID-19 pandemic-phase indicators. Results. Mean NPB rose from 3.68 pre-COVID-19 to 4.36 during restrictions and then stabilized near 4.07 post-2021 (≈10% above 2019); the booking-weighted median shifted permanently from 2 to 3 nights. A two-parameter log-normal fits best by wide AIC/BIC margins, consistent with a heavy-tailed distribution. Negative-binomial estimates imply post-vaccine bookings are 6.5% shorter than restriction-era bookings, while pre-pandemic bookings are 16% shorter. In a two-part (threshold) model at 28 nights, the booking share of month-plus stays rose from 1.43% (pre) to 2.72% (restriction) and settled at 2.04% (post), whereas the conditional mean among long stays was in the mid-to-high 50 s (≈55–60 nights) and varied modestly across phases. Hence, a higher average NPB is driven primarily by a greater prevalence of month-plus bookings. A seasonal ARIMA model with pandemic-phase dummies improves fit over a dummy-free specification (likelihood-ratio = 8.39, df = 2, p = 0.015), indicating a structural level shift rather than higher-order dynamics. Contributions. The paper provides national-scale, booking-weighted evidence that U.S. short-term-rental stays became durably longer and more heavy-tailed after 2020, filling a gap in the tourism and revenue-management literature. Implications. Heavy-tailed pricing and inventory policies, and explicit regime indicators in forecasting, are recommended for practitioners; destination policy should reflect the larger month-plus segment. Full article

The coronavirus disease 2019 (COVID-19) pandemic has put a renewed focus on the value of vaccines in combatting potentially life-threatening diseases. The primary aim was to conduct a longitudinal study on the dispensing patterns of vaccines (from 2017 to 2021) in a section of the private healthcare sector in South Africa. A descriptive cross-sectional pharmacoepidemiological study on health insurance data covering 5 years was conducted. The study included all vaccines available on the South African market (childhood, adult, travel, and other vaccines). The study population consisted of 3.8 million individuals. The descriptive statistics were calculated. The vaccine-dispensing patterns were distinctly different in 2021 compared to the preceding four years. The COVID-19 vaccine was introduced in 2021 in South Africa. Although the total number of medical insurance scheme members stayed relatively constant, the number of vaccine claims increased approximately seven-fold in 2021 compared to the average for the preceding four years (2017 to 2020). The tetanus and pneumococcal vaccines were the most dispensed bacterial vaccines, whilst the influenza and COVID-19 vaccines were the most dispensed viral vaccines. COVID-19 vaccines accounted for 55.74% of all vaccines dispensed over the 5 years, and for 85.70% of the vaccines dispensed in 2021. An increase in the number of bacterial vaccines dispensed was observed towards the middle of 2020, which was attributed to the pneumococcal vaccine. Pneumococcal vaccines were administered during the COVID-19 pandemic to prevent morbidity and mortality from co-/secondary infections and superinfections. Similar ongoing studies on vaccine-dispensing patterns in the post-COVID-19 era are necessary, especially since the outbreak of various vaccine-preventable diseases has recently been observed. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article

In the context of macroeconomic recovery and accelerating digital transformation in the post-pandemic era, the hotel industry in China is undergoing profound structural changes. This research investigates the core competencies required for hotel managers to navigate these challenges. Data was collected via a quantitative survey involving a structured questionnaire, was conducted among hotel managers in Shanghai, China, resulting in 404 valid responses. Employing exploratory factor analysis using SPSS, this study identifies seven key competency dimensions encompassing 36 ranked items, including interpersonal communication, leadership, operational knowledge, human resource management, financial analysis, technology, and administrative management. The results show that economic recovery has brought new opportunities but also challenges to the hotel industry, and that managers must possess a diverse set of core competencies to adapt to the demanding new market changes. The novelty of this research lies in its empirical grounding and its focus on the intersection of digitalization and economic recovery within China’s hotel industry. It pioneers a dynamic strategic competency framework tailored to the evolving demands of the hotel industry during a period of economic volatility, providing empirical evidence and advice for optimizing the industry’s talent training systems. Simultaneously, it brings a new perspective for dealing with the recovery path for the hotel enterprises in other urban and travel destinations, aiming to promote industry sustainability and competitive advantages. Future research could extend the proposed framework by exploring its applicability across different cultural and economic contexts. Full article

Between 2019 and 2022, passenger volume on China’s urban rail transit system sharply declined due to strict COVID-19 control measures. On 8 January 2023, China implemented the “Class B infectious disease Class B management” policy, marking a significant shift towards a more relaxed approach to epidemic control. The main objective of this study is to evaluate the immediate and lasting effects of this policy on urban rail transit passenger volume. We used interrupted time series (ITS), combined with quasi-Poisson regression models and counterfactual analysis, to analyze monthly urban rail transit operation data covering the period from January 2021 to June 2024 for 42 cities. Our analysis shows that, relative to the expected trend without any intervention, monthly average passenger volume increased by approximately 101.34% after the policy’s implementation, with significant immediate effects observed in 41 cities and significant lasting effects observed in 33 cities. The study concludes that the “Class B infectious disease Class B management” policy has generally promoted the nationwide recovery of urban rail transit passenger volume, although with significant heterogeneity across cities. This result indicates that the reduction in travel restrictions and the restoration of public safety, resulting from the relaxation of COVID-19 prevention and control measures, contributed to the overall recovery of urban rail transit. This study not only provides innovative methodological insights but also offers valuable guidance on developing more effective urban planning strategies and urban rail transit operational measures in the post-pandemic era. Full article

This study examines luxury tourists’ behavior toward sustainability in the aftermath of the pandemic, with a particular focus on the Maldives, a destination renowned for both its luxury offerings and commitment to sustainable practices. A qualitative approach was employed, featuring structured interviews with one hundred European tourists who had visited the Maldives and stayed at luxury resorts, alongside semi-structured interviews with three tour operator managers who sell the destination. In addition, secondary data provided by the tour operator were used to support the analysis and triangulate the data for more robust findings. The results show a significant shift in consumer attitudes: luxury travelers have become increasingly sensitive to sustainability and the protection of the environment since the pandemic. The tourists in the sample reported heightened awareness of their personal environmental impact and a greater desire to mitigate it. Furthermore, participants expressed a willingness to pay a premium for environmentally sustainable services and products, demonstrating a clear preference for businesses that can substantiate their eco-friendly practices through credible certifications and accreditations. These findings highlight the growing importance of sustainability in shaping luxury consumer behavior in the post-pandemic era. Full article

The COVID-19 pandemic has had an unprecedented impact on public travel and has significantly altered people’s travel behavior. By comparing the travel behaviors of ride-hailing passengers before and after the pandemic (i.e., the pre-pandemic era and the post-pandemic era), it is possible to effectively identify the changes in their travel patterns and thus deeply understand the changes in individual travel habits in the post-pandemic era. Using data collected in Nanjing, China, this paper develops two ordered logit models to examine the impact mechanism of multi-dimensional influential factors on the trip frequency of ride-hailing users before and after the pandemic. The results reveal the following: (1) Before the pandemic, the increase in waiting time promoted the usage frequency of ride-hailing users, while in the post-pandemic era, the increase in waiting time reduces the travel frequency of ride-hailing users. (2) Before the pandemic, the increase in prices led to a decrease in ride-hailing users’ usage frequency, while it presents a push effect in the post-pandemic era. (3) In the post-pandemic era, ride-hailing users are more concerned about personal safety, avoiding conversing with the driver and touching objects in the car. Finally, some strategies, such as increasing awareness of personal information protection and security, optimizing waiting times, and improving the hygiene standards of ride-hailing, are proposed to assist transportation network companies in operating and restoring ride-hailing services in the post-pandemic era. Full article

The recreational waters of hotels are widely used by locals and travelers all around the world, and this impacts public health. In this regard, the WHO frequently offers recommendations—especially during the COVID-19 era—that operators and managers of public pools, hot tubs, or splash pads should continue to follow. This study aims to evaluate the microbiological results of COVID-19 sanitation protocols in recreational waters in hotels during the summer periods post-COVID-19 (2020–2022) and compare them with those pre-COVID-19 (2019). Of the samples tested during the pre-pandemic period, less than 1/5 were positive for total coliforms, common aerobic bacteria, and P. aeruginosa, while less than 3% of the samples tested were positive for E. coli. During the pandemic years, the percentages dropped by half for total coliforms, common aerobic bacteria, and E. coli, while only the percentage for P. aeruginosa remained the same. The relative risk (RR) for the presence of P. aeruginosa in recreational waters was greatly affected by chlorine levels, pH values, and water temperature. Our results elucidate the impact of sanitation protocols on the safety of recreational waters in hotels during public health crises. Moreover, the challenges operators face in maintaining long-term implementation of these protocols are highlighted, which in turn dictates the appropriate choice of preventive and applicable measures to ensure health and safety. Full article

Objective: To examine how the recent sharp rise in telemedicine has impacted trends in accessibility of breast reconstruction (BR). Patients and Methods: A retrospective study reviewed patients who underwent a total mastectomy at our institution from 1 August 2016 to 31 January 2022. By comparing cohorts before and during the widespread implementation of telemedicine, we assessed telehealth’s impact on healthcare accessibility, measured by distance from patients’ residences to our institution. Results: A total of 359 patients were included in this study. Of those, 176 received total mastectomy prior to the availability of telemedicine, and 183 in the subsequent period. There were similar baseline characteristics among patients undergoing mastectomy, including distance from place of residence to hospital (p = 0.67). The same proportion elected to receive BR between groups (p = 0.22). Those declining BR traveled similar distances as those electing the procedure, both before the era of widespread telemedicine adoption (40.3 and 35.6 miles, p = 0.56) and during the height of telemedicine use (22.3 and 61.3 miles, p = 0.26). When tracking follow-up care, significantly more patients during the pandemic pursued at least one follow-up visit with their original surgical team, indicative of the increased utilization of telehealth services. Conclusions: While the rate of BR remained unchanged during the pandemic, our findings reveal significant shifts in healthcare utilization, highly attributed to the surge in telehealth adoption. This suggests a transformative impact on breast cancer care, emphasizing the need for continued exploration of telemedicine’s role in enhancing accessibility and patient follow-up in the post-pandemic era. Full article

While high-speed rail (HSR) has achieved success in major cities in Europe and Asia, it is a new phenomenon in the US, and few studies on HSR in the US are available, especially from the users’ perspective. This study aims to fill the research gap by investigating the mode choice behavior in the Los Angeles and San Francisco corridor where HSR may soon become a feasible option. The impact of COVID-19 was also examined with regard to how people view modes of domestic travel and how their view may change. The geographic locations of travelers and the possible HSR characteristics in the US were also explored. Survey data of US travelers was collected on MTurk, which was analyzed using logistics regression and Two-Way MANOVA. The results indicated that convenience in transport, travel frequency, gender, mobility issues, income, and total travel time were determinants in the choice between HSR and air service, while travel frequency and total travel time were important in the choice between HSR and car transport. Most US travelers changed their views following COVID-19 in terms of domestic travel and exhibited a higher intention to travel by train and HSR. Geographic patterns were identified, such as people in the southern US were the most knowledgeable of HSR and had the greatest intention to use HSR, while people in the northeast exhibited the lowest intention. The findings indicate potential interest in HSR among US travelers, and offer much-needed empirical evidence for the potential success of HSR in the US. Full article

In this paper, to improve the operational service capability and attractiveness of the flex-route transit system, the real dynamic interaction scenario between passenger travel choice preference and system operation scheme in the post-pandemic era is described and quantified. The key technologies, operation mode, system framework, and interactive events required for dynamic interactive scheduling and route planning of flex-route transit are summarized. According to different choice preferences, the corresponding dynamic interaction scheduling strategies and route mixed integer programming model are proposed. An optimization scheme to improve the service capability of the system is introduced and analyzed. The computational results based on real-world cases show that the proposed strategy can better handle the relationship between requirements of transit system operation and requests of passengers without increasing operating costs, significantly improving the service performance of flex-route transit and the choice rate of passengers. We also find that the introduction of optimization schemes and the adjustment of passenger fares constitute a win-win strategy that benefits both passengers and transit operators. Full article

Background: Scholars have indicated differences in the attitudes of urban and non-urban populations, especially after the COVID-19 outbreak, which extend to their needs and expectations regarding rail transport development. The aim of this study is to enhance the quality of train services in the post-pandemic era, and multigroup analysis will be applied to achieve the difference in area context. Methods: The research data were collected from rail transport users throughout Thailand, consisting of 665 urban and 935 rural users. The questionnaires primarily focused on user expectations regarding rail service quality and travel conditions in the post-pandemic landscape using multigroup confirmatory factor analysis (MCFA). Results: The results unveiled significant variations in user trends and needs across different contexts and areas. In urban settings, there was a notably higher overall service expectation compared to rural areas. Specifically, urban users prioritized factors such as accessibility and service empathy, whereas rural rail users placed greater emphasis on staff quality and reasonable pricing. Conclusions: These findings furnish rail transport service agencies with valuable insights and guidance for comprehending their users’ needs. They can develop appropriate organizational strategies, service quality enhancements, and policy adjustments tailored to the unique demands of urban and rural areas in the post-pandemic era, thereby ensuring sustainability. Additionally, the methodology of multigroup analysis served as a significant scientific contribution; this showed that the statistical analysis of different area contexts in the study should not be ignored. Full article