Search Results (92)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

To ensure stability and transparency in the European logistics sector, in May 2017, the European Commission presented several proposals to change the regulation of the market—in particular, market access, driving and rest periods, and business trips. In the development of this package, several unfavourable decisions were made that go against Lithuanian transport companies, which will have a significant impact on the companies’ finances, as the frequent return of trucks will lead to additional fuel costs and is also in contradiction with the concept of green logistics. Thus, it is essential to study the Mobility Package’s pros and cons and compare researchers’ views. Accordingly, the subject of this article is the impact of the Mobility Package on Lithuanian logistics companies. This article employs various methods, including an analysis of the scientific literature and legislation, statistical data analysis, PEST analysis, and qualitative research based on expert interviews. The results allow us to identify that the content of the Mobility Package is driven by the goal of ensuring equivalent working conditions throughout the EU, which in this case is the most important object of the legal changes. Also, based on the results obtained, it can be stated that Lithuanian logistics companies that want to remain in the market have several solutions they can employ to achieve that goal, and to support their efforts, a competitiveness improvement model for Lithuanian logistics companies has been developed. Full article

This study investigates how pandemic-induced emotional disruption has reshaped sustainable consumer behavior in the digital age, with a focus on the continued influence of safety measures in the restaurant industry. As societies transition beyond COVID-19 restrictions, health-related anxieties persist, driving consumers to prioritize hygiene and risk reduction in their decision-making. Drawing on large-scale data from TripAdvisor and OpenTable, we analyze the effects of digitally communicated safety protocols on restaurant booking behavior across major U.S. cities. Our findings reveal that safety communication remains a salient factor in consumer choice, even after the acute phase of the pandemic. This effect is particularly pronounced in lower-tier restaurants, where visible digital safety signals help build trust and compensate for weaker brand equity. Conversely, in upscale establishments, where baseline hygiene standards are presumed, the marginal benefit of safety signaling is reduced. The study also identifies enduring patterns of emotional expression and anxiety in online reviews, indicating the long-term psychological imprint of the pandemic on consumer sentiment. By situating safety communication as both a psychological reassurance mechanism and a strategic digital marketing tool, this research contributes to the emerging discourse on sustainable marketing in post-crisis contexts. The results offer theoretical and managerial insights into how businesses can integrate health assurance into long-term brand strategies, reinforcing trust and resilience in digitally mediated, post-pandemic consumption environments. Full article

Telework emerged in the 1970s with the advent of Information and Communication Technologies (ICT) as a potential substitute for commuting trips and an answer to avoid congestion. While early studies supported this substitution effect, subsequent research has presented contradictory findings, with some studies demonstrating complementary effects and increased travel distances, while others show a reduction in travel or mixed results. These discrepancies may arise from methodological differences in study design, sampling, and modeling approaches. To analyze these factors, a systematic literature review complemented by three meta-analyses was developed. OLS and GLS-RE models were built to measure telework impacts on the number of trips (total and by purpose), commuting distance, and total distance traveled. Our research suggests that while telework reduces commuting and business trips, particularly for full-time teleworkers, it may increase commuting distances. Total distance traveled presents mixed results, heavily dependent on research design. By identifying these patterns, we outline methodological directions for future research, including improved sampling strategies, advanced modeling techniques, and rigorous control variable selection. Full article

The COVID-19 pandemic severely affected Japan’s tourism-related industries, leading to significant revenue losses in the accommodation, restaurant, and tourist facility sectors. Many businesses experienced difficult situations, resulting in closures and layoffs as a result of the prolonged decline in tourism demand. Focusing on the first half of the pandemic (2020–2021), this study analyzes the loss of sales and the influencing factors among tourism-related businesses in the Nishimikawa region of Aichi Prefecture. A questionnaire survey was conducted in November 2021, and changes in sales from April 2020 to September 2021 were estimated to assess the economic impact across different sectors. A quantitative analysis was also performed to examine the relationship between sales and the state of emergency, the domestic travel subsidy program Go To Travel campaign, and business attributes. The results indicate that COVID-19 severely impacted business sales, especially in the accommodation and food service sectors, while the tourism facility and retail sectors were less affected. In the Nishimikawa region, popular for day trips from nearby areas, the Go To Travel campaign had a limited effect, highlighting the need for region-specific support measures. Full article

In the digital era, user-generated online reviews serve as a valuable resource for understanding visitor experiences in cultural institutions. This study analyses sentiments and thematic trends in TripAdvisor reviews of mountain museums, using Latent Dirichlet Allocation topic modelling and sentiment analysis. A dataset of 2157 reviews from ten museums was classified into local and non-local perspectives, revealing significant differences in visitor expectations. Findings indicate that local visitors prioritize historical authenticity and educational value, whereas non-local visitors emphasize aesthetic appeal, interactivity, and cultural immersion. Sentiment analysis highlights generally positive perceptions, with business travellers and groups of friends reporting the highest satisfaction levels. Comparative analysis across visitor types reveals distinct engagement patterns, with families valuing child-friendly exhibits, couples seeking cultural enrichment, and solo travellers focusing on intellectual depth. These insights inform strategic recommendations for museum management, including multilingual content, interactive elements, and guided tours dedicated to specific visitor profiles. Despite limitations related to lack of real-time feedback, this research demonstrates the potential of sentiment analysis in enhancing museum experiences. Future studies should integrate multimodal analysis and real-time tracking to further refine visitor experience evaluation. Full article

The objective of this research is to examine the role of intellectual property (IP) in fostering business development, particularly focusing on patent management in Ecuador and its alignment with international standards. The study employs a comparative analysis of Ecuadorian legislation against the framework established by the World Intellectual Property Organization (WIPO) to identify challenges and opportunities within the national IP system. Key methods include reviewing existing legal texts, interviewing stakeholders, and analyzing patent registration processes. The findings indicate that while Ecuador has made significant strides in harmonizing its IP laws with international treaties, such as the Patent Cooperation Treaty (PCT) and the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), considerable barriers remain, particularly related to bureaucratic inefficiencies and a lack of technical resources in key institutions like the National Service of Intellectual Rights (SENADI). The conclusions highlight the need for enhanced efficiency and implementation of IP regulations to stimulate sustained innovation growth, attract national and foreign investments, and, ultimately, strengthen Ecuador’s competitiveness in a global economy. This research contributes to the understanding of how effective IP management can serve as a vital tool for economic development and innovation. Full article

The tourism sector thrives on a comprehensive understanding of the factors that motivate individuals to explore new destinations. Identifying the push and pull factors that drive travel decisions is essential for analyzing tourist behavior and recognizing the external constraints that tourism enterprises and destinations must consider. Adopting a sustainable approach to these motivational forces underscores the need to balance tourism growth with the preservation of destinations, the well-being of local communities, and responsible travel practices. Push and pull factors in tourism are inherently linked to the emotional states that travelers experience throughout the decision-making process, from the initial intention to travel to the post-trip evaluation. The sector prospers by understanding the reasons that inspire individuals to discover new places. Determining these motivational factors is crucial for comprehending tourist behavior and addressing the external limitations that tourism businesses and destinations must navigate. A sustainability-focused approach highlights the significance of aligning tourism growth with destination preservation and community well-being, ensuring a responsible and enduring tourism model. This study aims to examine the impact of positive and negative emotions on push and pull motivational factors across different phases of the COVID-19 pandemic, adopting a sustainability perspective. The research was structured into four empirical studies: (i) pre-pandemic phase, involving a sample of 508 tourists; (ii) pandemic phase, with data collected from 507 participants; (iii) post-pandemic phase, comprising 488 respondents; (iv) comparative analysis, assessing variations across the three periods. The results indicate that emotional states exert a significant influence on push and pull motivational factors, with variations observed depending on the period of data collection: before, during, and after the COVID-19 pandemic. However, while emotions exhibited fluctuations across the three phases, push and pull factors demonstrated relative stability over time. These findings emphasize the critical role of emotional experiences in shaping travel motivations, highlighting the interplay between psychological drivers and destination attributes. This understanding is essential for tourism businesses and policymakers to develop strategies that align with evolving traveler expectations while promoting sustainable and responsible tourism practices. Full article

In recent years, the growing number of Online Travel Review (OTR) platforms and advances in social media and search engine technologies have led to a new way of accessing information for tourists, placing projected Tourist Destination Image (TDI) and electronic Word of Mouth (eWoM) at the heart of travel decision-making. This research introduces a big data-driven approach to analyzing and measuring the perceived and conveyed TDI in OTRs concerning the reflected perceptive, spatial, and affective dimensions of search results. To test this approach, a massive metadata analysis of search engine was conducted on approximately 2700 reviews from TripAdvisor users for the category “Attractions” of the city of Kastoria, Greece. Using artificial intelligence, an analysis of the photos accompanying user comments on TripAdvisor was performed. Based on the results, we created five themes for the image narratives, depending on the focus of interest (monument, activity, self, other person, and unknown) in which the content was categorized. The results obtained allow us to extract information that can be used in business intelligence applications. Full article

Global corporations frequently grapple with a dilemma between fulfilling business needs and adhering to travel policies to mitigate excessive fare expenditures. This research examines the multifaceted nature of business travel, delving into its key characteristics and the inherent complexities faced by management in formulating effective policies. An optimal travel policy must both be practical to implement and contribute to budget optimization. The specific requirements of each company necessitate tailored policies; for instance, a manufacturing company with scheduled trips demands a distinct policy, unlike a consulting firm with unplanned travel. This study proposes a modified regression decision tree machine learning algorithm to incorporate the unique features of corporate travel policies. Our algorithm is designed to self-adjust based on the specific data of each individual company. The authors implement the proposed approach using travel data from a real-world company and conduct simulations in various scenarios, comparing the results with the industry standard. This research offers a machine-learning-based approach to determining the optimal advance booking policy for corporate travel. Full article

Travelers’ perceptions of hotels and their aspects have been the focus of much research and are often studied by analyzing consumers’ online reviews. Yet, little attention has been given to the effect of the trip mode, i.e., whether the person travels alone or with others, on travelers’ preferences as sentiment. Here, we study the influence of the trip mode and purpose using a mixed-methods approach. We conducted a user study to evaluate the perceptions of reviews across trip modes and found that star ratings do not consistently capture the sentiment in text reviews; on average, solo travelers’ text reviews are perceived as more negative than the star ratings they assigned, whether they travel for business or pleasure. We then analyzed over 137,000 reviews from TripAdvisor and Venere and found that a co-occurrence network approach naturally divides the text of reviews into hotel aspects. We used this result to measure the importance of hotel aspects across various traveler modes and purposes and identified significant differences in their preferences. These findings underscore the need for personalized marketing and services, highlighting the role of trip mode in shaping online review sentiment and traveler satisfaction. Full article

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions. Full article

This paper analyses the commuting frequencies and modal choices of travellers in Melbourne, using a dataset reflecting travel behaviour before and after COVID-19. A factor analysis of 63 latent variables identified seven key factors, which were used in cluster analysis to examine the relationships between latent constructs, land use, and socio-demographic variables, as well as commuting behaviours. The analysis categorised white-collar employees into four groups based on their remote work engagement, with socio-demographics and industry type as key factors. The analysis shows that female clerical and administrative workers who worked from home during the pandemic are now returning to the office, raising gender equality concerns within society. Meanwhile, the education and training sector mandates office attendance despite the feasibility of remote work, as universities prioritise in-person attendance to attract more international students, impacting societal norms around telecommuting. The analysis revealed that saving on commute costs, reducing travel time, and spending more time with family are the among the primary factors influencing travel behaviour among white-collar employee’s post-pandemic. The study found that the decrease in public transport trips is associated with increased telecommuting rather than service dissatisfaction, especially among Central Business District (CBD) employees who still rely on public transport. This trend suggests that the CBD sector’s growing acceptance of remote work is reducing daily commutes, which puts additional pressure on public transport providers to sustain and improve their services. A decline in service quality could further reduce ridership, highlighting the need for consistent, high-quality public transport. Furthermore, the study found that increased telecommuting is likely to reduce car trips in the future, especially among healthcare and social workers who prefer driving due to public transport’s unreliability for their demanding schedules. By examining variables like the advantages and disadvantages of working from home, convenience, accessibility, and the efficiency of public transport, this study enhances the understanding of transport behaviour and underscores the need to improve public transport reliability to support sustainable cities as remote work grows. Full article

This study uses an input–output model to assess the economic impact of the 2018 Mile High 420 Festival on the Colorado economy. A comprehensive assessment was conducted to determine the economic impact of the Mile High 420 Festival, which included analyzing the direct, indirect, and induced effects of festival spending. The study involved 233 respondents whose primary motive was to attend the 420 Festival. Using IMPLAN, the study’s data were analyzed to determine the economic activity generated by visitor activity. On average, each respondent spent USD 2013 during their trip to Colorado. The largest expense per person was on cannabis-related activities and shopping, followed by lodging and food and drink. When these expenses were multiplied by the number of visitors (25,650), the total spending amounted to USD 51.7 million. When indirect and induced spending was included, the 2018 Mile High 420 Festival generated a total economic impact of over USD 95 million for Colorado. It contributed to the creation of 787 jobs in the region. No study has been conducted on the economic impact of a cannabis-themed festival in the current tourism literature. Therefore, this study contributes to filling this gap by developing literature on the impact of cannabis tourism and its implications for host communities or states, especially for state policymakers and business professionals. The results of this study are expected to serve as a reliable benchmark for subsequent economic impact analyses and comparisons with other industries. Full article