Search Results (95)

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

The quality of water and its impact on consumers’ health has been studied extensively, along with concerns surrounding the use of polyethylene terephthalate (PET) packaging. This research aimed to analyze consumer behavior regarding water consumption patterns, with a focus on sustainability, packaging preferences, and perceptions of drinking water quality. Two surveys, conducted in 2019 and 2024, used a 23-question structured questionnaire to assess the public willingness to prevent water waste in the context of the circular economy. The surveys addressed consumer identification, drinking water preferences, the awareness of alternative consumption methods, and the openness to sustainable solutions such as water filters. Key quantitative findings showed a 10.4% increase in the amount of bottled water purchased in a single trip and a 12.1% rise in the frequency of weekly purchases, particularly among women and younger consumers. Simultaneously, a 4% increase in the preference for PET packaging over glass raised concerns about environmental sustainability, while the preference for tap water dropped by 5%, correlated with a 4.2% decline in the perceived tap water quality. The brand preference also shifted notably, with Aqua Carpatica rising to 38% and Borsec declining from 37% to 16%, reflecting the influence of purity-focused marketing. The novelty of the approach lay in identifying emerging trends related to sustainability, health, and circular economy principles. A comparative analysis of Romanian citizens’ responses over time highlighted changing perceptions of water use and waste reduction. To support the analysis, 13 statistical indicators were evaluated, a Spearman correlation test was applied to 13 criteria, descriptive statistics were computed, and a t-test was conducted across eight hypotheses. Full article

With increasing concerns over climate change and air pollution, sustainable transportation has become a critical component of modern city planning. Bike-sharing systems have emerged as an eco-friendly alternative to motorized transport, contributing to energy conservation and emission reduction. To elaborate on bike-sharing’s contribution to urban sustainable development, this study conducts a quantitative analysis of its environmental benefits through a case study of the Bluebikes program in the Boston area, using a longitudinal dataset of 20.07 million bike trips from January 2015 to December 2024, with data between January 2020 and December 2021 excluded. A combination of Scheiner’s model and Multinomial Logit model was adopted to evaluate the substitution of Bluebikes trips, an optimized Seasonal Autoregressive Integrated Moving Average (SARIMA) model was employed to predict future usage, while energy savings were calculated by estimating reductions in gasoline and diesel consumption. The findings reveal that during the analyzed period, Bluebikes trips saved 2616.44 tons of oil equivalent and reduced CO₂ and NO_X emissions by 7614.96 and 16.43 tons, respectively. Furthermore, based on the historical trends, it is forecasted that the Bluebikes program will annually save an average of 723.66 tons of oil equivalent and decrease CO₂ and NO_X emissions by 2422.65 and 4.52 tons between 2025 and 2027. The results highlight the substantial environmental impact of Bluebikes and support policies that encourage their usage. Full article

Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals. 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 limited availability of mobility data makes it challenging to model demand, especially its spatiotemporal variations. Simultaneously, traditional transport modeling tools often rely on less disaggregated approaches, leading to gaps in understanding. To overcome these limitations, this study introduces the spectral clustering method to uncover major demand patterns considering various transport modes. It focuses on Athens, Greece, and utilizes a set of 1347 reported trips. The study reveals six distinct trip clusters. The first group, “an evening stroll nearby”, captures short distance tours typically undertaken by walking. The second cluster, “my work is nearby but I use my car” highlights a significant trend where individuals with short commutes, less than 6 km, predominantly use private cars. The third cluster, “commuting by metro”, features long-distance trips primarily for work. The fourth cluster reveals long-distance work-related trips with private cars, favored by active residents with high income. The fifth pattern, “trips of young people”, involves midnight recreational and moderate-distance morning trips for education, with an increased usage of public transport. The sixth cluster concerns short distance tours for various activities. The findings indicate that the second cluster’s high reliance on private cars for short trips is problematic. Reducing this reliance should be a priority for policymakers. Full article

This article presents a review of the problems and solutions concerning the distance protection of transmission lines connected to inverter-based resources (IBRs). After a brief description of IBRs and distance protection, the reported problems are classified based on their causes and effects. The causes are related to IBR behavior, and the effects are related to distance protection. The effects are classified as overall effects (observable wrong trips or an observable lack of activation of distance functions) and specific effects (related to the particular internal relay elements that failed, causing the observable overall effects). Furthermore, special attention is paid to clearly describe the research literature from relay manufacturers, since it should be closer to the current trends related to real-life problems and solutions. The causes and specific effects particularly mentioned in the reviewed literature are summarized in corresponding tables, including information about those papers where such causes and effects cannot be clearly identified. Full article

Tourism and recreation managers rely on spatial-temporal data to measure visitors’ behavior for gauging carrying capacity and sustainable management. Location-based service (LBS) data, which passively record location data based on mobile devices, may enable managers to measure behaviors while overcoming constraints in labor, logistics, and cost associated with in-person data collection. However, further validation of LBS data at more refined spatial and temporal scales within tourism attractions is needed. We compared observations of salient spatial–temporal measures from a stratified sample of onsite visitors’ GPS traces in a popular U.S. National Park during peak season over two years with a sample of visitors’ traces collected during the same period by a third-party LBS data provider. We described trip characteristics and behaviors within 34 points of interest (POIs) and then pre-processed both datasets into weighted, directed networks that treated POIs as nodes and flow between POIs as edges. Both datasets reported similar proportions of day-use visitors (~79%) and had moderate-to-strong correlations across networks depicting visitor flow (r = 0.72–0.85, p < 0.001). However, relative to the onsite data, LBS data underestimated the number of POIs the visitors stopped by and differed in its rank of popular POIs, underestimating the length of time visitors spent in POIs (z = 1, p ≤ 0.001) and overestimating visitation to the most popular POIs (z = 180, p = 0.044). Our findings suggest that LBS data may be helpful for identifying trends or tracking tourist movement in aggregate and at crude spatial and temporal scales, but they are too sparse and noisy to reliably measure exact movement patterns, visitation rates, and stay time within attractions. Full article

In this study, the spatial–temporal characteristics of AOD in the Yellow River Basin (YRB) and urban agglomerations within the basin were analyzed at a 1 km scale from 2011 to 2020 based on the MCD19A2 AOD dataset. This study shows the following: (1) From 2011 to 2020, the AOD value of the YRB showed a declining trend, with 96.011% of the zones experiencing a decrease in AOD. The spatial distribution of AOD displayed a pattern of high in the east, low in the west, high in the south, and low in the north. The rate of decline showed a distribution pattern of fast in the southeast and slow in the northwest. (2) The AOD in the YRB showed similar characteristics in different seasons: the south and east were consistently higher than the north and west. The seasonal AOD values in the YRB showed the following pattern: summer > spring > autumn > winter. The AOD values of urban agglomeration were basically larger in spring and summer. (3) The SDE and mean center of the yearly AOD were located in the southeast and Shanxi Province, with the movement from southeast to northwest. It can be divided into three stages based on the movement trajectory: northeast–southwest round-trip movement (2011–2014), one-way movement to the northwest (2014–2018), and southeast–northwest round-trip movement (2018–2020). Full article

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users. Full article

The COVID-19 pandemic profoundly affected people’s travel behavior and travel desires, particularly regarding trips to prominent destinations. This study explores the pandemic’s impact on travel behavior and online search patterns for 12 landmarks across six continents, utilizing data from three online platforms, i.e., Google Trends, X, and Tripadvisor. By comparing visitation and search behavior before (2019) and during (2020/2021) the pandemic, the study uncovers varying effects on the spatial separation between user location and landmarks. Google Trends data indicated a decline in online searches for nearby landmarks during the pandemic, while data from X showed an increased interest in more distant sites. Conversely, Tripadvisor reviews reflected a decrease in the distance between users’ typical review areas and visited landmarks, underscoring the effects of international travel restrictions on long distance travel. Although the primary focus of this study concerns the years most affected by COVID-19, it will also analyze Tripadvisor data from 2022 to provide valuable insights into the travel recovery beyond the pandemic. Full article

The asymmetric operation is a method that allows High and Extra-High Voltage (HV, EHV) power lines to function with one or two phases open. With the increasing share of Renewable Energy Sources (RES) in National Power Systems (NPS), they are becoming more volatile and less reliable due to decreasing inertia and other issues related to the integration and exploitation of the Inverter-Based Resources (IBR) (decreasing short-circuit ratio, different types of interactions, etc.). On the other hand, phase-to-ground faults are a common cause of tripping off power lines which affects the overall reliability of the power system. Thus, for power systems experiencing a decreasing trend in reliability and robustness, the asymmetrical operation of the power lines may enhance them. In this way, this article reviews the state of the art and new developments in the academic landscape regarding asymmetrical operation. The review is not, however, limited to HV and EHV systems, so it examines cases of asymmetric operation in Low and Medium Voltages (LV, MV) as well. The challenges and opportunities that this unique mode of operation imposes on power networks are also presented, providing a fresh reference for researchers looking to enter this topic. 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 work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond. Full article

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis. Full article