Search Results (323)

Rural tourism is closely linked to a local’s tradition, identity, and cultural heritage. When staying in a specific tourist destination, a modern tourist expects a complete experience of the destination. Experience is necessary in rural tourist accommodation where guests can feel the local culture, nature, gastronomy, environment, and heritage. However, what about the interior design of the accommodation facility and the furniture design that will provide a rural and at the same time modern atmosphere? The paper aims to explore the traditional heritage, culture, indigenous elements, and ornamentation of rural artifacts of Istria, a region in Croatia, to propose a conceptual design of functional contemporary furniture for furnishing the living room in tourist accommodation. The furniture collection, with visual and artistic elements, surface treatment, construction, and selected sustainable materials and ornamentation, aims to brand the indigenous rural Istrian heritage, while at the same time combines an innovative contemporary expression. The research is divided into two stages: the first stage was conducted during field research using photography, observation, and interview methods, and the collected data from this stage served as inspiration for designing a furniture collection in the second stage. The second stage uses the cyclical method of the creative process to design new sustainable furniture concept, consisting of a table, stool, chest of drawers, and coffee table, which form a collection in the tourist interior environment. This furniture design model that uses original heritage to brand the rural environment and increase the attractiveness of accommodation in rural areas could be applied to other locations so tourists can fully experience the rural area they visit and achieve experiential, relaxed holidays and amenities, thus supporting the sustainable development of rural tourist destinations. The practical implications of this research have yet to be confirmed. It is desirable to investigate the impact and satisfaction of users in such designed interiors and confirm such a concept. This will require the engagement of architects and designers and hotel accommodation owners, as well as and the support of economic entities, local authorities, and the government, who need to systematically change the ways of branding rural values to achieve a holistic approach to the tourism offer. Full article

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety. Full article

The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based on their cultural backgrounds is still insufficiently addressed. Previous articles suggest that an individual’s cultural background plays a significant role in shaping tourist values and expectations. This study investigates how tourists’ cultural backgrounds, represented by their geographical regions of origin, impact their entertainment experiences, sentiments, and mobility patterns across the three countries. We gathered 387,010 TripAdvisor reviews and analyzed them using a combination of advanced text mining techniques and network analysis to map tourist mobility patterns. Comparing sentiments and behaviors across cultural backgrounds, this study found that entertainment preferences vary by origin. The network analysis reveals distinct exploration patterns: diverse and targeted exploration. Vietnam achieves the highest satisfaction across the cultural groups through balanced development, while Thailand’s integrated entertainment creates cultural divides, and Indonesia’s generates moderate satisfaction regardless of cultural background. This study contributes to understanding tourism dynamics in Southeast Asia through a data-driven, comparative analysis of tourist behaviors. The findings provide insights for destination management, marketing strategies, and policy development, highlighting the importance of tailoring tourism offerings to meet the diverse preferences of visitors from different global regions. Full article

Climate change is a major global issue because transportation is a major source of pollutants and greenhouse gases that affect human health and air quality. However, to effectively prioritize and fund mitigating actions, decision-makers lack scientific rigor and diagnoses with sufficient spatial resolution. Based on the Origin-Destination Matrix (ODM), this study suggests a methodology to measure and identify mobility emissions (CO₂, Nox, PM) at the neighborhood level with high spatial resolution. Testing of the methodology was performed in Valencia, Spain. Even though many studies calculate carbon footprint, few make use of precise geographic information and openly accessible data, and they frequently concentrate on entire cities rather than smaller areas. To determine all potential routes for each Origin-Destination (OD) trip, the process uses geostatistics to estimate daily trip activity data (kilometers traveled). The COPERT calculator methodology from the European Union is used to analyze these routes to calculate the total emissions and the distance traveled per neighborhood. Based on road infrastructure, the methodology determines which neighborhoods receive emissions and creates measures of equitable environmental responsibility. It also identifies short trips that might be replaced by cycling or walking, as well as possible improvements to public transportation. Full article

This study advances the inference of travel purposes for dockless bike-sharing users by integrating dockless bike-sharing and point of interest (POI) data, thereby enhancing traditional models. The methodology involves cleansing dockless bike-sharing datasets, identifying destination areas via users’ walking radii from their start and end points, and categorizing POI data to establish a correlation between trip purposes and POI types. The innovative GMOD model (gravity model considering origin and destination) is developed by modifying the basic gravity model parameters with the distribution of POI types and travel time. This refined approach significantly improves the accuracy of predicting travel purposes, surpassing standard gravity models. Particularly effective in identifying less frequent but critical purposes such as transfers, medical visits, and educational trips, the GMOD model demonstrates substantial improvements in these areas. The model’s efficacy in sample data tests highlights its potential as a valuable tool for urban transport analysis and in conducting comprehensive trip surveys. Full article

The Russia–Ukraine conflict has influenced global LNG shipping patterns; nevertheless, current research about its effects on the nodes and local regions of the LNG shipping network remains insufficient. This study employs a series of network metrics and a robustness evaluation model to examine the evolution in the structure and functionality of the LNG shipping network amid the Russia–Ukraine conflict, integrating LNG vessel origin–destination data from 2021 to 2023 to analyze the network’s structure and robustness. The research indicated that: (1) The alteration in trade relations instigated by the Russia–Ukraine conflict modified global LNG flows, resulting in a fragmented overall network structure and diminished transportation efficiency. The Russia–Ukraine conflict catalyzed the enhancement of European ports, leading to a substantial rise in the significance of premier European ports within the LNG transport network. Significant export ports, such as Ras Laffan, hold substantial importance within the network. (2) Among various assault techniques, degree-based intentional attacks inflict the greatest harm on the LNG shipping network. The robustness of the LNG shipping network declined following the Russia–Ukraine conflict, rendering it particularly susceptible in 2023. The findings indicate that the Russia–Ukraine conflict altered the structure of the LNG transportation network and diminished its robustness. The work holds substantial theoretical importance for examining the influence of geopolitical events on LNG transportation and for improving the maritime industry’s ability to navigate complicated circumstances. Full article

Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents’ actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization. Full article

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning. Full article

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer–Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin–destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures. Full article

Vehicle-to-grid (V2G) technology is emerging as an innovative paradigm for improving the electricity grid in terms of stabilization and demand response, through the integration of electric vehicles (EVs). A cornerstone in this field is the estimation of the aggregated available capacity (AAC) of EVs based on available data such as origin–destination mobility data, traffic and time of day. This paper considers a real case study, consisting of two aggregation points, identified in the city of Padua (Italy). As a result, this study presents a new method to identify potential applications of V2G by analyzing floating car data (FCD), which allows planners to infer the available AAC obtained from private vehicles. Specifically, the proposed method takes advantage of the opportunity provided by FCD to find private car users who may be interested in participating in V2G schemes, as telematics and location-based applications allow vehicles to be continuously tracked in time and space. Linear and nonlinear dynamic models with different input variables were developed to analyze their relevance for the estimation in one-step- and multiple-step-ahead prediction. The best results were obtained by using traffic data as exogenous input and nonlinear dynamic models implemented by multilayer perceptrons and long short-term memory (LSTM) networks. Both structures achieved an $R^2$ of 0.95 and 0.87 for the three-step-ahead AAC prediction in the two hubs considered, compared to the values of 0.88 and 0.72 obtained with the linear autoregressive model. In addition, the transferability of the obtained models from one aggregation point to another was analyzed to address the problem of data scarcity in these applications. In this case, the LSTM showed the best performance when the fine-tuning strategy was considered, achieving an $R^2$ of 0.80 and 0.89 for the two hubs considered. Full article

Travelers’ attention to high-quality human habitats is increasing, and the role of urban greenways in improving the quality of travelling spaces has also been appreciated. This research aims at making the weight calculation of suitability more scientific and reasonable, clustering the shared bicycle travelling OD points according to suitability, and analyzing the distribution of OD points. Taking Xiamen as an example, multiscale geographically weighted regression and entropy weight methods were used to calculate the weights of variables using multi-source big data. The clustering of origin-destination (OD) points for shared bicycle travel are identified using the DBSCAN clustering algorithm, which can provide accurate support for greenway planning and shared bicycle placement. The results show that the density of tourist attractions, POI entropy index, road density, and intermediate are four important factors affecting the suitability of greenways. The clustering results of the shared bicycle OD points show that the high-aggregation areas of origin and destination points are located in the northeast and southwest directions as well as west and east directions. This study provides a theoretical and modelling analysis reference for greenway planning and design. Full article

An important development task for the suburbs of smart cities is the transition from rigid and economically inefficient public transport to the flexible order-based service with autonomous vehicles. The article proposes a compact model with a minimal input data set to estimate the effective daily travel time (EDTT) of an average resident of a suburban area considering the availability of the first-mile autonomous vehicles (AVs). Our example case is the Järveküla residential area beyond the Tallinn city border. In the model, the transport times of the whole day are estimated on the basis of the forenoon outbound trips. The one-dimensional distance-based spatial model with 5 residential origin zones and 6 destination districts in the city is applied. A crucial simplification is the 3-parameter sub-model of the distribution of distances on the basis of the real mobility statistics. Effective travel times, optionally completed with psycho-physiological stress factors and psychologically perceived financial costs, are calculated for all distances and transportation modes using the characteristic speeds of each mode of transport. A sub-model of switching from 5 traditional transport modes to two AV-assisted modes is defined by an aggregated AV acceptance parameter ‘a’ based on resident surveys. The main output of the model is the EDTT, dependent on the value of the parameter a. Thanks to the compact and easily adjustable set of input data, the main values of the presented model are its generalizability, predictive ability, and transferability to other similar suburban use cases. Full article

This study analyzes inter-regional traffic patterns and network structures using origin–destination (OD) data. Block modeling, a method that clusters nodes performing similar roles within a network to identify functional regional structures, distinguishes passenger and freight patterns. Eigenvector centrality extracts central cities, while multiple regression analysis compares factors influencing flows in core areas. The findings reveal that (1) freight flows exhibit more active inter-regional movement than passenger flows, relying heavily on long-distance transport; (2) passenger hubs tend to be geographically central, whereas freight hubs are located in peripheral areas; and (3) passenger flows are shaped by regional characteristics, industrial structure, and infrastructure, while freight flows are influenced by regional characteristics, infrastructure, and land use patterns. Population density and industrial facilities significantly impact both flow types. This study provides a comprehensive understanding of the distinct characteristics of passenger and freight flows, bridging gaps in the existing research. Moreover, it offers practical insights for policymakers aiming to promote balanced development and sustainable regional growth, emphasizing the integration of underdeveloped areas into broader strategies to address disparities and foster connectivity. By combining advanced analytical methods, this study establishes a novel framework for enhancing regional planning and policy formulation. Full article

This study develops a methodological framework for measuring energy conservation and CO₂ emission reductions that considers both origins and destinations. The framework encompasses four key aspects: transportation, accommodation, cooking, and housing rehabilitation. Data were collected through a literature review, questionnaire surveys, and field measurement tracking. Compared to living in the origin, senior tourists from Nanchang visiting Zhongyuan Township in China for summer tourism can save 5.747 MJ of energy and reduce CO₂ emissions by 3.303 kg per capita per day. An in-depth analysis indicated that the research site could further enhance energy conservation and reduce CO₂ emissions by improving public transportation services, optimizing the energy structure of the destination, and diversifying the available recreational offerings. Depending on the characteristics of the destination and the primary origin, summer or winter tourism in various countries or regions can employ the methodological framework to evaluate energy conservation and CO₂ emission reductions after identifying specific parameters. The improved pathways identified through this research can serve as a checklist for other countries or regions aiming to explore energy conservation and CO₂-emission-reduction pathways for summer or winter tourism. Enhancing climate-driven tourism development may offer a new avenue for the tourism industry to contribute to carbon reduction targets. Full article

Eco-routing, as a key strategy for mitigating urban pollution, is gaining prominence due to the fact that minimizing travel time alone does not necessarily result in the lowest fuel consumption. This research focuses on the challenge of selecting environmentally friendly routes within an urban street network. Employing microsimulation modelling and a computer-generated mirror of a small traffic network, the study integrates real-world traffic patterns to enhance accuracy. The route selection process is informed by fuel consumption and emissions data from trajectory parameters obtained during simulation, utilizing the Comprehensive Modal Emission Model (CMEM) for emission estimation. A comprehensive analysis of specific origin–destination pairs was conducted to assess the methodology, with all vehicles adhering to routes recommended by Google Maps. The findings reveal a noteworthy disparity between microsimulation results and Google Maps recommendations for eco-friendly routes within the University of Pittsburgh Campus street network. This incongruence underscores the necessity for further investigations to validate the accuracy of Google Maps’ eco-route suggestions in urban settings. As urban areas increasingly grapple with pollution challenges, such research becomes pivotal for refining and optimizing eco-routing strategies to effectively contribute to sustainable urban mobility. Full article