Search Results (29)

The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities. Full article

The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. In the temporal dimension, we dynamically model temporal dependencies by incorporating multiple sources of time semantic features such as confirmed COVID-19 cases, weather conditions, and holidays. Additionally, we integrate a time attention mechanism to better capture variations over time. In the spatial dimension, we consider factors related to the addition or removal of stations and utilize spatial semantic features, such as urban points of interest and station locations, to construct dynamic multi-graphs. The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. Experimental results, obtained through comparisons with baseline models on the same dataset and conducting ablation studies, demonstrate the feasibility and effectiveness of the proposed model in predicting bike-sharing demand. Full article

Climate change and its fluctuations exert significant impacts on the tourism industry, particularly through the influence of high temperatures as typical meteorological and climatic factors on tourists’ travel intentions, spatial behavior preferences, and destination choices. This study employs China as a case study to investigate the effects of high-temperature weather on tourism and tourist travel. By analyzing news reports, conducting observations, and examining statistics, an exploratory analysis of tourism in China under high-temperature scenarios reveals several noteworthy findings. Firstly, tourists seeking relief from the summer heat exhibit a preference for short-distance trips and destinations rich in natural resources. Secondly, heat-escape tourism products have gradually transformed over time, evolving from mountain heat escapes in the 1980s to waterfront vacations in the 1990s, artificial water leisure in the 2000s, and ultimately culminating in the development of heat-escape cities in the 2010s. Additionally, this study examines interregional disparities in summer tourism climate amenity across China using the Holiday Climate Index (HCI), the Tourism Climate Index (TCI), and daily data from 775 weather stations. It also provides a summary of the spatiotemporal evolution from 1961 to 2020 within the context of climate change, revealing intriguing findings. Moreover, a case study of Shanghai Disneyland demonstrates the greater significance of the holiday system compared to temperature constraints. This study aims to examine the interaction between high temperatures and China’s tourism in the context of climate change, providing a scientific foundation for government agencies and tourism enterprises to develop effective policies and plans. Full article

Traffic congestion is a contemporary urban issue plaguing transportation planners, land developers, policy-makers, and citizens. While many studies have investigated the impact of built environments on traffic behavior in large metropolises on a regional scale, little attention has been paid to smaller urban areas, in China’s context, especially on a neighborhood level. This study investigates the spatial–temporal pattern of traffic congestion in a small-scale city, Xining, in China. By applying multivariate least-square regression analysis to social-sensing hyperlocal travel data, the results indicate that Xining is experiencing morning and evening traffic peaks on the weekdays and pre-weekends and only the evening peak during the weekends or holidays. The pre-weekend congestion is significantly worse than on a normal weekday, implying that stronger measures to consolidate traffic management should be implemented during this time. Educational land use and residential areas were found to contribute significantly to traffic congestion in Xining, and their combined effects tend to exacerbate the situation. The study furthers the understanding of traffic congestion in small urban areas, providing urban planners and policy-makers with new insights to formulate evidence-based strategies for mitigating traffic congestion. Full article

Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model. Full article

Climate and weather conditions at a destination influence the decision regarding what season and which location tourists might travel to. Assuming that the holiday experiences and satisfaction during their stay are influenced by weather and climate as well, this study investigates the question: does bad weather lead to a higher complaint rate among guests who booked vacation rentals? To answer this question, the complaint behavior and the weather parameters temperature, precipitation, wind speed and humidity are examined. The correlations between weather and complaining behavior are proven using the four-field coefficient. The chi-square four-field test is used to subsequently test independence. As a result, a correlation between the weather parameters and complaints cannot be proven based on the applied methods and used data. The four-field coefficient cannot confirm a correlation, as it is close to zero for all four weather parameters. For further investigations, more complaint data are required to obtain more significant results. Full article

A hotel is interested that the guest buys from it not only accommodation, but also catering services, preferably an all-inclusive option. However, many tourists choose only accommodation or accommodation with breakfast, and dinners and other things are purchased outside the place of accommodation. Therefore, it is important to know the eating behavior of tourists, and what hotels must do to make guests want to use food services at the place of accommodation. The purpose of this article is to show the reasons for not buying full meals at hotels during vacations by the inhabitants of Poland. The study used the diagnostic survey method with the help of the direct survey technique. A proprietary survey questionnaire was developed. The direct survey was conducted among 3071 tourists across the country. The study was conducted in 2019–2020. For data analysis, a discriminant function was chosen to examine the differences between groups based on a set of selected independent variables. When buying tourist holidays in travel agencies, 32.40% of Poles bought the all-inclusive option, 33.15% bought breakfast and dinner, 12.47% bought breakfast only, while 21.98%, bought accommodation without any food. For tourists who did not buy any meals at the hotel, the most important factors for eating out were mainly unwillingness to adapt to the hours of serving meals at the place of accommodation, and the desire to control the quality of raw materials needed for preparation of individual dishes. Among hotel guests who only had breakfast at the hotel, the main reasons for eating lunch and dinner outside of the hotel were the desire to try local dishes in regional restaurants, to get to know different restaurants, and to eat meals made entirely of ecological materials. A big barrier to buying meals in a hotel was the lack of offering dietetic dishes or their too high price. Older people dined out because of the lack of dietary dishes or their too high price and because they look for restaurants that serve meals prepared from ecological ingredients. Younger people, on the other hand, did not dine at the hotel because they did not want the hours of serving meals at the hotel to limit their sightseeing in the city and surroundings. Full article

Traveling during off-peak season can mean cheaper flights, cheaper hotels, and the chance to see a destination at a less frenetic time of year. To alleviate the congestion of roads and tourist attractions, a better demand management plan is needed to guide tourists to avoid travel during holidays. This study takes holiday tourists’ peak travel avoidance behavior as the research object, and a Nested Logit (NL) model of travel time and destination joint decisions was established based on Utility Maximization Theory. Model calibration and elastic analysis were carried out using Revealed Preference/Stated Preference (RP/SP) survey data. Results show that tourist attributes such as the number of tourists traveling together, travel companion, duration of the visit, the number of previous visits, tourism motivation, type of tourist attraction, quality grade of tourist attraction, and degree of congestion significantly influence destination decisions. Travel scope, travel duration, age, and other factors significantly influence travel time decisions. The traffic congestion around tourist attractions, holiday admission ticket prices, and non-holiday admission ticket prices significantly influence travel time and destination decisions. Holiday admission ticket price increases have a strong impact on the decision to change the travel destination, while non-holiday admission ticket discounts have a weak impact on travel time decision behavior. The findings of this study offer a theoretical basis for holiday travel management and tourism management. It is practical and significant to reasonably guide tourists to travel during the off-peak season and to understand the travel needs and characteristics of holiday tourists, thus adjusting the distribution of holiday tourist flow. Full article

Greenway is an important linear public space that meets the diverse needs of the public. With the increasing popularity of greenway construction, the study of different greenway usage behavior in different socio-economic areas is of great value to the detailed design and construction of greenway in the future. Using the theory of environment-behavior studies (EBS), this study selected representative urban greenways and suburban greenways in Beijing, China, and conducted a questionnaire survey. Descriptive statistics and the chi-squared test are used to quantitatively analyze and summarize the behavior of greenway users. It is found that user gender, educational level, and residence (i.e., permanent resident or visitor), as well as season of use, are highly similar for urban greenways and suburban greenways in Beijing. However, due to a close relationship with urban location, modified by temporal, spatial, and personal factors, different behavioral characteristics are evident as follows: (1) Urban greenways are most closely related to daily life, work and education of urban residents, with short travel distances, short single use time, high frequency of use, high social and cultural value, wide distribution of age groups and wide distribution of time periods of use. (2) Suburban greenways are an important choice for residents’ outdoor activities on weekends and holidays. It is mainly used for ecological protection and sightseeing, supplemented by sports and fitness functions. It has the characteristics of low use frequency, high income level, wide distribution of time and distance, mainly used by young and middle-aged people, and used for a single time of more than 1 hour. Natural scenery along the trail is the most important attraction factor, and waterfront space and walking space are the main use behavior characteristics. Full article

Greenspaces, including parks, provide various socio-ecological benefits such as for aesthetics, temperature remediation, biodiversity conservation, and outdoor recreation. The health benefits of urban greenspaces have received particular attention since the onset of the COVID-19 pandemic, which has triggered various movement restrictions and lifestyle changes, including regarding the frequency of people’s visits to greenspaces. Using mobile-tracking GPS data of Kanazawa citizens, we explored how citizens’ behaviors with respect to outings changed before and during Japan’s declaration of a COVID-19 state of emergency (April–May 2020). We also examined citizens’ greenspace visits in relation to their travel distance from home. We found that Kanazawa citizens avoided going out during the pandemic, with a decrease in the number, time, and distance of outings. As for the means of transportation, the percentage of outings by foot increased on both weekdays and holidays. While citizens refrained from going out, the percentage change of the percentage in large greenspace visits increased very slightly in 2020. As for greenspace visitation in 2020 compared to 2019, we found that citizens generally visited greenspaces closer to their homes, actually increasing visitation of nearby (within 1000 m) greenspaces. This study of how outing behaviors and greenspace use by Kanazawa citizens have changed underscores the value of nearby greenspaces for physical and mental health during movement restrictions under the pandemic. Full article

After the first wave of the COVID-19 outbreak, many tourist destinations promoted a safe, COVID-free image to attract tourists. The main purpose of this paper is to examine and analyze the effect that the image of a place as a safe tourist destination (STD)—in our case, Greece—can have on the decision-making processes of tourists who were willing to take summer holidays in 2020 amid the COVID-19 pandemic. We examined the relationships between destination safety perceptions, trust, attractive attributes of destinations, travel intentions, and health-protective behavior for domestic and inbound tourists from Spain. This study confirms differences in destination safety perceptions among domestic and inbound tourists from countries that have suffered significant negative impacts due to the novel coronavirus. Full article

In this study, call detail records (CDR), covering Budapest, Hungary, are processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is introduced to describe the behavior of a group of subscribers. It is defined as the time when the mobile phone activity of a group rises in the morning. Its counterpart is the time when the activity falls in the evening. Inhabitant and area-based aggregation are also presented. The former is to consider the people who live in an area, while the latter uses the transit activity in an area to describe the behavior of a part of the city. The opening hours of the malls and the nightlife of the party district are used to demonstrate this application as real-life examples. The proposed approach is also used to estimate the working hours of the workplaces. The findings are in a good agreement with the practice in Hungary, and also support the workplace detection method. A negative correlation is found between the wake-up time and mobility indicators (entropy, radius of gyration): on workdays, people wake up earlier and travel more, while on holidays, it is quite the contrary. The wake-up time is evaluated in different socioeconomic classes, using housing prices and mobile phones prices, as well. It is found that lower socioeconomic groups tend to wake up earlier. Full article

This paper aims to analyze the external and objective barriers of the digital difference between being at home and being on holiday, and the intrinsic and subjective inhibitors to remaining online once at a destination. In this study, the literature is thoroughly reviewed, going beyond the traditional economic and technological explanations, along with those related to skill, to consider those rooted in well-being and psychology. Hence, a more integrative and exhaustive framework deals with how tourists approach their perceived hazardous and oversaturating digital environment. Finally, the role played by sociodemographics is studied by profiling those who are predisposed toward disconnecting in order to preserve their wellness. In total, 346 tourists were surveyed at random, with proportional stratification, on the island of Gran Canaria. The measuring instrument comprised a questionnaire whose scales gathered information about more than eighteen devices, twenty-eight social media platforms, and sixteen device and social media barriers. The obtained evidence demonstrates how crucial “detox” motivations are when trying to elucidate the differences in digital behavior between their home and holiday destination. Similarly, the evidence highlights that while gender, age, nationality, and income are associated with these differences, education is not. This study pioneers an analysis of the detox barrier regarding staying connected while on holiday and provides insight into how this intrinsic and subjective inhibitor interacts with other external hindrances to people’s health, both where they live and where they travel. Full article

The use of electric vehicles (EVs) to provide different grid services is becoming possible due to the increased penetration levels, mileage efficiencies, and useable battery sizes of EVs. One such application is providing reliability-as-a-service (RaaS) during short-term power outages. Instead of using a dedicated backup power source, EVs can be contracted to provide RaaS, which is an environmentally friendly solution with benefits for both building owners and EV owners. However, the presence of EVs at a particular location during different hours of the day and the availability of energy from EVs is uncertain. Therefore, in this study, a suitability analysis is performed concerning the use of EVs to provide RaaS for different types of buildings. First, the National Household Travel Survey (NHTS) survey data are used to estimate driver behavior, such as arrival/departure times, daily mileage, and traveling duration. Then, the usable battery size and mileage efficiency of EVs is extracted from the database of commercially available EVs. Based on these parameters, the daily energy consumption and available energy of EVs to provide RaaS are estimated. A suitability analysis is conducted for residential, commercial/industrial, and mixed buildings for both weekdays and holidays. The participation ratio of EV owners is varied between 10 and 90%, and nine cases are simulated for commercial/industrial buildings and multi-unit residential buildings. Similarly, the ratio of home-based EVs is varied between 5 and 50%, and 10 cases are tested for mixed buildings. The analysis shows that mixed buildings are the most suitable, while commercial/industrial buildings are the least suitable for using EVs to provide RaaS. To this end, an index is proposed to analyze and determine the desired ratio of EVs to be contracted from homes and workplaces for mixed buildings. Finally, the impact of EV fleet size on the available energy for RaaS is also analyzed. Full article

Generation Z has been online since the beginning, the online space is an integral part of their lives and personalities, and they make up about 30% of the world’s population. It is claimed that this youngest cohort is already the most numerous generation on the Earth. The most important holiday parameters for them are price and location. They want to explore new places and be active while abroad. The study examines the impact of safety concerns on changes in travel behavior during the COVID-19 pandemic. We focused on members of Generation Z who study the Tourism and the Recreation and Leisure Studies programs, so these students have a positive attitude towards traveling. Data were collected via internal university systems at two periods of time connected to different stages of the pandemic outbreak. The sample was chosen randomly. The sample of Period 1 (n = 150) was composed in 2020, after the lifting of restrictions at the end of the first wave of the COVID-19 pandemic in the Czech Republic. The sample of Period 2 (n = 126) was collected one year later, after the lifting of restrictions at the end of the third wave of the COVID-19 pandemic in the Czech Republic. Correspondence analysis was used for better understanding and representation. This is a unique research study on Generation Z in the Czech Republic and Central Europe. As a result of the contemporary demographic changes in the world, this generation will shape future travel demand. Hence, understanding these youngest travelers will be key to predicting how tourism trends could evolve in the next few years and how these could influence worldwide tourism. The respondents thought they would not change their travel habits in the next five years because of the pandemic. When Periods 1 and 2 were compared after one year of the pandemic, the respondents preferred individual trips to group trips and individual accommodation to group accommodation facilities. On the other hand, our findings revealed a significant increase in safety concerns related to changes in travel behavior when the above-mentioned periods were compared. The research contributes to mapping young people’s attitudes towards travel in the constrained and changing conditions resulting from the COVID-19 pandemic. The findings help analyze the consumer behavior of the target group. Full article