Travel Plans Research Articles

Adaptive behavior of intercity travelers within urban agglomeration in response to adverse weather: Accounting for multilayer unobserved heterogeneity

Travelers often change their behavior in reaction to adverse weather conditions. This paper seeks to conduct an empirical evaluation of both ordered and unordered discrete outcome frameworks for examining the adaptive behavior decisions of intercity travelers and investigate the varying effects of explanatory factors on different alternatives of adaptive behavior during adverse weather. The data from 754 respondents in the Beijing-Tianjin-Hebei urban agglomeration, China are collected by a two-phase survey instrument. To capture the unobserved heterogeneity more effectively, this paper develops three advanced models to investigate the variations in intercity travel behavior during adverse weather. Four alternative adaptive behaviors are determined as outcome variables: maintaining original travel plans, changing only the intercity mode, changing the departure date, and canceling the trip, while potential influencing factors including adverse weather conditions, trip-related characteristics, individual attributes and urban agglomeration attributes are statistically assessed. The results indicate that the unordered models consistently outperform their ordered counterparts, and the incorporation of multilayer heterogeneity enhances the model fit. Furthermore, significant factors and their coefficient values vary across the different adaptive behavior alternatives. Intercity travelers demonstrate a higher probability of changing departure dates or canceling trips during snowy and windy days compared to rainy and foggy days. Trains exhibit higher flexibility and reliability during adverse weather, and access attributes significantly affect intercity travel adaptive behavior. Additionally, the analysis of individual and urban agglomeration attributes uncovers variations in adaptive behavior among individuals and cities. These findings provide profound insights into the complexities and variations of intercity travel behavior during adverse weather, and propose practical strategies to mitigate the detrimental impacts of adverse weather on intercity travelers.

An Android Smart City Traveller App (NaviCity)

Discover the cutting-edge and adaptable "Smart City Traveler App" that elevates the journey of individuals discovering unfamiliar cities. Through the utilization of state-of-the-art technologies, this mobile application expertly evaluates user preferences, time limitations, and curiosities, supplying individuals with custom travel plans and suggestions. With the ultimate purpose of enhancing sightseeing for both new and experienced travelers, the app commences with registration where users are prompted to input crucial information for tailoring their exploration. By leveraging the comprehensive location data collected through the Google Places API, the app guarantees a wealth of information on countless destinations for users to access. The Smart City Traveler App, constructed with a sleek combination of Flutter and Firebase, takes the hassle out of trip planning by aggregating top FourSquare rankings. With an intuitive interface, users can easily peruse and utilize this wealth of information to make informed choices. Not only does the app boast detailed maps pinpointing points of interest, but it also offers up various routes to maximize time efficiency. In the case of longer stays, the app even suggests exciting adventure activities or refreshing water parks, based on highly rated reviews. Incorporating Google Maps, the Smart City Traveler App puts the power in users' hands to confidently explore the city. Transforming city exploration into an unforgettable journey, this app is a must-have for adventurous travelers. Seamlessly blending cutting-edge technology with the thrill of discovery, the Smart City Traveler App is a testament to the endless possibilities of merging travel and technology. With personalized features and efficient optimization, it empowers users to fully indulge in the urban landscape, tailored to their own preferences and constraints.

An integrated geospatial data model for active travel infrastructure

Active travel has received increased investment and interest in many countries both due to COVID-19 and to policies which promote a shift in mobility behaviours to support a wide range of public and individual goods. However, while there has often been substantial investment in the physical infrastructure that can help facilitate active travel, there has not so far always been commensurate investment in the data infrastructure which can help enable people to shift trips to active modes. Current fragmented data and data models and a lack of data standards pose a barrier to the development of the applications which are needed to support planners, users and journey planning. There is therefore a need for a more integrated, better-connected and more richly attributed active travel geospatial network model. This paper describes the development of such a data model, and its initial application to a case study of Great Britain. It demonstrates how the development, population and maintenance of such a data model could facilitate a range of novel applications, such as the personalisation of active travel journey planning to address different user needs and capabilities. If the potential societal benefits from investment in physical active travel infrastructure are to be fully realised, this needs to be supported by the availability of a robust spatial data infrastructure capable of providing the information required by walkers, wheeled users and cyclists to make effective use of the active travel network.

Tour Sentiments Personalized Recommendations System Using AI and Deep Learning

In a world saturated with travel information overload and generic recommendations, "Tour Sentiments: A Personalized Travel Recommendation System Using AI " emerges as a groundbreaking initiative. This project seeks to transform travel planning by harnessing the capabilities of Artificial Intelligence and real-time google Maps with street view and duration of travel. The primary goal of 'Tour Sentiments' is to create a personalized travel recommendation system that adapts to the changing emotions and preferences of travelers. By employing real-time sentiment analysis using google gemini, the system aims to offer tailored suggestions that align closely with individuals' current feelings and desires, departing from conventional one-size-fits-all approaches. Beyond mere efficiency, the ultimate vision of 'Tour Sentiments' is to elevate the travel experience, infusing it with joy, discovery, and lasting memories. Through the amalgamation of AI and sentiment analysis, the project aspires to make global exploration more accessible and captivating, setting a new standard for personalized travel guidance and enriching journeys worldwide.

Rural Journey: A Rural Tourism Planning Platform

The report from the 20th National Congress of the Communist Party of China highlighted the comprehensive advancement of rural revitalization by maintaining the priority of agricultural and rural development, consolidating and expanding the achievements of poverty alleviation, accelerating the construction of a strong agricultural country, and solidly promoting the revitalization of rural industries, talent, culture, ecology, and organizations. Rural Journey is a rural tourism planning platform designed specifically for rural traveling that aims to better integrate the three main groups of travel planners, customers, and villagers. It provides a platform for those aspiring to become travel planners and allows local villagers to join in serving travelers, adding distinctiveness to their services. Vigorously developing rural revitalization through this platform can expand income sources for residents and contribute to local poverty alleviation and income creation. The platform emphasizes manual services to address the inaccessibility of the elderly and youth in using online software by offering more detailed, one-on-one services to better meet the diverse requirements and preferences of travelers. The platform can create personalized travel plans tailored to different requirements by understanding the users better. Travel planners conduct comprehensive planning to develop optimal solutions, reducing financial expenditure and innovating a model for travel planning in rural areas.

Collaborative strategy for electric vehicle charging scheduling and route planning

AbstractDue to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber‐physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off‐peak hours. For the off‐peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi‐EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.

Route and charging planning for electric vehicles: a multi-objective approach

ABSTRACT Electric vehicle (EV) travel planning is a complex task that involves optimizing both the routes and the charging sessions for EVs. Existing algorithms rely on single-objective optimization, which limits their ability to consider EV users’ multiple, often conflicting objectives. In this paper, we introduce a new, genuinely multi-objective approach to EV travel planning, which can find Pareto sets containing multiple EV travel plans optimized simultaneously for multiple objectives. We focus on the bi-objective optimization for travel time and cost. To our knowledge, our algorithm is the first to perform such a genuine multi-objective optimization on realistically large country-scale problem instances involving 12,000 charging stations. We implemented our approach into a fully operational prototype application and extensively evaluated it on real-world data. Our results show that our approach can achieve practically usable planning times with only a minor loss of solution quality despite the very high computational complexity of the problem.

Identifying alternative stops for first and last-mile urban travel planning

Urban travelers today are seeking increasingly more information to plan their optimal trip, based on additional factors other than scheduled departure times. Still, some route planning applications provide a simple approach with a few parameter settings (e.g. to minimize travel time between two specific places at a certain time) and without any multimodal solutions. Our approach provides travelers with a set of non-dominated nearby stops that presents a number of traveler preferences in an easily comprehensible and quickly calculable manner. We display first and last-mile stops that fall on a Pareto front based on multiple criteria such as travel time, number of transfers, and frequency of service. Our algorithm combines stop and route-based information to quickly present the traveler with numerous nearby quality options for their itinerary decision making. We expand this algorithm to include multimodal itineraries with the incorporation of free-floating scooters to investigate the change in stop and itinerary characteristics. We then analyze the results on the star-shaped public transportation network of Göttingen, Germany, to show what advantages stops on the Pareto front have as well as demonstrate the increased effect on frequency and service lines when incorporating a broadened multimodal approach.

Risky choice and diminishing sensitivity in MaaS context: A nonlinear logit analysis of traveler behavior

In the rapidly transforming landscape of urban transportation, Mobility-as-a-Service (MaaS) has emerged as a potentially transformative solution to adapt to fluctuating travel demand. However, despite arguments in the literature suggesting that uncertainty in travel plans may influence travelers’ preferences for MaaS, the extent and mechanisms of this impact remain relatively underexplored. This study aims to bridge this gap by evaluating travelers’ preferences toward MaaS subscriptions, mode choices, and their reactions to uncertainty within a risky-choice modeling framework. We incorporate a linear-specified benchmark model and two nonlinear models (one based on Expected Utility Theory with Constant Absolute Risk Aversion adjustments, and the other on Prospect Theory), to estimate travelers’ risk perceptions and their valuation of gains and losses associated with travel time and costs caused by occasional activities during commute trips. The data utilized in our study originates from a specifically designed stated preference survey carried out in Hong Kong. Our findings present insightful observations into MaaS bundle configurations, socio-demographic patterns, and notably, behaviors under uncertain circumstances. In the face of uncertainty, we observe a noticeable increase in reluctance to adopt MaaS, and a heightened preference for taxis. The models also underscore significant risk aversion and diminishing sensitivity concerning travel time and cost, with these tendencies proving more pronounced for travel time compared to cost. These insights are valuable for policymakers and MaaS operators, offering an enhanced understanding of users’ risk attitudes and the intricate interplay between travelers’ preferences for MaaS and the uncertainties inherent in daily travel.

Tourism in Times of Crisis and Post-Crisis

The COVID-19 pandemic has had a profound effect on tourists' travel plans. Whilst the relationship between perceived risk and tourists' decisions and behaviours concerning COVID-19 is already an established research area in tourism studies, the dynamics of post-pandemic travel intentions and how they are influenced by travel motivation, destination safety and the notion of revenge tourism received very little attention. Therefore, this study proposes a model linking revenge tourism, destination safety, and travel motivations, leading to post-pandemic travel intentions. Furthermore, this research note lays a foundation for future research that can guide towards understanding tourists' travel intentions in times of crisis, similar to the COVID-19 pandemic.

“Revenge Travelling” and COVID-19 – Reality or Myth?

During the COVID-19 pandemic, the tourism sector was sometimes completely shut down. However, the strict measures were relaxed in periods characterized by lower infection rates. In this context, “revenge travelling”, an assumption that people would travel excessively to relieve their pandemic fatigue and compensate for missed holiday trips, was discussed amongst the tourism industry and media. This paper aims to unravel whether “revenge travelling” concerning COVID-19 is a reality or myth. It examines six indicators that constitute the phenomenon amongst students at a German university. Four out of the six indicators did not provide any proof of “revenge travelling” behavior. Tertiary students did not travel more frequently in 2021 than in 2020; they did not spend more on vacations, and COVID-19-related considerations did not change their travel planning. Additionally, only a minority of the students who travelled more often linked this to compensating for missed vacations. Nonetheless, the indicators “travel durations” and “travel destinations” revealed some evidence of travel behavior associated with the phenomenon. In sum, this study argues that COVID-19-related “revenge travelling” is a myth rather than reality.

A Predictive Model Based on TripAdvisor Textual Reviews: Early Destination Recommendations for Travel Planning

Although many studies have considered the effects of online reviews on tourists’ decisions, none have directly investigated how to leverage open data analyses to create early choice sets and facilitate destination planning. This paper illustrates how salient characteristics can be mined from the shared experiences embedded in review data and incorporated into a predictive model to build a travel counseling approach. The model is designed by first defining a prediction-based mechanism from online reviews and then generating a multinomial classification problem on all candidate destinations of interest. The model is implemented by applying Natural Language Processing (NLP) and Deep Learning (DL) technologies to review textual features. The model is validated using 75,315 reviews from TripAdvisor along with destinations from 257 U.S. national parks. Empirical results indicate a best classification accuracy of 67%, outperforming two previous approaches. Findings shed light on how to exploit past tourists’ experiences to generate early destination recommendations to identify items for choice sets and reduce tourists’ travel-planning effort. Theoretical and managerial implications regarding social media analytics are provided based on online review meta-data in touristic management.

Informational and analytical model for assessing the level of tourist satisfaction with accommodation facilities

Income from the tourism business directly depends on meeting the needs of consumers and their psychology. Individual and collective marketing psychology for the innovative development of the tourism industry depends on many factors. One of these factors is belonging to a social class, for example by gender, age, education, marital status, annual income, and others. The presented study is aimed at supporting decision-making regarding the assessment of the level of satisfaction of tourists from accommodation facilities according to social class, which reflects the individual and collective psychology of consumers. The purpose of the conducted research is to develop an information-analytical model for assessing the level of satisfaction of tourists concerning accommodation facilities according to social class, using the example of the Transcarpathia Region. For this purpose: informational and mathematical models of evaluation and forecasting of the level of satisfaction of tourists concerning accommodation establishments, relative to social classes, have been developed; the model was verified and adjusted on real data in Transcarpathia, Lviv, and Ivano-Frankivsk regions, which were collected from 327 respondents for the period 2020-2023; an approbation example of a fuzzy model for assessing the level of tourist satisfaction with accommodation facilities for the Transcarpathian region is given based on fragments of data from 209 respondents. The research uses the modern theory of fuzzy sets, linguistic variables, multidimensional membership functions, and system analysis, which make it possible to increase the degree of reasonableness of management decisions. Based on initial evaluations, the degree of validity of using various decisionsupport scenarios by various management subjects increases. Such scenarios can be: on the part of the tourist to improve the quality of travel planning, on the part of commercial companies to improve the quality of their services, and on the part of state authorities to understand the level of development of accommodation facilities. Further research of the problem can be seen in developing other fuzzy models and methods of assessing the level of tourism in selected regions through the psychology of consumers. The research presented and the models and methods developed in the future will serve as the basis of information technology for decision-making support for the digital transformation of the tourism industry, based on the individual and collective psychology of tourist marketing.

“Shared Online, Made People Envious, Felt Good”

An online survey of 200 participants was conducted on the experiences of sharing material and immaterial artifacts, focusing on interaction with non-media content types. The survey included six categories of sharing: music preferences, travel plans, sports activities, apartments and vehicles, virtual items in online games, and dietary preferences. The study identified factors that motivate participation in content-mediated interaction: discovery, curating self, connectedness, collaboration, enjoyment, and instrumental motivations, as a set of six motivating factors driving contemporary sharing practices. The authors consolidated findings from earlier work on motivations to share personal content, validating the set of motivating factors with the six selected non-media types of content. The results suggest that both extrinsic and intrinsic motivations affect content sharing practices. Furthermore, motivating factors were mapped to the reported positive and negative experiences, in order to understand their relation and to discuss their capacity to guide user experience design.

FACTORS INFLUENCING INTENTION TO USE VIRTUAL REALITY TECHNOLOGY IN TRAVEL PLANNING

This study aims to identify the factors shaping individuals intention to use virtual reality (VR) technology in travel planning. This study utilizes the value-based adoption model (VAM) to examine users’ perceived values and the intention to use VR in travel planning. The respondents of this study will be Malaysian youth aged 15 to 40. Data will be collected using a convenience sampling technique. To collect data, online survey questionnaires will be disseminated via social media. The findings of this study are expected to assist industry players in attracting more tourists through VR.

FACTORS INFLUENCING THE USE OF TIKTOK IN TRAVEL PLANNING

This study aims to investigate the use of TikTok in travel planning. Specifically, this study will use the UTAUT2 theory to examine the effects of performance expectancy, effort expectancy, social influence, facilitating factors, habit, and hedonic motivation on tourists’ intentions to use TikTok for their travel planning. In addition to these factors, the study considers perceived trust, self-efficacy, and personal innovativeness as additional independent variables that may influence their intentions. Data will be gathered from the respondents via a questionnaire using a purposive sampling. After data collection, the Statistical Package for Social Science (SPSS) will be used for analysis. It is anticipated that the findings of this research will add to the body of knowledge and provide useful information to the travel and tourism sector to enhance tourists' experiences.

Social Media Adoption in Tourism Promotion and Tourist Intention: A Diffusion of Invovation Approach

The tourism industry's reliance on social media as a potent marketing and promotional tool is increasingly evident, with platforms offering unparalleled reach and engagement with potential travelers. This research employs the Diffusion of Innovation (DOI) Theory to investigate the adoption and spread of social media platforms within the tourism industry. Using a sample of 300 respondents, selected through snowball sampling, the study employs Structural Equation Modeling (SEM) with Smart PLS to analyze the data. The research aims to understand the relationship between social media usage by different entities—government and official authorities, influencers, and tourism organizations—and its impact on tourism promotion and travelers' plans. Results indicate a significant positive relationship between government and official authorities' adoption of social media and the promotion of tourism. Similarly, social media use by influencers and tourism organizations positively influences tourism promotion. Moreover, this study underscores the substantial influence of government-backed promotions and those supported by tourism organizations on the future travel plans of tourists. Notably, these promotional efforts play a crucial role in shaping the decisions of prospective travelers. In contrast, the research found a negligible impact of influencers' promotion on individuals' travel plans. The study's model, explaining 57% of the variance, attests to its predictive accuracy and concludes that in the contemporary digital era, the tourism industry's adept utilization of social media platforms, particularly through government and tourism organizations, emerges as a powerful force that significantly molds tourists' plans and contributes substantively to the industry's sustainable growth.

Exploring the Commuting Decision-Making Behavior of Low-Income Groups Based on Cumulative Prospect Theory

Based on cumulative prospect theory, this study proposes a decision-making model to reveal the commuting behavior of low-income groups under uncertain conditions. Considering the commuting characteristics of this group, travel economic cost is incorporated into the model. The decision-making behavior of low-income groups under different travel scenarios is analyzed to acquire the priority of commuting plans. Based on a stated preference survey of residents in Beijing, the commuting behavior of low-income groups is empirically analyzed. The results show that low-income groups will be affected by individual risk preference and characterized by bounded rational characteristics when making commuting decisions under uncertain conditions. Moreover, travel economic cost is a crucial factor for the decision-making of this group. When the travel budget time is less than the perceived time, they tend to seek risks and take punctual arrival as their primary goal. On the contrary, they tend to avoid risks. On the basis of ensuring the travel time meets their needs, low-income commuters always seek the economy and reliability of their travel plans. The empirical results further verify that the proposed model effectively describes the commuting decision-making behavior of low-income groups, and it is superior to the expected utility theory with respect to performance and behavior disclosure. This study provides a reference for improving the commuting efficiency and experience of low-income groups.

Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management

With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex temporal and spatial dependence of traffic flow sequence, it is very challenging to construct accurate traffic flow prediction in its ring neural network, graph network and Transformer model. Much of the existing work is based on very good models. Considering the advantages of convolutional networks, such as high computational efficiency and strong feature extraction ability, a traffic flow prediction model based on multi-view spatiotemporal convolution is proposed.

A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction

Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for traffic flow prediction for making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Moreover, sensors to catch the information about traffic flow will be interfered with by environmental factors such as illumination, collection time, occlusion, etc. Therefore, the traffic flow in the practical transportation system is complicated, uncertain, and challenging to predict accurately. Motivated from the aforementioned issues and challenges, in this paper, we propose a deep encoder-decoder prediction framework based on variational Bayesian inference. A Bayesian neural network is designed by combining variational inference with Gated Recurrent Units (GRU) which is used as the deep neural network unit of the encoder-decoder framework to mine the intrinsic dynamics of traffic flow. Then, the variational inference is introduced into the multi-head attention mechanism to avoid noise-induced deterioration of prediction accuracy. The proposed model achieves superior prediction performance on the Guangzhou urban traffic flow dataset over the benchmarks, particularly when the long-term prediction.