Individual Travel Research Articles

“Travel Inter-City like the men do”: Marketing British Rail’s Inter-City in Britain 1964–1979

The change in brand from British Railways to British Rail (BR) marked an important moment in the history of Britain’s railway. Running alongside BR’s modernization was a wider process of “professionalization” within the field of marketing. This paper explores how the wider professionalization of marketing impacted BR’s own marketing practices, showing that after 1965 BR opened its doors to new methods, means, and perhaps most importantly, specialists from outside the railway industry. Such marketing efforts helped to frame the railway in terms of individual travelers’ specific economic needs: by 1968 it had effectively segmented its passengers into demographic audiences, and by 1975, BR had a much better understanding of its markets. These individual economies were often highly gendered and saw only mixed success, but ultimately demonstrated an application of research, advertising, and promotion.

Impact of Areal Factors on Students’ Travel Mode Choices: A Bayesian Spatial Analysis

A preliminary analysis of the 2018/2019 Austin Travel Survey indicated that most off-campus students in Travis County, TX, tend to use cars rather than more sustainable transportation modes, significantly contributing to traffic congestion and environmental impact. This study aims to analyze the impacts of areal factors, including environmental and transportation factors, on students’ choices of travel mode in order to promote more sustainable transport behaviors. Additionally, we investigate the presence of spatial correlation and unobserved heterogeneity in travel data and their effects on students’ travel mode choices. We have proposed two Bayesian models—a basic model and a spatial model—with structured and unstructured random-effect terms to perform the analysis. The results indicate that the inclusion of spatial random effects considerably improves model performance, suggesting that students’ choices of mode are likely influenced by areal factors often ‘unobserved’ in many individual travel mode choice surveys. Furthermore, we found that the average slope, sidewalk density, and bus-stop density significantly affect students’ travel mode choices. These findings provide insights into promoting sustainable transport systems by addressing environmental and infrastructural factors in an effort to reduce car dependency among students, thereby supporting sustainable urban development.

Travel and Hospitality in Josephus’ Representation of the Essene Community (Judean War 2.124–127) and Its Literary and Cultural Contexts

Abstract In his description of the Essenes in the Judean War, Josephus asserts that members of this group settled in many cities, and, in doing so, formed an interregional community with members travelling between and visiting each other in different places. Individual travellers, in turn, travel light because they can enjoy the hospitality of the host community in the cities which they visit (War 2.124–127). How can we explain Josephus’ emphasis on the Essenes as a hospitable community in view of a first-century readership of War and in the wider socio-cultural context of the Roman Mediterranean? This article has two interlocking aims. The first is to examine the emphasis on travel and hospitality in Josephus’ description of the Essenes. To draw out this emphasis, we situate this passage in its wider narrative context and compare it to descriptions of the Essenes in other classical sources, especially the writings of Philo of Alexandria. Our second aim, taking the image of the Essenes as hosts and travellers as a departure point, is to elucidate the cultural constructs undergirding Josephus’ descriptions of hospitality throughout his writings. Starting from the premise that Josephus’ language of guest-friendship would be recognisable to local audiences in and around Rome, we consider how Josephus’ diction of travel and hospitality participates in broader cultural practices.

Intelligent Assisted Travel Wheelchair Based on Image Recognition Technology

This paper introduces an intelligent image recognition system integrated into a wheelchair based on deep learning in cold environments, aiming to improve the convenience and safety of disabled individuals. The system adopts advanced image recognition technology to monitor road conditions in real-time through the camera and to detect and measure distance to foreign objects on the road. The system visualizes the detection results on the wheelchair screen to assist the user in avoiding and improving the safety of their daily travel. In addition, the system also includes crawler tracks, seat heating, snow and rain protection, and other functions. The wheelchair has a wide range of application prospects and development potential. It is expected to be widely used in the future, providing a strong guarantee for the safe travel of disabled individuals in China.

Travel pattern recognition of urban rail passengers based on spatiotemporal sequence similarity

Understanding passenger travel patterns is helpful for the allocation of passenger resources in urban rail transit. Based on rail transit smart card data, this paper proposes a method to identify travel patterns by modeling individual spatiotemporal sequences. First, all the stations visited by individual passengers are extracted, and the similarity of the stations is calculated by the frequency of inter-station travel, the distance between stations and the activity duration of the stations. The main spatial activity area of the individual is divided using a hierarchical clustering algorithm. Then, the spatiotemporal sequence is inferred based on the individual's travel order. The sequence is a set of discrete values that characterize the spatiotemporal state. PCA-KL and K-Means++ are used to extract the similarity sequence structure to identify the passenger travel pattern. Finally, the rail transit smart card data of Xi'an in a certain month is taken as an example to identify its passenger travel pattern. The results show that complex passenger flow has 5 travel modes, of which 3 typical modes are commuting travel in a macro sense, accounting for 79% of the passenger flow. It can be seen that the pattern recognition based on the similarity of individual spatiotemporal sequences in this paper fully reflects the particularity and versatility of the research method, and is highly operational for different cities.

Assessment of recreational value of national Chambal sanctuary: application of individual travel cost model

Purpose This study aims to assess the recreational value of the National Chambal Sanctuary highlighting the potential benefits of conservation and the need for sustainable practices. Design/methodology/approach This study uses the Individual Travel Cost Method to evaluate the expenses of individuals visiting the National Chambal Sanctuary. Findings The study reveals that the National Chambal Sanctuary has a significant recreational value, with an average visitor value of INR 35,335.69 or USD 434 and an annual value of INR 132,473,501.81 or approximately USD 1.62m, indicating substantial economic contribution. Research limitations/implications The study's limitations may stem from data collection constraints, visitor reporting biases or other factors affecting estimates' accuracy. Future research could explore socio-economic factors or factors affecting low tourist inflow in India's protected areas (PAs). Practical implications This study suggests raising entrance fees for the National Chambal Sanctuary to ensure financial sustainability, based on its high recreational value and average consumer surplus. This has practical implications for policymakers, conservationists and the tourism industry. Social implications The study underscores the significance of protecting PAs like the National Chambal Sanctuary, suggesting that public attitudes towards biodiversity conservation can be influenced by highlighting its economic and recreational value and promoting awareness of its significance. Originality/value The study evaluates the recreational value of a wildlife sanctuary in India, offering insights into conservation's economic benefits and sustainable practices and promoting further research.

Linking Tourist Willingness to Pay and Beach Management: A Travel Cost Analysis for Balandra Marine Park, Mexico

Balandra, one of the most popular beaches in La Paz, Baja California Sur, was declared a flora and fauna protection area in 2012, and in 2019, the Mexican government chose it as the best beach in Mexico during the Tianguis Turistico celebrated on that year. Because of this latter distinction, this beach currently faces overcrowding. Formulating effective management policies depends, to a certain extent, on the knowledge of their recreational value and visitor characteristics. Recreational value allows us to know the benefits of the tradeoffs among the ecosystem services and society and exhibit the value of possible damages to marine ecosystems, like the one caused in 2022 by the fire of a tourist boat inside Balandra. Using the individual travel cost method and applying 159 questionnaires to site visitors, the individual willingness to pay to access Balandra Beach was estimated, resulting in USD 11.11/day/visitor. Recreational economic value (REV) for Balandra was estimated using two essential criteria: first, the total visitors registered in 2021, and second, the daily maximum carrying capacity. Welfare recreational loss was also calculated, derived from the site’s two-month closure, using REV as a proxy. Finally, beach management options and possible environmental and economic policy instruments that could be implemented are discussed.

Impacto de la tecnología en el turismo: Un análisis cienciométrico y revisión

The increasing integration of Information and Communication Technologies (ICT) in the tourism industry has significantly impacted internal management and communication with tourists, leading to the transformation of processes across various levels, ranging from large-scale tourism enterprises to services catering to individual travelers. The objective of this study is to comprehend these interactions through a scientometric and content analysis of pertinent research, leveraging the databases: Scopus and ScIELO. A comprehensive review of 3812 publications spanning from 2019 to 2023 has been conducted, adhering to criteria established by PRISMA. Additionally, the VOSviewer© tool has been utilized for network visualization in this investigation. The findings furnish insights for a profound understanding of the intersection between technology and tourism, thereby enabling researchers to explore novel domains, foster effective collaboration, and identify relevant sources and destinations within the technological and tourism realms. Moreover, this analysis, besides instigating new research endeavors through the examination of keywords, equips researchers with the ability to acquire knowledge concerning technological innovations, societal shifts, and the capacity to confront challenges and capitalize on emerging opportunities, all by tracking pivotal trends in technology- and tourismrelated terminology.

An approach for two-dimensional convolutional neural networks for hourly passenger boarding demand prediction based on uneven smart-card data

An invaluable resource for understanding passenger boarding patterns and forecasting future travel demand is the tap-on smart-card data. Positive instances, on the other hand—boarding at a given bus stop at a certain time—are less common than negative instances when looking at the smart-card data (or instances) by boarding stops and by time of day. Machine learning algorithms that are used to estimate hourly boarding numbers at a certain location have been shown to be much less accurate when the data is imbalanced. Before using the smart-card data to forecast bus boarding demand, this research tackles the problem of data imbalance in the data. To create fake traveling instances to add into a synthetic training dataset containing more evenly distributed traveling and non-traveling examples, we suggest using deep generative adversarial networks (Deep-GAN). Next, a deep neural network, or DNN, is trained on the synthetic dataset to predict which instances from a given stop in a certain time frame will travel and which ones won't. According to the findings, resolving the data imbalance problem may greatly enhance the predictive model's functionality and make it more accurate in predicting ridership profiles. The suggested strategy may create a synthetic training set with a better similarity so diversity and, therefore, a stronger prediction capability, according to a comparison of the Deep-GAN's performance with other conventional resampling techniques. The study emphasizes the importance of the issue and offers helpful recommendations for enhancing the quality of the data and model performance for individual travel behavior analysis and travel behavior prediction.

Understanding how and why travel mode changes: analysis of longitudinal qualitative interviews

BackgroundEncouraging alternatives to the car such as walking, cycling or public transport is a key cross-sector policy priority to promote population and planetary health. Individual travel choices are shaped by individual and environmental contexts, and changes in these contexts – triggered by key events – can translate to changes in travel mode. Understanding how and why these changes happen can help uncover more generalisable findings to inform future intervention research. This study aimed to identify the mechanisms and contexts facilitating changes in travel mode.MethodsProspective longitudinal qualitative cohort study utilising semi-structured interviews at baseline (in 2021), three- and six-month follow up. Participants were residents in a new town in Cambridgeshire, UK, where design principles to promote walking, cycling and public transport were used at the planning stage. At each interview, we followed a topic guide asking participants about previous and current travel patterns and future intentions. All interviews were audio recorded and transcribed. Data analysis used the framework approach based on realist evaluation principles identifying the context and mechanisms described by participants as leading to travel behaviour change.ResultsWe conducted 42 interviews with 16 participants and identified six mechanisms for changes in travel mode. These entailed increasing or reducing access, reliability and financial cost, improving convenience, increasing confidence and raising awareness. Participants described that these led to changes in travel mode in contexts where their existing travel mode had been disrupted, particularly in terms of reducing access or reliability or increasing cost, and where there were suitable alternative travel modes for their journey. Experiences of the new travel mode played a role in future travel intentions.ImplicationsApplying realist evaluation principles to identify common mechanisms for changes in travel mode has the potential to inform future intervention strategies. Future interventions using mechanisms that reduce access to, reduce reliability of, or increase the financial cost of car use may facilitate modal shift to walking, cycling and public transport when implemented in contexts where alternative travel modes are available and acceptable.

중국 Z세대 여성 개별관광객의 특성

The impact of COVID-19 has led to shifts in the tourism patterns of Chinese visitors to Jeju. Traditional group tours, commonly associated with package tourism, are rapidly giving way to individual or small-group tourism involving solo travelers, friends, or families. Notably, there is a relatively higher proportion of young female tourists compared to males. This study aims to comprehensively grasp the tourism preferences and behaviors of individual female tourists from China's Generation Z through extensive Indepth interviews. The goal is to draw implications for Jeju's tourism infrastructure to adapt to the increasing demand from Chinese individual young female travelers. Participants in the study are individual female tourists in their twenties from China, and their psychological and behavioral characteristics related to tourism preferences are thoroughly analyzed. The subjects of the in-depth interviews were individual Chinese female tourists in their twenties. The study meticulously described their psychological characteristics and behavioural traits related to tourism demand. It is hoped that the results of this research will serve as useful foundational data for developing marketing strategies aimed at attracting more individual Chinese female tourists to Jeju Island in the future.

Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing

Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility.

Optimizing Customized Bus Lines Considering Users' Transfer Willingness under Cooperative and Competitive Relationship between Metro and Online Car-hailing

In the context of ‘carbon peak’ and ‘carbon neutrality’, coordinating individual travel demand through multi-modal transportation and guiding travelers towards new shared public transportation (PT) modes is increasingly important. In this paper, we analyze the competitive and cooperative relationship between online car-hailing (OCH) services and metro systems in Nanning, China, and conduct aquestionnaire survey among different types of OCH users. A mixed choice model that considers psychological latent variables is constructed to investigate OCH users’ attitudes and cognitions toward customized buses (CBs). An improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to identify potential carpooling station sets, and a hybrid genetic-ant colony algorithm (GACA) is designed to solve bi-level programming model for CB line optimization. Case study results indicate an 83.8% overall transfer rate from OCH users to CBs, with the optimized scheme achieving a 69.68% reduction in carbon emissions.

“School near workplace” or “school near home”: Which one is better for the morning commute with both individual and household travelers in Y-shaped networks?

Household travelers typically need to coordinate their travel decisions in various aspects, such as destination and trip-timing, resulting in distinctive travel patterns compared to individual travelers. This paper investigates the morning commute problem in a “Y-shaped” network featuring “school near workplace” in consideration of both individual and household travelers and further understands the impact of school locations on the morning commute by comparing it with the “school near home” network. We analytically derive all equilibrium cases in the “school near workplace” network and classify them into three traffic patterns. We analyze the welfare effects of the staggering policy, finding that implementing the staggering policy in the “school near workplace” network can achieve a “win–win” scenario for individuals and household travelers in some equilibrium cases. Also, when the capacity ratio of school-constrained bottleneck to common-constrained bottleneck is below a threshold, which one is better (i.e., “school near workplace” or “school near home”) depends on the free-flow travel costs of the two networks. Locating the school “near workplace” is better than “near home” when the difference in the free-flow related costs for children between the two networks is not very large. Furthermore, we analyze the welfare effects of bottleneck expansion in the “school near workplace” network, finding that bottleneck expansion paradox may emerge when expanding the school-constrained bottleneck. Although properly designing the schedule gap can eliminate the paradox, a significant increase in the schedule gap may be needed to escape from the paradoxical cases when the common-constrained bottleneck capacity is slightly larger than the school-constrained bottleneck capacity. Our study sheds light on the importance of school locations in determining the performance of traffic management policies in the morning commute with both individual and household travelers.

When the personal and the collective intersects: Memory, future thinking, and perceived agency during the COVID-19 pandemic.

Do collective crises have an impact on the characteristics of mental time travel for individuals and collectives? The COVID-19 pandemic provides a unique context to address this question due to the intersection it created between the personal and the collective domains. In two studies (N = 273), we examined the valence and perceived agency involved in memory and future thinking for personal and collective domains. The second study also included a longitudinal component with 43 participants completing both studies. In research done prior to the pandemic, a valence-based dissociation between personal and collective events was consistently observed in Western samples. We wanted to see if these patterns changed during different stages of the pandemic. In the first study, participants no longer exhibited the usual positivity bias for the personal future, while in the second study, they did not exhibit the usual negativity bias for the collective future. The second aim of the current article was to assess the agency people attribute to themselves and their nation over events and how that relates to valence. People always attributed more agency to themselves over positive events than negative events in both personal and collective domains. Perceived nation agency, however, was associated with positivity in the collective domain but with negativity in the personal domain. Longitudinal analyses confirmed these patterns. Taken together, these results indicate that a collective crisis that has immediate and profound effects on personal lives can alter the patterns observed for mental time travel, especially for the future. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Impact of newly extended metro line opening on commuting carbon emissions: A comparative study before and during the pandemic

Urban transportation is a key contributor to greenhouse gas emissions. Given that metro systems serve as prominent modes of public transportation, it becomes imperative to investigate the alteration in carbon emissions subsequent to the inauguration of new metro lines, along with the determinants influencing the metro commuting choices. Previous research tends to use surveys to collect individual travel data, which may have sample selection and self-reported data problems. This research combines big data and survey method to study commuting carbon emission change and influencing factors of metro adoption. Because COVID-19 also has an impact on individual commuting mode, this research further explores total and per capita carbon emissions before and during the pandemic period. Selecting Shenzhen metro lines as a study case, it is found that the opening of newly extended metro lines leads to an increased proportion of residents commuting by metro and a decreased proportion of car users, resulting in a reduction of commuting carbon emissions. Additionally, during the pandemic, people tended to choose private cars for commuting due to its advantage of independence and privacy, resulting in an increase in commuting carbon emissions. Finally, regarding influencing factors of metro adoption, it is also found that young and middle-aged, highly educated, low-income residents or residents without cars are more likely to choose metro as their commuting mode. This research suggests the local government promote public transit after the pandemic period and tailor transport policies for the various resident groups, including providing subsidies.

OD-Prophet: Toward Efficiently Predicting Individual Origin-Destination Travel Demand in Location-Based Services

OD-Prophet: Toward Efficiently Predicting Individual Origin-Destination Travel Demand in Location-Based Services

Electromagnetic Exposure Levels of Electric Vehicle Drive Motors to Passenger Wearing Cardiac Pacemakers.

The number of individuals wearing cardiac pacemakers is gradually increasing as the population ages and cardiovascular disease becomes highly prevalent. The safety of pacemaker wearers is of significant concern because they must ensure that the device properly functions in various life scenarios. Electric vehicles have become one of the most frequently used travel tools due to the gradual promotion of low-carbon travel policies in various countries. The electromagnetic environment inside the vehicle is highly complex during driving due to the integration of numerous high-power electrical devices inside the vehicle. In order to ensure the safety of this group, the paper takes passengers wearing cardiac pacemakers as the object and the electric vehicle drive motors as the exposure source. Calculation models, with the vehicle body, human body, heart, and cardiac pacemaker, are built. The induced electric field, specific absorption rate, and temperature changes in the passenger's body and heart are calculated by using the finite element method. Results show that the maximum value of the induced electric field of the passenger occurs at the ankle of the body, which is 60.3 mV/m. The value of the induced electric field of the heart is greater than that of the human trunk, and the maximum value (283 mV/m) is around the pacemaker electrode. The maximum specific absorption rate of the human body is 1.08 × 10-6 W/kg, and that of heart positioned near the electrode is 2.76 × 10-5 W/kg. In addition, the maximum temperature increases of the human torso, heart, and pacemaker are 0.16 × 10-5 °C, 0.4 × 10-6 °C, and 0.44 × 10-6 °C within 30 min, respectively. Accordingly, the induced electric field, specific absorption rate, and temperature rise in the human body and heart are less than the safety limits specified in the ICNIRP. The electric field intensity at the pacemaker electrode and the temperature rise of the pacemaker meet the requirements of the medical device standards of ICNIRP and ISO 14708-2. Consequently, the electromagnetic radiation from the motor operation in the electric vehicle does not pose a safety risk to the health of passengers wearing cardiac pacemakers in this paper. This study also contributes to advancing research on the electromagnetic environment of electric vehicles and provides guidance for ensuring the safe travel of individuals wearing cardiac pacemakers.

Rethinking public service facility distribution and management strategies with the consideration of carbon peak – Insights from Suzhou, China

Existing practices have primarily focused on the accessibility and per capita indices of public service facilities (PSFs) in decision-making processes aimed at promoting social equity. However, the national commitment to achieving a carbon peak may induce additional considerations into the planning and policy decisions of PSFs for local governments. By conducting a two-stage simulation of carbon emissions (CEs) and correlation analysis, we identified uncertainties and confirmed the nonnegligible impact of land use structure, particularly PSF distribution, on achieving the urban transport-related carbon peak. Subsequently, we elaborated on the imperative of optimizing PSF distribution for carbon reduction, rather than solely depending on transport management strategies, through a case study of individual medical care travel characteristics. Moreover, a further discussion on the potential trade-offs and synergies between current and low-carbon-oriented PSF planning and policy decisions underscores the necessity of collaboration among decision-makers across pertinent administrative sectors.

Rainfall impacts on urban route choices by private vehicle users: insights from São Paulo, Brazil

Urban drivers frequently experience challenges posed by adverse weather conditions like heavy rain. Despite the influence of these conditions on individual travel behaviour, there is limited understanding of how various aspects of rainfall affect the route choices in urban trips. In this context, this paper aims to evaluate the impacts of different rainfall conditions on the route choice behaviour of private vehicle users. We propose a method to identify the specific routes taken by drivers, utilizing observed trips reproduced from a large-scale dataset containing license plate information recognized by speed device cameras. Path Size Multinomial Logit models penalizing routes that share common links were estimated based on a choice set comprised of routes with a maximum similarity threshold. Distance and the actual travel times in the routes were estimated using real-world data from a third-party company, and cumulative, average and maximum rainfall through each route of the choice set were obtained from data captured by meteorological radar. A case study in the city of São Paulo, Brazil, considered the average and maximum rainfall intensities, distance, and travel time in different models. The results indicate that both average and maximum rainfall intensities negatively impact the utility of routes. This research enables the identification of routes with the highest probabilities of being chosen during intense rainfall. Such information is valuable for implementing measures to minimize the impacts caused by adverse weather conditions in urban trips.