Travel Attributes Research Articles

Study on the travelers’ P&R usage behavior during snowfall in the corridor valley pattern city

According to the characteristics of corridor valley pattern cities, this paper presents four aspects of terrain space perception. Using a structural equation model (SEM), the four-dimensional terrain space perception is analyzed through second-order confirmatory factor analysis. A random parameter Logit model (RPLM) was developed to examine the impact of individual socioeconomic attributes, objective built environment, travel characteristics, and terrain space perception on travelers’ Park and Ride (P&R) use behavior during snowfall conditions. Additionally, a Logit model was utilized to assess how individual socioeconomic attributes of travelers in corridor valley pattern cities influence their perception of terrain space during snowfall weather. The findings indicate that the four latent variables related to terrain space perception proposed in this paper effectively explain travelers’ perception of terrain space in corridor valley pattern cities, and the observed variables also support these latent variables. The subjective and objective built environment of corridor valley pattern cities significantly affect travelers’ P&R usage behavior during snowfall weather, and there is heterogeneity in the terrain space perception during snowfall weather among different travelers in corridor valley pattern cities. Furthermore, the gender, age, income, and experience of using P&R of travelers in corridor valley pattern cities play a significant role in moderating terrain space perception.

Investigating work commuters mode choice behavior in Dalian city during pandemic: a hybrid choice model approach

ABSTRACT This study examined commuters' mode choice behavior during the COVID-19 pandemic, focusing on the impact of both tangibles and intangibles factors. To do so, the authors developed a Hybrid Choice Model (HCM) which investigated the effects of sociodemographic characteristics, travel attributes, and intangible factors such as satisfaction, safety, and discomfort, on travel behavior. The model was tested using Stated Preference data collected from commuters in Dalian City (China) via wenjuanxing, an online survey platform. The results indicate that individual characteristics played a significant role in shaping people's perceptions and mode choice behavior was influenced by sociodemographic factors, travel characteristics, and the aforementioned intangible factors. More specifically, individuals, who perceived a higher level of safety, measured in terms of worries about robbery, accidents, and pandemic concerns, were less likely to choose cars. People with a stronger perception of discomfort favored metro over bus. Private car users reported higher satisfaction. The findings will be useful to policymakers for establishing more resilient transport solutions.

Public transport mode choice behavior of different-income passengers during large-scale public health incidents

Public transport ridership has been hit hard by the COVID-19 pandemic in many countries. Investigating passengers' public transport mode choice behavior during large-scale public health incidents can uncover the major influential factors and help propose policies and strategies to reduce the pandemic transmission and recover the public transport revenue. This study develops an integrated choice and latent variables (ICLV) model by income based on structural equation model to model passengers' public transport choice behavior during the normalized stage of the pandemic. The model considers passengers' socioeconomic attributes, travel attributes, and attitude-perception attributes, and can appropriately capture passengers' psychological latent attributes. Taking Beijing China as an example, we collect some revealed preference survey data online. The modeling results show that the risk perception as a mediator variable has a significant impact on mode preference. Moreover, the convenience of public transport has the largest influence on risk perception. These findings suggest that risk perception and the convenience of public transport play a major role in passengers' mode choice behavior. In addition, the impacts of the various influential factors on the public transport mode choices are significantly different across different income groups. Further, the ICLV model can achieve better performance and is superior to the traditional Multinomial Logit model. The modeling framework can help propose targeted and instructive strategies during the normalized stage of the pandemic by uncovering the major influential factors in passengers’ public transport mode choices, which is applicable to similar pandemics in the future.

Identifying Traveler Groups Lacking in Seatbelt Usage and Suggesting Policy Measures

This study was aimed to identify the traveler groups who are exposed to potential of severe injury due to lack of seatbelt use. Moreover, policy measures have been suggested to enhance seatbelt use among such groups. A hybrid data collection approach was adopted to gather traveler information. Seatbelt usage questionnaire was used as the survey instrument. The survey was carried out in the parking lots of educational and commercial facilities around Bahrain. Statistical tests, namely, t-tests and Analysis of Variance (ANOVA), were used to compare the attributes of travelers. Literature review was used to verify the findings of this study and identify common traveler groups which are lacking in seatbelt use. The recommendations are also provided on policy measures which could be adopted globally to promote seatbelt use. It was found that occupation, education, gender, nationality and accident occurrence have significant impact on seatbelt wearing. It was also found that accident experience and seatbelt wearing, both are dependent on travelers’ social and cultural background rather than each other. The findings of this study matched previous literature. Groups lacking in seatbelt use included male passengers belonging to lower- and middle-income groups which fits the description of expatriate workers in Bahrain. Some policy measures, appropriate to these groups, have also been suggested.

Balancing Work and Leisure: Unraveling Constraints on Work-Leisure Integration in Bleisure Travel

ABSTRACT Bleisure travel has gained momentum due to the flexibility of the modern workplace and the desire for work-life balance. Despite this, due to the contrasting characteristics of work and leisure and the limited resources available to travelers such as time and energy, it is difficult to assume that integrating these two domains has always been seamless. Utilizing a focus group interview and an online survey, this research explains travel attributes that generate conflicts between work and leisure during bleisure travel, impacting travelers’ subjective well-being. Findings contribute to the travel and work-life balance literature, offering insights for travel managers to mitigate conflicts and enhance travel benefits, recognizing the complexities of harmonizing professional responsibilities and leisure pursuits in contemporary travel practices.

Examining the dynamic engagement process of passengers in online car-hailing system: a view of user value

ABSTRACT The online car-hailing system developed rapidly in recent years. This paper introduces the concept of user value and develops a machine learning framework for modeling the dynamic transformation of passenger value in online car-hailing system. Specifically, during the engagement process, four user value types representing the engagement status were captured based on both consumption and travel attributes. Additionally, we modeled user value conversion prediction throughout the engagement process, seamlessly integrating the benefits of features enhancement and time series prediction. Experiments using real order data demonstrated that the proposed machine learning framework has higher predictive accuracy than other methods. The value categories of online car-hailing users change over time. High-value users have high retention rates, while low-value users are the opposite, and medium-value users have the greatest potential. The results provide an important basis for modeling the engagement process of online car-hailing passengers, and for promoting the customer maintenance and management in online car-hailing systems.

Exploring online consumer experiences and experiential emotions offered by travel websites that accept cryptocurrency payments

Digital marketers are becoming increasingly aware of people’s online consumer experiences owing to the increasing popularity of online consumption. Exceptional online consumer experiences and memorable encounters are crucial. Although the use of cryptocurrency payments has become a new area for online website business growth, research on the experiential attributes of online travel websites supporting cryptocurrency payments is still lacking. The current research utilized text analytics and sentiment analysis to analyze reviews from seven online travel websites that accept cryptocurrency payments. This study identified 17 online experience topics and examine their relationship with ratings. Thereafter, perform emotional analysis in the comments. This research aimed to clarify the experiential aspects and emotional responses associated with online travel websites that accept cryptocurrency payments, ultimately contributing to a profound understanding of consumer experiences in this domain. Result provides new insights into the online experiences of online travel websites that accept cryptocurrency payments and contributes to theoretical and practical implications in this field.

Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?

Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by both numeric data and urban imagery, thus necessitating a synergetic framework to combine them. This study creates a theoretical framework of deep hybrid models consisting of a mixing operator and a behavioral predictor, thus integrating the numeric and imagery data for travel behavior analysis. Empirically, this framework is applied to analyze travel mode choice using the Chicago MyDailyTravel Survey as the numeric inputs and the satellite images as the imagery inputs. We found that deep hybrid models significantly outperform both classical demand models and deep learning models in predicting the aggregate and disaggregate travel behavior. The deep hybrid models can reveal spatial clusters with meaningful sociodemographic association in the latent space. The models can also generate new satellite images that do not exist in reality and compute the corresponding economic information, such as substitution patterns and social welfare. Overall, the deep hybrid models demonstrate the complementarity between the low-dimensional numeric and high-dimensional imagery data and between the traditional demand modeling and recent deep learning. They enrich the family of hybrid demand models by using deep architecture as the latent space and enabling researchers to conduct associative analysis for sociodemographics, travel decisions, and generated satellite imagery. Future research could address the limitations in interpretability, robustness, and transferability, and propose new methods to further enrich the deep hybrid models.

The effects of virtual reality travel on satisfaction and visiting intention utilizing an extended stimulus-organism-response theory: perspectives from Thai tourists

PurposeThis study investigates how virtual reality (VR) travel attributes (e.g. sense and quality of information) influence spectators' flow experience, how emotion and past experience affect enjoyment and examines the impact of flow experience and enjoyment on satisfaction and booking or visiting intention.Design/methodology/approachThe VR tour stimuli were fabricated using scenic views from the National Aquarium in the USA. Participants were equipped with Matterport VR and audio headsets and started their virtual travel. Once the participants completed their VR tours, they were asked to complete the questionnaire. Using the stimulus-organism-response theory, 303 valid responses were analyzed using partial least square structural equation modeling (PLS-SEM).FindingsThe results showed that the sense and quality of information in VR travel positively and significantly impacted the flow experience. Moreover, emotions and past experiences positively and significantly influenced the enjoyment of VR travel. Similarly, flow experience and enjoyment positively and significantly affect satisfaction. However, satisfaction with VR-related tourism experiences negatively affects users' bookings and visiting intentions.Practical implicationsThis study concludes that, from Thai tourists' perspectives, virtual travel should be used as a solution only during the pandemic because, in the long term it can cause a loss to the business chain in the tourism industry.Originality/valueTo the best of the authors' knowledge, no prior research has examined the influence of past experiences and emotions on satisfaction with VR travel.

An empirical investigation of the distribution of travellers’ willingness-to-pay: A comparison between a parametric and nonparametric approach

Understanding the distribution of the willingness-to-pay (WTP) to reduce travel time or other unpleasant travel attributes experienced by travellers is crucial to making well-informed choices about infrastructure investments and the pricing of services. Given multimodality, heavy tails, and the impact of averaging errors in the classical parametric distribution-based analysis of WTP, this study compares WTP distributions across six distinct datasets using a range of model specifications, including multinomial logit (MNL), mixed logit (MMNL) and nonparametric logit mixed logit (LML). The models are estimated directly in willingness-to-pay space, highlighting that WTP is not uniformly distributed with a high likelihood of having a multi-modal distribution across the population. The findings confirm the intuition on the presence of negative WTP for some undesirable travel attributes. This study raises awareness among transport researchers, modellers, and authorities about using the correct WTP distributions in practice. It adds some empirical evidence regarding the true underlying WTP distribution of various travel attributes to the transport literature.

Research and Application of Intelligent Weather Push Model Based on Travel Forecast and 5G Message

In the realm of daily activity planning, precise weather forecasting services hold paramount significance. However, the prevalent dissemination of weather forecasts through conventional channels like radio, television, and the internet often yields only generalized regional predictions. This limitation contributes to diminished forecast reach, inadequate accuracy, and a lack of individualization, thwarting the effective distribution of meteorological insights and inhibiting the fulfillment of personalized forecast demands. Addressing these concerns, our study proposes a personalized weather forecasting approach that harnesses machine learning techniques and leverages the 5G messaging platform. By amalgamating projected user travel data, we augment personalized weather reports and extend user coverage to achieve tailored, timely, and high-quality weather services. Concretely, our research commences with an extensive analysis of large-scale user travel behavior data to extract pertinent travel attributes. Subsequently, we construct a user’s future location prediction model—dubbed the Loc-PredModel—by employing the Extreme Gradient Boosting (XGBoost) algorithm to forecast users’ trip destinations and arrival times. Anchored in the anticipated outcomes of user travel behavior, personalized weather data reports are formulated. Experimental results underscore the Loc-PredModel’s remarkable predictive prowess, demonstrating a root mean squared error (RMSE) value of 0.208 and a coefficient of determination (R2) value of 0.935, affirming its efficacy in prognosticating users’ trip destinations and arrival times. Furthermore, our 5G message-driven platform, rooted in intelligent personalized meteorological services, underwent testing within Chengdu city and garnered positive user feedback. Our research effectively surmounts the limitations of conventional weather forecasting platforms by furnishing users with more precise and customized weather information predicated on behavioral analysis and the 5G information ecosystem. This study not only advances the theoretical groundwork of intelligent meteorology but also offers invaluable insights and guidance for future advancement. By providing users with a more personalized and timely intelligent meteorological service experience, our approach exhibits transferability, with the research methodology and model potentially extendable nationwide or even on a larger scale beyond the study’s Chengdu-based scope.

COVID-19 and Public Transport in Auckland, New Zealand: Investigating Vulnerable Population Groups’ Ridership Behavior

Public transit ridership was severely affected during the COVID-19 pandemic in 2020 and the effects have continued since. The present study examines changes to ridership immediately post-pandemic in 2021. Research investigating the effects of COVID-19 on disadvantaged population groups is limited and the present study addresses this knowledge gap. Ridership of socially-disadvantaged groups such as low-income, female, and ethnic minority people is examined using order logit regression models. The study uses data from an online travel survey conducted in Auckland, New Zealand, immediately after all COVID-19-related restrictions were lifted. This allowed the collection of revealed preference data for the post-pandemic period. The regression models included the effects of socio-demographic characteristics of individual riders, travel attributes, and built environment factors. Findings suggest that those with lower income and from an ethnic minority group are likely to continue using transit frequently post-pandemic. Younger riders from the ethnic minority group are less likely to use transit frequently, while pre-COVID-19 they were more likely. Access to transit stops near home and work are significant factors for the ethnic minority group. Higher land use mix near the residence and work locations are found to induce more transit trips for all. It is critical for transit agencies to understand how the usage has evolved post-pandemic. These findings highlight the importance of considering the effects of the pandemic on different disadvantaged groups. Public transport service providers are encouraged to consider equity as they develop strategies to improve transit ridership.

Research on Choice Preference of Parallel Trains for High-Speed Rail Heterogeneous Passengers

The passenger capacity utilization of parallel trains on China’s high-speed railway (HSR) varies greatly, which seriously affects the revenue and passenger service quality of railroad companies. One of the key factors contributing to this issue is the uniformity of fares. To address this, China’s railway enterprises are implementing differential pricing reforms for parallel trains. This paper presents a two-step method that combines latent class analysis (LCA) and random parameter logit (RPL) models to estimate the preferences of diverse passengers for parallel trains and quantify them using willingness to pay. To illustrate this, we conducted revealed preference and stated preference questionnaire surveys at Nanning East Railway Station, China, focusing on the Nanning-Guangzhou HSR route. These surveys gathered data on passengers’ personal and travel attributes. LCA was carried out according to passenger attributes, and passengers were divided into three categories according to the goodness-of-fit measures. Through the questionnaire data, the travel time and departure time period of each category of the passenger were converted into costs, and the absolute value of each influencing factor of passengers for parallel trains was obtained. Considering the limited rational choice behavior of passengers, the perceived value of each influencing factor of passengers on parallel trains was calculated through prospect theory. Finally, an RPL model for parallel train choice of heterogeneous passengers was constructed, and the parameters of each category of the passenger were estimated based on the absolute value and perceived value of the influencing factors.

A Path Recommendation Method Considering Individual Driving Preferences

The issue of congestion on urban roads stems from an imbalance between transport demand and supply. It has become imperative to address the problem from the traffic demand side. While managing effective traffic demand relies on understanding the individual preferences of drivers, the current method for gathering preferences (i.e., through questionnaires) is both expensive and may not accurately capture the characteristics of respondents due to their varying interpretations of the options. To overcome these challenges, we proposed a path recommendation method that takes individual travel preferences into consideration by employing automatic license plate recognition (ALPR) data for the extraction of individual travel preferences. We initially identified key factors influencing the path selection behaviors of drivers, including path attributes, travel attributes, and individual attributes. Subsequently, we constructed a path satisfaction model based on individual preferences, employing an improved analytic hierarchy process (AHP). Furthermore, we utilized the pth percentile approach, rather than expert scores, in order to determine the relative importance of each indicator in the improved AHP. By applying the proposed model to the ALPR data from Xuancheng City, we successfully extracted the path selection preferences of drivers. We designed various scenarios to verify the reliability of the model, and the experimental results demonstrated that the proposed path satisfaction model can effectively capture the influence of underlying indicators on the path selection behavior of individuals with diverse travel preferences, considering different driver types and path attributes. Moreover, compared to the real trajectory, the recommended paths yielded an overall satisfaction improvement of over 10%, confirming the reliability and practicality of our proposed model.

Analysis of Factors Affecting Walking Speed Based on Natural Field Data: Considering the Attributes of Travelers and the Travel Environment

In Mobility as a Service (MaaS), walking plays a crucial role in connecting various modes of transportation. In order to provide more accurate predictions of walking travel time, a comprehensive and in-depth study is required to examine the factors that influence walking speed. Many existing studies focus on exploring various factors affecting walking speed, but there is limited research on further investigating the magnitude of their impact and the reasons for differences among different pedestrians. This study examines the relationship between personal characteristics and the degree of influence of environmental factors on walking speed. We recruited 31 volunteers and investigated their traveler characteristics such as height, weight, and age, as well as environmental factors such as weather conditions, ground conditions, and sidewalk Level of Service (LOS). Descriptive statistics were performed on walking speed, revealing the influence of these factors. For example, the speed of females is 89% of that of males. When in a hurry, the speed increases by 17%, while on uneven roads, the speed decreases by 11%. We then proposed the influence coefficient f to represent the degree of influence and analyzed its correlation with personal characteristics. We discovered some strong correlations. For instance, the greater the body weight, the more significant the reduction in walking speed due to precipitous weather or uneven roads. Similarly, the taller the person, the greater the increase in walking speed under the influence of a rushed situation. Finally, we constructed a series of regression models for “f” and a speed estimation model. Our findings provide support for predicting personalized speeds in various scenarios, based solely on the traveler’s personal characteristics and speeds in controlled group scenarios in the travel service system, and contribute to the study and development of MaaS in terms of travel time prediction, travel route planning, and personalized services.

Factors affecting college students’ attitudes towards carpooling

Abstract As a sustainable mode of travel, carpooling is an effective solution to environmental and energy problems, but it has not been fully utilized. The research on carpooling attitudes, especially for college students, still needs to be improved. In response to this problem, this paper aims to investigate the effect of personal attributes, travel attributes, attitude attributes and other attributes of students on carpooling attitudes in China. To collect the data, an online questionnaire survey was implemented at Shenzhen University, and 514 students participated. Based on the samples, the multinomial logit model is used to explore the contribution of all variables. The model results showed that personal attributes such as sex and age had no significant effect on the attitude of college students towards carpooling. The safety of carpooling and its high cost make college students have a neutral attitude towards it. The most concerning factor for college students in carpooling is comfort. These findings can provide valuable suggestions for measures to be taken in response to different attributes affecting students’ attitudes and are particularly important for the university and government to make optimal decisions to motivate students to select carpooling.

Validating the satisfaction with travel scale and measuring long-distance recreational travel satisfaction

The study of long-distance recreational travel is limited, despite its importance in both travel behavior and tourism literature. To aid the literature, this study validates the existing satisfaction with travel scale (STS)—which has been mostly used and validated for commute and daily travel—and investigates the factors affecting travel satisfaction. The data was collected from a questionnaire survey of visitors to US national parks conducted in the Summer of 2022, and a structural equation modeling framework was used for analyses. Confirmatory factor analysis suggested a three-factor structure with two affect-related factors—positive deactivation and positive activation—and a third factor (cognitive evaluation) related to valence and cognitive assessment of travel experience. The proposed measurement scale for long-distance recreational travel satisfaction could be used by tourism studies to investigate the interrelationships between travel satisfaction and tourism satisfaction, which could offer ways to increase the sustainability of tourism destinations. Structural equation model results show that socio-demographics, general travel attributes, trip-specific characteristics, travel-based activities, travel time perception, and attitudes are directly related to travel satisfaction. In addition to these direct effects, socio-demographic and general travel attributes are related to travel satisfaction indirectly through attitudinal characteristics (driving enjoyment and polychronicity). This investigation revealed a few differences in commute and long-distance travel behaviors: mostly the impacts of age, income, and travel duration on travel satisfaction. Based on the study results, it is also speculated that future autonomous vehicles would be a favorable choice for long-distance recreational travelers only if the option of manual driving is possible in those vehicles.

Analysis of Key Factors Affecting Low-Carbon Travel Behaviors of Urban Residents in Developing Countries: A Case Study in Zhenjiang, China

The transport sector accounts for 23% of global carbon emissions, the second largest after electricity and heat generation. Low-carbon travel, such as walking, cycling, and use of public transit, has become an effective way to reduce transportation-related emissions, however, what factors and how they affect low-carbon travel behavior remain unclear. This paper analyzes the key factors that affect the low-carbon travel behavior of urban residents by exploring 26 potential correlation indicators. Low-carbon travel survey data of urban residents in Zhenjiang, China are used as an example. Five factors derived from 26 indicators were identified and named as key factors influencing urban low-carbon travel behavior: traveler personal attributes (contribution rate 31.646%), user satisfaction with travel processes (contribution rate 17.438%), attitude towards low-carbon travel and environmental awareness (16.090%), the level of public transportation system development (10.793%), and overall attributes of travelers’ family (10.561%). The results provide a theoretical basis for the development and implementation of novel urban low-carbon travel concepts in the future.

A study on the factors that influenced the choice of transport mode before, during, and after the first lockdown in Milan, Italy

COVID-19 measures implied many changes to travel behaviour and transport mode choice during the pandemic. This study seeks to understand what individual characteristics and travel attributes are related to transport mode choice before, during, and after the first lockdown in Italy. Based on an online survey (carried out in May 2020 in Milan), three multinomial regression models are presented. The results show that and in which measure parameters regarding distance and duration of daily travel are markedly related to transport mode before the lockdown. However, these factors are less significantly associated with the transport mode during and after the lockdown. Meanwhile, factors such as Preferences and Worry about using public transport have more significant relationship with the modal choice during the pandemic. Regarding individual characteristics, women are more likely to use active mode during and after the lockdown. Additionally, two personality traits of Agreeableness, and Openness to experiences are related to transport mode during and after the lockdown, respectively. Overall, this study reveals that in addition to socio-demographic factors, other variables such as worry about using public transport, preference, and personality are associated with the choice of transport mode during the lockdown.

The Role of Attitude, Travel-Related, and Socioeconomic Characteristics in Modal Shift to Shared Autonomous Vehicles with Ride Sharing

The integration of automation and shared mobility services would significantly affect transportation demand, especially mode choice. However, little is known about how attitudes, travel attributes, and demographic factors affect the modal shift to shared autonomous vehicles (SAVs). A stated preference survey was designed to determine the preferences of car and transit users in relation to a modal shift to SAVs. The binary logit models’ results revealed distinct behavior patterns and systematic heterogeneity among transit and private car users based on a representative sample of 607 individuals in 2021. The shifting behavior of both users is positively affected by attitudinal factors, including consumer innovativeness, perceived usefulness, sharing intention, and ecological awareness, while negatively affected by privacy concerns. In terms of travel-related attributes of SAVs, car users are eight times more sensitive to waiting times compared to transit users, who are three times more concerned with travel costs. Further, privacy concerns, the number of passengers sharing a trip, and the ratio of waiting time to travel time of SAVs were the major barriers to shifting the likelihood of car users’ behavior. In light of these findings, based on the likely effects of SAVs on shifting behavior, a number of practical implications are suggested for more effective policy making.