Travel Habits Research Articles

Exploring the influence of driver’s presence on passengers’ willingness level to take autonomous taxis – Evidence from Seattle metropolitan area

This study aims to identify the potential determinants of people's willingness to adopt autonomous vehicle (AV) taxis, with or without a backup driver, using Binary Logistic Regression in combination with the enhanced Random Forest attribute selection method. The results indicate that young men with frequent use of ride-sharing services, walk frequently, and have household incomes of approximately $150,000 − $200,000 are more likely to be interested in using AV taxis regardless of the presence of a backup driver. The study finds that previous travel habits (use of different modes) can greatly influence individuals' interest levels in using AV taxis. The study points AV taxi service providers should address female passengers' concerns to pursue a larger market. This study innovatively uses individuals’ preferences for their residential locations, such as proximity to workplace, walkability, and affordability. Through the inclusion of these variables in the analysis, this study offers a more comprehensive insight into the potential users of AV taxis, thus enabling policymakers and planners to pursue a transportation system that is sustainable, equitable, and responsive to the diverse needs and preferences of different populations. The study has limitations in that it lacks users' perceived benefits and their specific needs pertaining to AVs. Finally, the study emphasizes the importance of examining the causes of inequality and promoting equitable access to AV technology, particularly for vulnerable groups.

Examining Travel Mode Preference for the COVID-19 Post-Vaccination Period: Insights from Dhaka, Bangladesh

This research investigates preferences of general public towards travel modes in the COVID-19 post-vaccination times. First, a questionnaire survey is conducted in Dhaka, Bangladesh, to gather information on people's socio-demographic characteristics, travel choices before, during, and after the pandemic, and attitudes towards public transit, active transport, and personal car. Next, three ordered logit models are developed to explore how mode preferences vary across socio-demographics, travel characteristics, and risk perceptions. This research identifies factors that contribute to commuters' travel mode preference as well as factors that act as deterrents. Analysis reveals that there will be a noticeable shift from public transport to private car and active transport. To be able to walk or bike to work, people may consider relocating closer to their workplaces. Individuals who used a private car before vaccination may continue to do so after being vaccinated. Additionally, women are more likely to prefer own vehicles while people aged more than 49 years are less likely to prefer public transport. Results also suggest that use of food delivery services, ridesharing, and working from home significantly affects travel mode preference. Individuals who never used ridesharing services are more likely to prefer walking and bicycling after vaccination. The outcomes of this study are expected to assist transport policymakers in the Global South to better understand COVID-19′s long-term influence on mode choice behavior and help in developing pragmatic policies to promote sustainable travel habits.

The Changing Lives of Amish Women: Surprising Findings from My PhD Research

In the foreword to Plain Women(Reynolds, 2001), the ethnographer Simon Bronner writes, “In fact we know far more about Amish men than about Amish women.” His statement prompted me to want to know more about Amish women. My research into how life has changed for Amish women fulfilled both my own intellectual curiosity and fills a gap in the understanding of the lives of Amish women in the wider academic field, although a thorough treatment of the lives of Amish women was subsequently published, which both supports and contrasts with my findings (Johnson-Weiner, 2020). After an initial introduction to Amish women via a gatekeeper, I used snowball sampling to find other Old Order and New Order Amish women, first in Holmes County, Ohio, and later in various communities in Pennsylvania. My fieldwork was based on an ethnographic approach whereby I lived with Amish families as well as interviewed New Order and Old Order women in Ohio and Old Order women in Pennsylvania during visits in 2012 and 2014. This article describes changes identified by those women in homes and gardens, household technology, travel, and vacation habits in their lifetimes.

Education for sustainable mobility in Slovenia: Using gamification to influence the travel habits of children

The ever-increasing mobility of modern society has a number of negative effects on space, the environment, health and quality of life. It is therefore crucial to raise awareness of the importance of sustainable mobility, part of which is effective and comprehensive education of children. The article presents some key findings of the Sustainable Mobility in Preschools and Primary Schools project, carried out in the period 2019–2021. We investigated whether gamified activities can change students' travel habits and whether the change in travel habits is influenced by their age. The study is based on the survey data from 28,043 primary school students (grades 1–9) across 220 schools (77.7% in rural, 22.3% in urban settlements). Employing paired-samples t-tests and one-way analysis of variance, the study evaluated the impact of gamified activities on travel habits. The results show that children can significantly change their traveling habits with the help of gamification. Although the greatest change in travel habits occurred during the implementation of the activity, the results suggest that more lasting effects can also be achieved. Furthermore, younger participants displayed greater responsiveness to the gamified interventions compared to their older counterparts. This research enhances the corpus of knowledge pertaining to educational strategies for sustainable mobility by providing insights from one of the largest projects to investigate the influence of gamified activities on children's commuting habits.

A survey of route recommendations: Methods, applications, and opportunities

Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens’ travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: (1) Methodology-wise. We categorize a large volume of classic methods and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. (2) Application-wise. We present numerous novel applications related to route commendation within urban computing scenarios. (3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends.

Exploring the Generation Z Travel Trends and Behavior

Abstract Tourism represents one of the sectors with the highest growth, and the upward trend of evolution implies the anticipation and adaptation to the changes in travel behavior. Currently, young travelers represent an important segment of the global tourism market. Generation Z decision-making processes are influenced both by the specific characteristics of the generation and by their travel habits. The travel motivations of the first generation born in the age of technology include socializing, acquiring unique experiences along with eco-friendly vacation options. During a trip, Generation Z visits museums, attends cultural or sports events and documents their choices by following travel influencers or social media posts. In these conditions, the paper aims to analyze the characteristics of the generation Z travel behavior and their perception of the future of travel. To achieve this objective, there will be a theoretical approach of generation Z coupled with secondary data analyses that will highlight the way young travelers organize their vacation.

An Empirical Framework to Quantify the Individual Traffic Congestion Cost for Private Motorized Vehicle Users

Traffic congestion negatively affects the economy, human well-being, safety, and environment in both developed and developing countries. In the past, numerous studies have been undertaken to assess congestion costs. This research focuses on quantifying the individual costs incurred due to traffic congestion and the effects of socio-demographic characteristics on the individual congestion of private motorized vehicle users in the context of an urban metropolitan (Lahore) in Pakistan. Further, the study has also proposed individual traffic congestion cost (ITCC) analytics to estimate the costs related to traffic congestion. Also, statistical tests, such as the ANOVA and T-test, have been employed to determine the impact of socio-demographic characteristics on individual congestion costs. The primary data was collected from the target population by filling out a questionnaire in the study area. With a confidence level of 95% and a sampling error of 6%, this study used a sample size of 282 participants. The study findings showed that the ITCC for the respondents ranged from 17.29 rupees per day to 1136.88 rupees per day. The ANOVA and the t-test indicate that all socio-demographic variables affect the calculation of ITCC. Furthermore, this research showed that car-owning respondents faced higher individual congestion costs than motorbike-owning respondents. The anticipated contribution of this study will aid private motorized vehicle users in estimating their individual congestion costs and managing their expenses. In addition, it will encourage individuals to modify their travel habits to make more efficient use of transportation for their daily tasks.

Inbound and Outbound Tourism in China: A Perspective Article

The tourism industry in China has undergone significant changes due to globalization, technology, and changing consumer preferences. This perspective article, based on a systematic literature review, examines examines trends, challenges, and opportunities in inbound and outbound tourism in China, focusing on the industry's dynamism. Both inbound and outbound tourism have been negatively impacted by the COVID-19 pandemic, which has prompted new strategies and environmental adaptations. China has changed its priorities within the country to encourage domestic travel and support local firms. The rise in internationalisation and the growing middle class in China have led to an unprecedented rise in outbound travel. Chinese travellers are affecting travel habits and spending all around the world in the tourism sector. Language barriers, cultural differences, and safety concerns are some of the challenges that come with travelling abroad. To reach its full potential, China needs to adapt to the shifting global environment and take into account the specifications and preferences of both inbound and outgoing tourists.

The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities

The outbreak of COVID-19 has caused unprecedented decline of ridership in urban rail transit and changed passenger travel habits, which greatly challenges subway operations. Therefore, it is necessary to better understand and quantify the impact of COVID-19 on passenger travel behavior, specifically route choice. Thus, we collected automatic fare collection data and 2060 random samples through a web-based survey in Beijing on passengers’ route choice behavior during the COVID-19 pandemic. This study utilizes an initial dataset to conduct an analysis and introduces an improved Generalized Random Regret Minimization model (GRRM) aimed at understanding passengers' route choice adjustments in response to COVID-19 guidance information. This improved GRRM accounts for two decision-making criteria, namely, maximum utility and minimum regret, and considers passenger heterogeneity. This marks the first instance of capturing the heterogeneity shift effect in route choice perception during the COVID-19 pandemic. The results show that the improved model has the best fitting result with adjusted Rho square of 0.536, demonstrating that the attributes related to guidance information (i.e., information push/time to receive traffic information/perceived route COVID-19 risk) indeed enhance the model’s fit. Furthermore, the research employs Value of Information Time to quantify the preference of passengers for information in various groups. Compared with the usual scenario, women, young and non-commuter passengers are more likely to receive an early information update to plan their trips in advance. Finally, the perceived risk of COVID-19 on routes is examined in relation to passengers’ personal attributes. It is observed that the elderly and students exhibit heightened sensitivity to the epidemic at all stages, while young passengers and commuters are particularly sensitive only during the small-scale epidemic. These findings offer valuable insights for managers to implement targeted strategies, thereby enhancing passenger flow control and encouraging increased subway ridership.

Travel insurance: an adult CHD patient's perspective.

Many adult CHD patients encounter difficulties in obtaining affordable travel insurance. We aimed to assess their travel habits and perspectives through a questionnaire. Our results indicate that many adult CHD patients use travel insurance but incur a premium due to their condition. There is an urgent need to provide better guidance to these patients on travel insurance options available to them.

Charging and Discharging Optimization of Vehicle Battery Efficiency for Minimizing Company Expenses Considering Regular User Travel Habits

With the increasing popularity and development of electric vehicles, the demand for electric vehicle charging is also constantly increasing. To meet the diverse charging needs of electric vehicle users and improve the efficiency of charging infrastructure, this study proposes an optimization strategy for electric vehicle charging and discharging. This method considers both the user’s travel mode and the operational efficiency of the charging pile. Firstly, a probability model based on travel spatiotemporal variables and Monte Carlo algorithm were used to simulate the travel trajectory of electric vehicles, providing a data foundation for optimizing the charging and discharging schemes of electric vehicles. Then, with the dual objective of minimizing the operating costs of charging piles and user charging costs, a linear programming model was constructed to optimize the charging and discharging strategies of electric vehicles. Finally, the model was validated using an apartment building as an example. The results indicate that, under the normal travel habits of users, with the goal of minimizing company expenses, the annual cost of the company reaches its minimum at a certain number of charging piles. When the cost of electric vehicle users dominates the objective function, they will pay more attention to battery degradation, significantly reducing their willingness to participate in discharge.

Оптимізація транспортно-логістичної системи задля спрощення переміщення населення сільської місцевості

The purpose of the article is to examine existing data for the selection of survey areas, data collection for the mobile network at the level of travel chains, and analysis of travel patterns. Travel demand in three rural areas with different population densities is considered, and requirements for new mobility solutions based on demand and people's travel habits are established.
 Public transport plays a decisive role in the logistics of territorial communities of Ukraine. However, inadequate provision and implementation of technological advances have left rural areas behind and heavily dependent on private cars. This article describes how to improve the availability of transport services in rural areas, where the demand for travel is often underestimated. The demand for travel in three rural areas with different population densities was analyzed, and the requirements for new mobility solutions based on demand and people's travel habits were determined.
 There is a need to access a higher level of subsidies if on-demand mobility services cannot attract an adequate level of demand so that rural and semi-rural communities have access to the same level of services and opportunities available to urban areas. Most of these services are deployed in urban settings with high travel demand and population density. Research on regions with low demand is rarely considered.
 Many on-demand services are community-based, where demand is determined based on local knowledge and specific needs. Those mobility services that are more commercially oriented use qualitative market research to determine demand for specific population segments and specific locations.
 Social isolation is exacerbated by lack of accessibility, roads and public transport, but is also linked to socio-demographic factors.
 It is suggested that consideration be given to removing barriers to commercially viable new mobility services in rural areas by: using data-driven approaches to understand typical daily travel patterns; identifying new mobility services that can be deployed for commercial purposes and those that will require ongoing financial support to provide socially necessary services.

Optimization scheduling method for multiple flexibility resources considering electric vehicle cluster

To unleash the scheduling potential of flexible resources, an enhanced flexibility approach is proposed, which takes into account various flexible resources including electric vehicle (EV) clusters. Initially, the dispatch potential of individual EV users is predicted based on historical data regarding travel habits and charging station parameters. Subsequently, the power and energy-feasible regions are constructed to quantify the flexibility of the EV clusters. To enhance the overall system’s operational efficiency and flexibility, various constraints related to flexibility resources are taken into consideration, including the source-grid-load-storage aspects. As a result, a multidimensional flexibility resource supply-demand model is established and the analysis target cascading method is applied to solve this sophisticated model. Finally, the modified system is used to test and analyze the proposed model and method. The results demonstrate the proposed model and method can enhance the operational flexibility of high-proportion new energy distribution networks.

Optimal dynamic pricing for public transportation considering consumer social learning.

Effective public transportation pricing strategies are critical to reducing traffic congestion and meeting consumer demand for sustainable urban development. In this study, we construct a dynamic game pricing model and a social learning network model for consumers of three modes of public transportation including metro, bus, and pa-transit. In the model, the metro, bus, and pa-transit operators maximize their profits through dynamic pricing optimization, and consumers maximize their utility by adjusting their travel habits through social learning in the social network. The reinforcement learning algorithm is applied to simulate the model, and the results show that: (1) as consumers' perceived sensitivity to different modes of travel increases, the market share and price of each mode of travel adjust accordingly. (2) When taking into account consumers' social learning behavior, the market share of metros remains high, while the market shares of buses and pa-transit are relatively low. (3) As consumers become more sensitive to their perception of each travel mode, operators invest more resources in improving service quality to gain market share, which in turn affects the price of each travel mode. Our results provide decision support for optimal pricing of urban public transportation.

Congestion pricing for sustainable urban transportation systems considering carbon emissions and travel habits

Effective congestion pricing is essential for the sustainable development of urban transportation systems. To explore the impact of congestion pricing on travel behaviors and the urban environment, this paper investigates vehicular carbon emissions and travelers' car dependency under three different schemes of congestion pricing (CP), namely, charging by travel distance (CP1), charging by travel time (CP2), and charging by distance+time (CP3). The interaction between travel behavior adaptation and the corresponding CP scheme is considered in the bi-level model we constructed to determine the optimal rate for the best of regional traffic and carbon emissions. The results of the case study indicate that all schemes perform well in meaningful ways, while CP3 works the best in terms of carbon emission (-23.06 %) and car-mode share (-4.9 %). This suggests that CP3 is a more promising charging mode, as it tends to increase the complexity and psychological pressure for travelers looking for alternatives and inhibiting their car reliance and habitual paths. The findings are conducive to forming the habit of green mode use so as to facilitate carbon emission reduction and traffic jam alleviation, which provide references for policy-making and promote urban transportation systems to operate in an environmentally sustainable and socially responsible manner.

Travel behaviour differences among people with disabilities: A cluster analysis of accessible taxi users before and during the COVID-19 pandemic

IntroductionPeople with disabilities (PWD) represent the largest minority group in Canada. Many municipalities have implemented paratransit services and subsidized accessible taxi services to provide affordable and equitable transport choices for PWD. However, few scholars have examined who is using these services and how they use them, making it challenging to assess the social impacts of paratransit projects. This study helps to address this knowledge gap through its examination of the differences in travel behaviour among PWD who use accessible taxi services in Toronto, Canada. Specifically, our study engages four research questions: (1) who are accessible taxi service users? (2) how are individuals using accessible taxi services? (3) are there different groups of users based on their travel behaviour? and, (4) how has travel behaviour changed in relation to the COVID-19 pandemic? MethodsThis article employs a cluster analysis to categorize users based on their travel habits and patterns and examines the impact of the COVID-19 pandemic on their travel behaviour. Study data includes approximately one million trips made by 40,000 users between June 2018 and June 2021. ResultsThe study identifies five distinct user profiles among accessible taxi users. During the pandemic, there was a significant decrease in the number of PWD using accessible taxi services, particularly for occasional users. Conversely, the percentage of users who made health-related trips increased. Additionally, the findings suggest that PWD who continued or started using accessible taxi services during the pandemic are more likely to be among the most socially disadvantaged groups. ConclusionsThis study can help transit service providers and policymakers better understand the different types of paratransit users and improve services. Additionally, the findings contribute to the existing literature on addressing transport-related social exclusion in Toronto, which is largely focused on population groups with low-income and affordability issues, and not disability.

Understanding compensatory travel

Resources to which people expect to represent their social and personal status influence important traveler decisions. Yet we know little about the antecedents and consequences of travelers' behavior under resource fluctuation. Three studies examined how resource status affects travelers' spending propensities in crisis times from the life-history framework. Results presented that travelers with lower childhood backgrounds increased their willingness to pay for international flights to compensate for their resource deficit when their current income is relatively low and travel restriction is severe. Without resource-scarce cues, travel intentions were associated with travelers' childhood travel habits. The research suggests potential customer relationship strategies by showing how and why travelers' life histories affect compensatory behavior that companies may adopt during the industrial crisis.

University commuting during the COVID-19 pandemic: Changes in travel behaviour and mode preferences

One prominent change induced by the COVID-19 pandemic concerns the worldwide use of public transportation for commuting purposes. This study focused on university commuting in Italy by examining the propensity to change transport modes under different infection risk scenarios. Data were collected in 2020 through an online survey of college mobility conducted by the Italian University Network for Sustainable Development. Asking the respondents to consider both a pessimistic and an optimistic scenario, with respect to the risk odds of being infected, we followed a two-step approach to study the prospective travel habits of college users. First, we tested a logit model to estimate the propensity to abandon one's pre-COVID-19 commuting mode. Then, we investigated the factors influencing the choice of switching from public transportation to either cars or active modes by estimating a multinomial logit model. By exploiting the novelty of considering two risk scenarios, this study highlighted that, especially in the pessimistic case, the change to active modes was constrained by spatial aspects in favour of motorized vehicles. From a policy perspective, this COVID-19-based natural experiment advocates transportation authorities taking effective actions to ensure that, in case of emergencies, a modal shift would not benefit more-polluting transport means.

Biases in self-reported travel behaviour: effects on self-reported travel distance, travel time and rain prevalence

Travel time and distance are key variables in evaluating transport planning decisions. However, people's perceptions and experiences often differ from objectively measured conditions. Therefore, this paper assesses the effect of different socio-demographic variables, travel habits, and mobility options on self-reported travel distance, travel time, and rain prevalence. Data from a survey on the potential use of e-bikes are analysed using logistic regression models. The most important conclusion from this paper is that travel distances in self-reported surveys are often overestimated, especially when these distances are short. Travel times are better estimated.

Assessing the Long-Term Impact of COVID-19 on Travel Behavior: The United Arab Emirates Perspective

The unprecedented COVID-19 pandemic has caused significant short-term travel disruptions and profound changes in daily travel routines. Yet, the long-term impact on travel behavior and mobility remains unclear. The present study examines the enduring effects of COVID-19 on travel patterns in the United Arab Emirates (UAE) through a comprehensive survey assessing long-term impacts and participants' travel behavior changes during different stages of the pandemic. The survey covered travel modes, frequency, duration, and distance, unveiling a pandemic-driven shift towards increased personal vehicle usage due to public transportation concerns. Nevertheless, as the pandemic gradually recedes, individuals are slowly reverting to their pre-pandemic travel habits. Moreover, the results indicate that travel patterns vary depending on the distance traveled. Short-distance travel has largely returned to pre-pandemic levels, while medium-distance travel continues to lag. Intriguingly, post-pandemic levels of long-distance travel have exceeded the pre-pandemic figures, indicating a potential alteration in travel behaviors. These observed changes were substantiated by a paired t-test, demonstrating a significant difference across nearly all periods examined. In summary, this research sheds light on the long-term consequences of COVID-19 on travel behavior and mobility in the UAE. The survey data uncovers a noteworthy transformation in travel patterns during the pandemic, with subsequent gradual reversion to pre-pandemic norms. Notably, the findings suggest potential shifts in travel preferences across different distances, with long-distance travel surpassing pre-pandemic levels. These insights contribute to a deeper understanding of the lasting impacts of COVID-19 on transportation and travel choices.