Travel Demand Research Articles

The impact of green knowledge and sustainability on green travel intentions through the lens of theory of reasoned goal pursuit

Purpose The purpose of this study is to investigate the factors influencing green travel intentions within the tourism sector. By examining the moderating effect of green knowledge within the theory of reasoned goal pursuit (TRGP) framework, the research aims to enhance understanding of green travel decision-making and contribute to the development of strategies that promote sustainable travel practices in Vietnam and beyond. Design/methodology/approach Drawing upon the TRGP, a conceptual framework is developed to understand the relationships among active procurement goals, active approval goals, attitudes toward green travel, subject norms, green travel motivation, perceived behavioral control and green travel intention. The study adopts a quantitative research design and collects data from a sample of 544 Vietnamese tourists through a survey instrument. Findings The relationships between TRGP variables are discovered and confirmed in the context of ecotourism. Furthermore, the moderating roles of green knowledge are also investigated, directly contributing to sustainable tourism through the lens of TRGP. Research limitations/implications The study's exploration of green travel intention and the role of green knowledge contributes to theoretical frameworks and underscores the significance of environmental education in fostering sustainable travel behaviors. Stakeholders in the Vietnamese tourism sector can benefit from practical insights by focusing on eco-friendly accommodations and responsible tour packages. Leveraging social influence and educational campaigns may further encourage sustainable travel practices among Vietnamese tourists, enhancing their inclination toward environmentally conscious behaviors. Originality/value The research underscores the importance of interventions that target goal-oriented factors, positive attitudes, social norms, perceived control and green knowledge in molding sustainable travel behaviors. These insights provide a foundation for tourism stakeholders to tailor strategies such as environmental education programs and supportive policies to promote sustainable tourism practices among Vietnamese tourists effectively.

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.

Rural travel burdens in the United States: Unmet need and travel costs

Transportation accessibility, or the ease of reaching valued destinations, is a critical determinant of a person's ability to satisfy their essential needs. A lack of accessibility can result in travel burdens such as high transportation costs or unmet needs and adversely affect well-being. Prior research establishes the inverse relationship between travel burdens and access to transportation options such as public transit and proximity to destinations as well as a person's resources, including their income and access to a personal vehicle. Although travel behavior is understood to differ across rural versus urban contexts, little is known about the nature of travel burdens in rural communities. Using the 2017 National Household Travel Survey, this study quantifies and compares travel burdens in rural versus nonrural communities in the United States. In each context, we quantify and compare i) the magnitude of travel burdens, ii) who experiences travel burdens, and iii) the individual and environmental factors that are associated with travel burdens. We find higher rates of burdensome travel outcomes among rural residents. People who live in rural areas are more likely to report burdensome travel costs and unmet travel needs due to a lack of transportation options compared to people living in nonrural areas, and these differences are exacerbated for people without car access. Dispersed rural contexts are unique in that they exhibit a combination of higher rates of financial burden and unmet need relative to urban contexts. Within rural areas, financial burdens and unmet need are less prevalent for those who live in a small town when compared with those living in more dispersed areas, which suggests that even a small concentration of services and opportunities may facilitate greater access. Collectively, our results highlight the need for research that attends to context-specific needs and strategies to address travel burdens in rural communities.

Supporting the Mind in Space: Psychological Tools for Long-Duration Missions.

The psychological well-being of astronauts is becoming just as vital as their physical and technical readiness as space missions extend into deep space. Long-duration missions pose unique challenges, such as isolation, confinement, communication delays, and microgravity, which can significantly affect mental health and cognitive performance. This commentary discusses the need for innovative mental health support systems, including automated psychotherapy, as well as Earth-based training methods like mindfulness and relaxation techniques, to address the psychological demands of space travel. By integrating these approaches into pre-mission preparation and in-flight routines, astronauts can develop self-regulation strategies to manage stress, improve focus, and enhance emotional resilience. Automated psychotherapy available 24-7 provides real-time confidential support when communication with Earth is delayed. As space exploration moves forward, the success of missions will depend not only on technological advancements but also on the development of psychological countermeasures that prioritize mental health alongside physical well-being. This paper emphasizes the importance of continued research and collaboration to refine and test these tools in analog environments, ensuring astronauts are mentally and emotionally prepared for the challenges of space.

Studying Understudied Populations’ Travel Behaviors with a Machine Learning Approach: A Focus on Hispanic and Latinx Households

To address a gap in the literature regarding differential effects of built environments (BE) on minority populations, this study uses boosted regression tree modeling to explore travel behaviors of Hispanic and Latinx (H/L) households, finding significant differences in the variables that influenced these households compared to others. Overall, BE variables had greater impacts on H/L households than others. Nonlinear effects (effective ranges, thresholds) were identified for top-ranking BE contributors: transit stop density, activity density, land use mix, and destination accessibility. Compact development is shown to have increased impacts on reducing vehicle miles traveled (VMT) for these populations.

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Investigation of Traffic System with Traffic Restriction Scheme in the Presence of Automated and Human-Driven Vehicles

In the context of transportation development, the simultaneous emergence of automated vehicles (AVs) and human-driven vehicles (HDVs) can lead to varied traffic system performance. For the purpose of improving traffic systems, this paper proposes a traffic restriction scheme only for HDVs. We develop a variational inequality (VI) model to describe travel mode and route choices under this restriction scheme and design an algorithm to solve the model. The proposed model and algorithm are applied to a Sioux Falls network example to evaluate the effects of the traffic restriction scheme. Our findings indicate that the scheme improves overall social welfare, with a higher proportion of restricted travelers leading to greater social welfare as well as increased travel demand due to changes in capacity. However, some lower exogenous monetary factors lead to negative social welfare, as the presence of AVs may exacerbate road congestion. Additionally, advancements in technology are needed to adjust the weightings of travel time and congestion level estimates to further enhance social welfare. These results offer valuable insights for traffic demand management in traffic systems with a mix of AVs and HDVs.

Understanding the impact of COVID-19 on mobility behavior of public transport passengers: the case of Metropolitan Area of Porto

Public transport systems worldwide experienced significant declines in usage during the COVID-19 pandemic due to lockdowns and work-from-home mandates. While numerous studies have examined these phenomena, there is still a need for empirical evidence that not only documents what occurred but also provides actionable insights for future transport planning. This study aims to enhance understanding of public transport passengers’ mobility behaviors during different stages of the pandemic, using the Metropolitan Area of Porto, Portugal, as a case study. Automated Fare Collection data from 2020 were analyzed and compared with data from the pre-pandemic year of 2019. The analysis included temporal, spatial, spatio-temporal, and sociodemographic dimensions. Key patterns and trends identified include a rapid recovery of ridership post-restriction easing, homogenized daily travel patterns, varied impacts on different transport modes, and significant shifts in demographic travel behaviors. These findings highlight the resilience of public transport demand and suggest that adaptive scheduling, enhanced safety measures, targeted support for vulnerable groups, promotion of off-peak travel, investment in bus infrastructure, and encouragement of multi-modal transport are essential strategies. Implementing these strategies can help improve public transport planning and mitigate the adverse effects of future crises.

Enabling personalized smart tourism with location-based social networks

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

Review on road congestion pricing: a long-term land use effect perspective

Road congestion pricing (RCP) is an important way of controlling traffic congestion from the perspective of traffic demand management. This paper introduces the concept and four main types of applications of RCP, summarises the development of RCP theory, reveals the first- and second-order effects of RCP on transportation systems and land use, and analyses the long-term impact of RCP on the spatial location structure of land use. Relevant research progress considering land use effects is systematically and comprehensively reviewed from three research perspectives: demand modelling (pre-RCP), monitoring and analysis (post-RCP) and modelling methods (integration models and other models). The results showed that RCP will affect residents’ travel behaviour in the short term and will lead to changes in spatial location decisions in the long run, thus creating new travel demands and travel modes, affecting the transportation system and urban spatial patterns again. Implementing cordon charges has an agglomeration effect on the population in city centres. Cordon charges also have a significant negative impact on businesses in the toll area and a dispersive effect on jobs. Combined with appropriate strategies for shared autonomous vehicles, RCP can simultaneously improve transportation efficiency and regional accessibility.

An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

Origin-destination (OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. We have constructed the benchmarks for these four problems at https://github.com/tsinghua-fib-lab/OD_benckmark. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow related research, fostering interdisciplinary perspectives and modes of thinking.

Do Users Prefer Particular Public Bus Transport Operators in Bhopal, India?

This paper focuses on the unique situation of users’ preferences when choosing between different Public Bus Transport Operators (PBTOs) in Bhopal. While many studies have examined the choice between different modes of transport (such as choosing a car over a bus), the factors that lead users to prefer one PBTO over another—despite similar characteristics—have not been studied. This paper analyzes these differences in preference through travel behavior attributes. It also explores how the three PBTOs in Bhopal serve the daily mobility needs of different segments of the population and reveals the factors influencing users’ choice of one PBTO over another.

Do Smart Loading Zones help reduce traffic congestion? A causal analysis in Pittsburgh

Rising demand for ride-hailing services and e-commerce delivery intensifies competition for urban curbside spaces, leading to uncoordinated travel behavior, increased traffic congestion and social costs. One possible solution to address those issues is Smart Loading Zones (SLZs), equipped with advanced technologies to optimize curbside use. Yet, the real-world impact of SLZs on traffic flow is unclear due to a lack of real-world data and rigorous studies investigating SLZ’s causal effect on traffic speed. With granular speed data and real-world implementations of SLZs from Pittsburgh, PA, this study applies the regression discontinuity design method to rigorously examine the causal impact of SLZs on traffic speed in the downtown network. The results showed that the introduction of SLZs could enhance the traffic speed of the nearby road segments by 4.5%, while controlling for the underlying trend of speed and multiple influential factors such as time, weather, and road characteristics. In addition, SLZs with a short length could statistically improve traffic speed but those with a long length exert no significant effect. These heterogenous effects might be attributed to the weak enforcement at the time of SLZ deployment in Pittsburgh. The results confirmed the overall positive impact of SLZs on improving congestion. However, policies such as effective dimension planning and robust enforcement policies are essential to maximize the benefits of SLZs.

Perceptions of Women’s Safety in Transient Environments and the Potential Role of AI in Enhancing Safety: An Inclusive Mobility Study in India

Travel safety for women is a concern, particularly in India, where gender-based violence and harassment are significant issues. This study examines how the perception of safety influences women’s travel behaviour and assesses the potential of technology solutions to ensure their safety. Additionally, it explores how AI and machine learning techniques may be leveraged to enhance women’s travel safety. A comprehensive mobility survey was designed to uncover the complex relationship between travel behaviour, reasons for mode choice, built environment, feelings, future mobility, and technological solutions. The responses revealed that security and safety are the most critical factors affecting women’s travel mode choices, with 54% and 41%, respectively. Moreover, over 80% of women indicated a willingness to change their travel behaviour after experiencing fear, anxiety, or danger during their everyday journeys. Participants were 24% less willing to use ride-sharing services than ride-hailing services, which could affect the transition towards more sustainable transportation options. Furthermore, AI-based sentiment analysis revealed that 46% of the respondents exhibited signs of ‘anger’ regarding what could help women feel safer in transient environments. The practical implications of this study’s findings are discussed, highlighting the potential of AI to enhance travel safety and optimise future sustainable transport planning.

Study of travel motivation and visit intention: The mediating role of perceived risks and travel constraints

The motivation to travel has been extensively studied, as it plays a crucial role in influencing people's travel decisions. Various theories have been proposed to explain travel behavior, including allocentric, psychocentric, push-pull factors, TPC, and TCL. This study delves into the impacts of perceived risks and travel constraints on Japanese people's motivation to travel to Mongolia, employing the push-pull theory as a framework. In the questionnaire survey, 142 Japanese citizens were selected by simple random sampling method, and the survey was conducted in Japanese and English between April and May 2023. The collected data were analysed using IBM SPSS 26 and SmartPLS 4 software. Out of the seven proposed hypotheses, four were confirmed and three were rejected. When examining risk/constraints as mediating variables, the results revealed that push motivation did not significantly influence travel intention, while pull factors demonstrated a statistically significant relationship. Furthermore, two and three variables were identified to characterize the push and pull factors that drive Japanese people's travel decisions. Two segments of travellers were also identified: those seeking nature-based experiences and those interested in historical and cultural attractions. These findings may guide tourism business operators seeking to attract Japanese travellers to Mongolia.

High mobility lifestyles: Unpacking travel behavior in Accra's rapidly expanding periphery

This paper explores everyday travel behavior among urbanites in Accra's periphery and unpacks how travel choices and preferences are conditioned and constrained in a context of rapid urban expansion, unregulated residential sprawl, infrastructural deficits, congestion and changing socio-economic conditions. The paper draws on qualitative interviews (n = 48) and a travel survey (n = 2107) targeting economically active adults residing in peripheral neighborhoods. Their travel behavior is characterized by a high proportion of adults who regularly travel outside their neighborhoods, over relatively long distances (10+ km) and mainly using motorized transport. The paper proposes “high mobility lifestyles” as a metaphor to capture key aspects of travel behavior in the peripheries and explores how travelers navigate the opportunities and constraints associated with living in the peripheries. As a unique feature, spatially explicit destination data are used to map the predominant destinations and long distances travelled from Accra's peripheral neighborhoods. The research illuminates how infrastructural deficits, long distances, severe congestion and rising transport costs combine to make high mobility lifestyles exceedingly strenuous, costly, and time-consuming. Ultimately, in-depth understanding of travel behavior, embedded in contextual conditions, can provide insights on how travel choices may be changed and how transport, systems may be made more sustainable and inclusive.

Numerical study on flow field of high-speed train passing through a new type of station

With the growing demand for efficient travel, tunnels are being lengthened and train speeds are being increased. High-speed subway brings new challenges to the flow environment of tunnel. Additionally, the emergence of express trains passing through stations without stopping has an enormous impact on the tunnel's and the station's flow field. This study focuses on a new type of station (NS) with overtaking and avoidance lines, allowing slow trains to stop for passengers to get on and off, while express trains pass through without stopping. The study analyzes pressure variation and wind speed when express trains pass through the NS without stopping. Different train speeds are taken into account. Based on this, the enhancement of station ventilation by utilizing the piston winds generated by express trains passing through NS is investigated. The air exchange effect and energy savings of the NS are explored. The results indicate that enhancing station air exchanges by fully utilizing piston wind saves about 190.68 kWh/day in mechanical ventilation energy consumption. This study contributes to the improvement of subway station construction and the advancement of subway train development. It can offer data assistance and theoretical direction for high-speed train operations.

A research need: exploring the experiences and behaviours of veiled Muslim female tourists in Western destinations

ABSTRACT Previous research has explored the dress code, behaviour, motivations, and experiences of Western and Asian female tourists during their visits to Western, Asian, and Islamic destinations. However, there is a paucity of research that explores the behaviour and perspectives of hijab-wearing female Muslim tourists when visiting Western destinations for tourism purposes. Furthermore, despite the fact that some studies have explored the experiences of Muslim women in Islamic destinations (i.e. Malysia and Turkey) there remains a significant research gap in examining the travel experiences and behaviours of veiled female Muslim tourists who visit Western destinations. This is surprising considering the significant market potential presented by Muslim women who travel to Western destinations and their patterns of consumption. This research note serves to encourage scholarly inquiry into the experiences, coping mechanisms, spatial and socio-contextual factors, and constraints faced by veiled Muslim female tourists in Western countries, while advocating for a comparative analysis with Asian and Western counterparts to facilitate a holistic comprehension of their travel experiences.

A copula-based approach for multi-modal demand dependence modeling: Temporal correlation between demand of subway and bike-sharing

As a representative mode of shared mobility, bike-sharing serves not only as a convenient way to conduct short-distance trips in urban areas, but also as a feeder mode to public transit, forming the Bike and Ride (BnR) system. Conducting management for such a hybrid multi-modal system faces various challenges, including the complex interactions between bike-sharing and other modes, highly dynamic passenger demand, and the difficulty of accessing direct transfer data. To overcome such difficulties, our study proposes a framework for assessing the dependency between the two usage modes. Firstly, a Dynamic-Time-Warping-based (DTW) method is utilized to determine the catchment area (CA) between the two modes, allowing the BnR-related tendency similarity under a given time scale to be considered. Then, the patterns of probabilistic dependence between travel demand of the two modes are obtained by a copula-based approach, which separates correlations under specific usage levels from single modal demands. A case study on the multi-modal system formed by docked bike-sharing and subway in New York is conducted to validate the proposed framework. The tendency similarity is found to be most pronounced within 500 m on average under a 4-hour interval. For each formed station group (SG), the best-fitted copula type is selected, capturing the strong tail correlations present only at specific usage levels. The results show a variety of different correlation patterns within SGs, despite the close geographic locations they may share. Areas of potential transfer resistance between the two modes are identified, which is more evident in first-mile-related (FMR) activities. In contrast, the two modes display more weak connections in last-mile-related (LMR) activities. The obtained results can be utilized by bike-sharing service providers to analyze demand distributions and conduct efficient station-level rebalancing. Compared to previous methods, our proposed framework is computationally inexpensive since no direct transfer of data or complex inference network is required. It incorporates statistically significant spatial–temporal information, allowing for a more accurate determination of the bi-modal assessment range. Moreover, considering that single-mode influences are mathematically removed, the resulting correlation in principle links to the strength of the connections between the two modes. Therefore, it can be assessed as an indicator of the reliability of the multi-modal system.

A novel data-driven approach for customizing destination choice set: A case study in the Netherlands

Modeling the destination choice has been of great interest for travel behavior community as well as policymakers in understanding the demand for land use and transportation infrastructures at aggregate and disaggregate levels and possibly devising policies to balance the demand and supplies. One of the challenges underlying predictions of location choice is the large choice set. While traditionally many methods had been devised to limit the choice set size either on a rather ad hoc basis or based on space–time prism by removing the locations out of reach of the subjects, the current study takes a substantially different approach and proposes a data-driven method to customize the generation of the choice set. The proposition is that observing the mobility patterns of citizens for multiple weeks would enable us to limit the choice set, depending on how far the subjects travel (beyond or within the distance they travel for their most frequent activities) to conduct their various activities. More precisely, using longitudinal trajectory data, we first classify people into two subgroups: returners and explorers, based on the size of the area (around their k most visited locations: k-radius of gyration) they move during the observation period. The destination choice set for four types of activities is then customized for returners (and explorers) and is used in a sequence of decisions represented by decision trees for the prediction of their destinations. The models for the whole sample and each subgroup separately are compared. The results suggest that the accuracy of destination prediction improves substantially for all four selected activity types, especially for the returners whose choice sets are formed based on their radius of gyration.