Travel Mode Choice Research Articles

Collective or Individual? Tourists’ travel mode choice during a pandemic

The coronavirus pandemic (COVID-19) led to holiday journeys being associated with significant health risks. While there are numerous studies on the impacts of the COVID-19 pandemic on travel mode choice in everyday mobility, there are a lack of studies on tourists’ choice of travel mode, even though tourism transport in Switzerland makes up 24% of all distance travelled. Based on an extended conceptual framework of the Health Belief Model (HBM), this study investigates the effect of COVID-19 on tourists’ intentions to choose a particular travel mode during the pandemic. Our findings show that the higher the perceived susceptibility of getting COVID-19 associated with the holiday journey, the lower the choices for collective travel modes. Furthermore, for those tourists who are more likely to take risks, their choices for collective travel modes are increased. The study recommends that public transport operators choose measures that increase the application of non-pharmaceutical interventions (NPIs) against pandemics while travelling; this may encourage the safe use of collective transport modes during a pandemic.

SCOPING REVIEW: TRAVEL MODE CHOICE IN SAFE ROUTES TO SCHOOL PROGRAMME

This scoping review aims to explore the travel mode choice in safe routes to school programme, as review in existing literature. A scoping literature review is performed by searching Scopus database and identified 189 related studies. However, only 53 studies throughout 2007 – 2022 were identified as meeting the criteria established for the study. The aspects contained in this study includes existing studies which provide information on the safe routes to school (SRTS) programmes and their travel mode choice. This paper could be very beneficial for researchers, policy makers and whoever interested in undertaking future studies related to SRTS.

FORECASTING FUTURE PUBLIC TRANSPORT MODE CHOICE BEHAVIOUR OF COMMUTERS IN BAHRAIN USING LOGIT AND CLASSIFICATION TREE MODELS: A COMPARATIVE STUDY

Global trade and social relationships are greatly facilitated by transportation. However, the majority of nations, including Bahrain, face substantial challenges with their transportation systems. For the development of technical solutions that can promote the progress of these transport systems, it is now crucial to have a complete understanding of travel demands and driver's characteristics. This paper aims to explore the influential factors concerning travel mode choice in Bahrain and utilize mode choice models to forecast the probable utilities of various future public transport modes. The study utilizes diverse, 3864 data records extracted from previous surveys as well as a recent one conducted within this research. Subsequently, using Minitab software, two types of mode choice models were built, namely the logit model and the classification tree model, focusing on modelling the future transportation system, considering potential public transport modes (Public Bus, Metro, and Tram). The analysis of the data identified trip cost as the top predictor, moreover, direct, and quick travel, accessibility, and convenience were also found to significantly influence the choice of travel mode in Bahrain. Additionally, the findings indicate that the metro is the preferred choice for future public transport, with a strong preference observed for a combination of metro and tram. The research also suggests, in terms of model performance, that when capturing more complex patterns, as in this study, the classification tree outperforms the multinomial Logit model. Overall, the research provides valuable insights into mode choice in Bahrain and highlights the important factors influencing commuting decisions. The results of this study can support the development of an efficient public transportation system that would satisfy the needs and preferences of commuters in Bahrain and ultimately lead to a sustainable and accessible transportation infrastructure in the country.

Jointly modelling activity start time, travel mode, companionship, and destination location choices

Activity-travel decisions, including time use (i.e. activity start time), travel choices (i.e. mode and companionship), and destination location choices are interdependent and complex in nature. They are often controlled by observed factors such as sociodemographic, vehicle ownership, built environment, land use, and unobserved factors like attitude, preference, or habits which typically are not captured in the survey data. To accommodate these complex interactions and capture trade-offs among the time use-travel-land use choices, this study proposes a joint discrete choice model by introducing unobserved factors that are common to them. The model results confirm the presence of complex interdependencies among these choice dimensions. In addition, this study highlights the contribution of observed and unobserved factors introduced in the model by calculating the total variance of utility differences. Lastly, this study provides behavioural insights on the activity-travel patterns, which can be further used to develop robust travel demand management policies.

Subsidizing residents or companies? An equilibrium-based analysis of subsidy strategies for EV charging facilities

This study examines the charging location choice behavior of residents in relation to employer-provided charging facilities and investigates the impact of different government subsidy strategies for charging facility construction on urban spatial structure and traffic-related air pollution. Using a monocentric two-zone city model, we analyze the residential distribution and travel mode choice of urban residents under various subsidy strategies from the perspective of social planners. The government can optimize urban resource allocation through transport infrastructure investments and implementation of emission taxes to maximize the so-called residents’ utility. We observe that despite the distorting effect of employer-provided charging facilities on travel mode choice, residents still experience an enhanced utility due to the positive environmental impact resulting from reduced pollution levels associated with electric vehicles (EVs) compared to gasoline vehicles (GVs). We provide numerical evidence to show that government subsidies, whether directed towards companies or residents, can effectively enhance the adoption of EVs and improve utility. In low-density cities, it is advisable for the government to encourage and subsidize residents in building charging facilities. Conversely, in high-density cities, prioritizing subsidies for companies would be more beneficial in improving utility and expanding the coverage of charging facilities. Furthermore, in cities where resident environmental awareness is relatively low, it is recommended that the government increase subsidies to promote EV adoption.

Regression approach to analyze the travel characteristics of university students

ABSTRACT This study at the University of Engineering and Technology (UET), Lahore, examined students’ travel behavior, mode choice and constraints. Data from 1,449 students were analyzed using various statistical methods, including descriptive analysis, latent class analysis (LCA), multinomial logistic regression (MNL), correlation, and linear regression. The model’s accuracy was evaluated by calculating the percent error. Walking was the most popular mode of transportation within the campus, preferred by 37% of students. Most students traveled 1-15 km, taking 10-15 minutes, with off-campus students typically making three trips to their destination. Safety concerns significantly influenced 37% of students’ travel behavior due to built-in constraints.

A study on commuters’ public transportation mode choice behavior in river valley-type cities considering terrain spatial perception: evidence from Lanzhou, China

Existing research rarely examines the subjective and objective built environment of river valley-type cities in relation to travel mode choice, particularly overlooking the heterogeneity among travelers in these cities. In this paper, based on questionnaire survey data and built environment data, terrain spatial perception (TSP) is introduced to expand the theory of planned behavior (TPB), and a Structural Equation Model (SEM) is established. Factor analysis and path analysis are conducted using SPSS and AMOS to estimate latent variables. An integrated model of SEM and random parameter Logit model (RPLM), which can not only analyze the psychological perception factors of commuters in river valley-type cities but also consider the heterogeneity of psychological perception, was constructed to analyze the impact of personal attributes, objective built environment factors, and psychological latent variables on the commuting mode choice behavior of public transport users in river valley-type cities. The results indicate that the five observation indicators corresponding to the proposed terrain spatial perception latent variables can better explain the terrain spatial perception of commuters in river valley-type cities. Different from plain cities, the subjective and objective built environment of river valley-type cities notably influence the travel behavior of commuters. Moreover, the parameters of terrain spatial perception follow a normal distribution, indicating that the sensitivity of different commuters to the terrain spatial perception of river valley-type cities is heterogeneous. The results of our study can provide a reference for alleviating traffic issues in valley cities.

Coupling graph neural networks and travel mode choice for human mobility prediction

Travel mode serves as the link for communication among people, between people and objects, and between people and places. The human mobility is closely related to travel mode. Scientifically predicting human mobility can help alleviate traffic congestion and provide flexible travel choices. However, in current predictions, only the travel demand or human mobility is taken into account, while the influence of residents on the travel mode choice is neglected. Therefore, taking Wuhan City as an example, this paper proposes a new method for predicting human mobility by employing graph neural network techniques and travel mode choice behavior. The prediction model established in this study utilizes network analysis to measure the accessibility time and road network distance of traffic travel mode. The graph neural network method is employed to capture the dynamic temporal and spatial relationships underlying human mobility. Furthermore, the performance of the prediction model is evaluated. The results indicate that at a fine spatial scale, the new method can more accurately reveal the spatial patterns of changes in human mobility, significantly improving the accuracy of predicting human mobility.

Non-linear effects of children's daily travel distance on their travel mode choice considering different destinations

The scientific layout of child-friendly facilities in the community is of great significance to children's daily activities and healthy growth, which is also a vital concern of the United Nations. However, there is little evidence to prove the intrinsic mechanism of children's daily travel behavior and daily travel destinations. Therefore, this study takes 118 communities in Shuangliu District, Chengdu, as an example and uses a eXtreme Gradient Boosting (XGBoost) model to explore the non-linear effects and threshold effects of children's daily travel distance on their travel mode choices by considering different destinations of children's daily travel in the communities. We found that children's daily travel distances and destinations are vital for their travel mode choice, especially the most significant effect of travel distance. For different children's daily travel destinations, travel distance has a more substantial threshold effect on children's non-walking travel mode choice. Therefore, to scientifically and rationally distribute child-friendly facilities in the community, which may help children's healthy development, a child-friendly city should be planned, constructed, or renewed through the internal logical relationship between children's daily travel destinations, travel distances, and travel mode choices should be considered comprehensively.

Modeling travel mode choice behavior for planned special events using CPT-based HI-MADM approach

ABSTRACT Planned special events (PSEs) can significantly increase traffic demand, posing greater challenges than daily operations. In response, host cities often invest heavily in transportation upgrades. This study proposes a model integrating cumulative prospect theory (CPT) and multiple-attribute decision-making to understand mode choice behavior of PSE attendees. This integration enables direct extraction of traveler preference information, offering a more suitable description of the decision-making process. The decision matrix considers mode and attribute heterogeneity. The reference point for CPT is derived from previous PSE trips or commutes. Furthermore, this paper assesses asymmetric nonlinear sensitivity and distorted perceptions of different attributes. Analysis of mode-choice behavior for PSE participation, using survey data from Tianjin, China, validates the approach. It better characterizes PSE choice behaviors and demonstrates varying sensitivity to gains and losses, dependent on different attributes and distances. Thus, event planners should prioritize improving travel time and convenience to increase public transport mode share.

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces.

The built environment's impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people's travel experience and travel mode choice. The streets' built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device's power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.

Oldest old's travel mode choice and new mobility technology acceptance: case in America and China.

The oldest olds (aged 85 and over) are the fastest-growing age segment. However, our understanding of their mobility is limited. To address this gap, we invited 19 U.S. and 30 Chinese "oldest old" to take part in focus groups and complete a mobility questionnaire. We focus on travel mode choice, which includes changes in travel modes, frequency of usage, and perceptions of comfort. Older adults' familiarity and acceptance of new mobility technologies (e.g., ridesharing, carsharing, and autonomous vehicles) were measured by questionnaire and focus group. Word clouds were also used to illustrate people's reasons for choosing their primary mode of transportation. The results show that both panels of older adults similarly feel some extent of travel limitations. But the responses among the two groups differ: 18 American participants chose "drive myself" as their primary option a decade ago, while 11 chose it now; no Chinese participants selected it either a decade ago or now. Both currently and 10 years ago, there was a significant difference in mode choice between participants in China and the United States. However, this gap has narrowed over the past decade. Participants in China have significantly changed their transportation preferences compared to 10 years ago, while participants in the US have remained nearly unchanged. American respondents consider "ease" as an important factor, while Chinese respondents pay more attention to "safety" and "no other option to get around" when making travel mode choices. Compared to Chinese participants, American participants were more comfortable with driving an autonomous vehicle. These differences may result from the various developmental stages and transportation policies of the two countries. This study supports the development of new mobility technologies for the oldest old to improve their quality of life.

Evaluating Spatial Accessibility to COVID-19 Vaccination Sites based on Fine-Scale Population Distributions and Heterogeneous Travel Modes: A Case Study in Xiangtan, China

Understanding the inequity in spatial accessibility to COVID-19 vaccination sites is beneficial for the government to optimize vaccine resources. Nevertheless, the result of evaluating spatial accessibility to COVID-19 vaccination sites may be directly affected by the lack of fine-scale and accurate population distribution information in both urban and rural areas. In addition, the choice of travel modes related to different age groups has not been incorporated into existing methods well. Consequently, this study proposes a framework to evaluate spatial accessibility to COVID-19 vaccination sites based on fine-scale population distributions and heterogeneous travel modes. A divide-and-conquer strategy is first presented to estimate the fine-scale populations in both urban and rural areas. A hybrid time distance considering heterogeneous travel modes and a population-weighted proximity indicator are then defined to evaluate spatial accessibility for each age group. The experimental results from Xiangtan, China, show that (1) the divide-and-conquer strategy can effectively predict the urban and rural populations. (2) Similar clustering patterns of spatial accessibility are observed for each age group. (3) Inequities have been confirmed at the county level. The main findings in this study can provide valuable information supporting the spatial optimization of existing COVID-19 vaccination sites.

Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis

AbstractPredicting and understanding travellers’ mode choices is crucial to developing urban transportation systems and formulating traffic demand management strategies. Machine learning (ML) methods have been widely used as promising alternatives to traditional discrete choice models owing to their high prediction accuracy. However, a significant body of ML methods, especially the branch of neural networks, is constrained by overfitting and a lack of model interpretability. This study employs a neural network with feature selection for predicting travel mode choices and Shapley additive explanations (SHAP) analysis for model interpretation. A dataset collected in Chengdu, China was used for experimentation. The results reveal that the neural network achieves commendable prediction performance, with a 12% improvement over the traditional multinomial logit model. Also, feature selection using a combined result from two embedded methods can alleviate the overfitting tendency of the neural network, while establishing a more robust model against redundant or unnecessary variables. Additionally, the SHAP analysis identifies factors such as travel expenditure, age, driving experience, number of cars owned, individual monthly income, and trip purpose as significant features in our dataset. The heterogeneity of mode choice behaviour is significant among demographic groups, including different age, car ownership, and income levels.

A machine learning approach for unraveling the influence of air quality awareness on travel behavior

Urbanization has escalated air pollution levels with subsequent health implications. This study explores the potential of awareness about air quality levels on travelers’ choices and proposes machine learning models to predict travel mode under exposure to different air quality levels. These models are Random Forest, XGBoost, Naive Bayes (NB), K-Nearest Neighbor, Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR). The models are trained using data from individuals who have an understanding of air quality levels. The trained model is further used to predict travel mode choices when the knowledge of air quality reaches all travelers. Travel modes are aggregated into open/closed modes, private/public modes, and motorized/non-motorized/metro modes to assess the impact of air quality awareness and modal shift. The model evaluation shows that the Random forest (RF) exhibits the highest accuracy and F1 score. The model demonstrates that as air quality worsens, commuters shift their preferences from open modes of transport to closed modes. Similarly, during periods of deteriorating air quality, commuters exhibit a preference for public transportation over private modes. This study emphasizes the crucial role of disseminating air quality information, empowering individuals to make informed travel decisions, and mitigating health risks.

Group model building for developing systems-oriented solutions to reduce car dependency in Belfast, United Kingdom

ABSTRACT The continued prevalence of car dependency is a concern worldwide due to known negative health, social, economic, and environmental impacts. Belfast is a city in the United Kingdom with high levels of car dependency and car use. The most successful interventions to reduce the complex problem of car dependency combine a range of approaches, but how the influencing factors work together and interact to successfully achieve a change in car dependency is poorly understood. This study aimed to better understand the factors that influence the high levels of car dependency in the Belfast Metropolitan Area. We worked with 17 stakeholders to co-design a causal loop diagram to understand the complex system of car dependency in the city. The causal loop diagram reflects a shared understanding of car dependency and highlights the various mechanisms that are important for reducing car dependency. Twenty-three factors under the four themes of policy, infrastructure, economics, and social norms were identified and four key feedback loops between these themes are discussed. It is shown how individual behaviour in relation to travel mode choice is influenced by system-wide factors that, in the Belfast context, make car use the easiest and most convenient choice for the majority.

Determining causality in travel mode choice

This article presents one of the pioneering studies on causal modeling in travel mode choice decision-making using causal discovery algorithms. These models are a major advancement from conventional correlation-based techniques. We propose a novel methodology that combines causal discovery with structural equation modeling (SEM). This modeling approach overcomes some of the limitations of SEM by combining the strengths of both causal discovery and SEM. Causal discovery algorithms determine causal graphs from observational data and domain knowledge, and SEMs estimate direct causal effects and test the performance of causal discovery algorithms. In this study, we test four causal discovery algorithms: Peter-Clark (PC), Fast Causal Inference (FCI), Fast Greedy Equivalence Search (FGES), and Direct Linear Non-Gaussian Acyclic Models (DirectLiNGAM). The results show that DirectLiNGAM based SEM model best captures causality in mode choice behavior. It passes several goodness-of-fit tests, including Root Mean Square Error of Approximation (RMSEA) and Goodness-of-Fit Index (GFI), and it achieves the lowest Bayesian Information Criterion (BIC) value. The analyses are conducted on data collected from the 2017 National Household Travel Survey in the New York Metropolitan area.

Satisfaction-induced travel: Do satisfying trips trigger more shared micro-mobility use?

Most studies on travel satisfaction assumed it as an outcome of travel choices. However, travel choices may also be affected by people’s satisfaction with travel. Ignoring this potential reverse effect will lead to an biased understanding on the link between travel behavior and subjective wellbeing. This research examined the influence of travel satisfaction on travel behavior, using questionnaire survey data on shared micro-mobility services in three European cities in 2022. The research findings suggest that travel satisfaction can serve as both pull factors and push factors of travel mode choice. A high satisfaction with shared micro-mobility trips encourages people’s future use of these services. A relatively low satisfaction with daily travel also prompts the non-users to try shared bikes/e-bikes as alternative transport modes. Our research provides direct evidence on the feedback effect of travel satisfaction on travel behavior.

Travel behavior and system dynamics in a simple gamified automated multimodal network

Automated Vehicles (AVs) are poised to disrupt travel patterns and the sustainability of transportation networks. Conventional methods for studying these changes, such as stated preference surveys and agent-based simulations, have limitations. Serious games offer a promising alternative, providing a controlled and engaging environment for investigating travel behavior. In our study, 200 participants, grouped into sessions of 10, engaged in a competitive serious game simulating 50 daily choices of travel mode and departure time across three automated options. Two scenarios were examined: one with recurring congestion and another with nonrecurring congestion. Automated transit had fixed schedules, while private and shared rides could adapt to a congested bottleneck. Results revealed that ridesharing dominated, reaching 60% mode share under recurring congestion, displacing transit, and a comparative equilibrium emerged between shared and private rides. In the nonrecurring congestion scenario, ridesharing dropped to 37%, and a comparable multimodal equilibrium developed. Participants rarely achieved the optimal score, attaining a maximum of 88% of its potential. This study highlights a policy paradox: unregulated AV traffic can reduce transit use, exacerbate recurring congestion, yet necessitate increased transit investment to address nonrecurring congestion, confirming the Downs-Thomson paradox. Creating appealing mass transit alternatives is imperative to ensure efficiency and sustainability in the era of automated mobility.

Characteristics of Travel Mode Choice of Families with Children Below Five Years Old

Travel mode choices for children under five years old have not been fully explored in past research studies The main objectives of this study were to identify travel characteristics of children aged below five years, travel mode choices for them, and investigate the factors affecting their selection. A survey using a questionnaire was conducted with parents in the Western Province of Sri Lanka to collect data. It revealed that private cars as the most frequent vehicle choice followed by public buses. The Multinomial Logistic Regression analysis identified the age of the child, distance, income, type of vehicle owned, and walking time to the nearest public transport station as the main factors that affect the travel mode choice of children under five years old. Also, the habit of securing the child and the child’s familiarity with the use of a car seat when traveling in a private car were identified as factors that affect travel safety. The recommendations were provided to policy makers, parents, and the public in order for children to make safer, comfortable, economical, and sustainable trips. KEYWORDS: Travel Mode Choice, Multinomial Logistic Regression Analysis, child travel safety, child travel behavior