Travel Mode Choice Research Articles

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

Lognormal distribution of daily travel time and a utility model for its emergence

Understanding the nature of human movement is important in many fields, such as transportation management, urban planning, and epidemiology. In this study, we focus on travelers who are grouped by their travel modes in the Tokyo Metropolitan Area. Notably, for each group of travelers, the distribution of daily travel time follows a lognormal distribution. For any travel mode, the distributions of normalized travel time for different years (1968–2008) and distances to the city center converge to a curve, although the average travel time and share of travel modes vary. By employing the concept of utility, we developed a disaggregate model wherein an individual maximizes utility from traveling (=benefit - cost) by modifying daily travel time. This model theoretically explained the observed distributions of travel time. The model was further used to demonstrate travel mode choices. The model parameters were estimated using the distribution of travel time, travel mode share, and manageable assumptions. These results provided interesting interpretations of the perceptual utility of the daily travel time of a population. Moreover, the obtained curves of utility versus time are similar to the shapes of the overall benefits assumed in previous studies. Overall benefit is a concept that has been developed and examined since the 1980s, and our study provides a mathematical foundation for this idea.

Non-linear effects of built environment factors on mode choice: A tour-based analysis

Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.

Robust optimisation for vertiport location problem considering travel mode choice behaviour in urban air mobility systems

With the rapid development of novel vehicle technologies, air taxis are becoming a new transport servicing model to achieve better urban air mobility (UAM) systems. The UAM system can mitigate severe traffic congestion problems on the ground in many metropolitan areas effectively. This research aims to design a robust UAM network to serve broader mobility needs in the future. In this study, we consider the two main competition transportation modes of air taxis (luxury ground taxis and ordinary ground taxis) and establish a travel mode choice behaviour model based on the multinomial logit choice approach to determine the aggregate air taxi demand flow. Furthermore, we account for the varying temporal preferences of different UAM passengers by incorporating different time values for heterogeneous passengers, such as leisure and business passengers, into the travel mode choice behaviour model. Additionally, to address uncertainties in user demand, we introduce a robust optimisation model for vertiport location under a budget uncertainty set. This model is then reformulated as a mixed-integer linear programming model to address computational challenges. Ultimately, we conduct a series of numerical experiments to showcase the effectiveness of our proposed mathematical model. The outcomes of this research offer valuable managerial insights and implications, aiding governments and UAM operators in scientifically designing UAM networks and making strategic decisions regarding infrastructure investments.

Tourists' urban travel modes: Choices for enhanced transport and environmental sustainability

This research investigates tourists’ urban travel mode choices in prominent Chinese tourist cities using latent class nested logit modelling. It identifies Comfort-Driven and Economy-Driven market segments, each exhibiting unique preferences towards transport-related factors as measured by elasticity and willingness-to-pay. Travel cost, walking time, waiting time, transfer time, crowdedness, and parking difficulty significantly affect tourists’ urban travel mode choices. Interestingly, car ownership and environmental consciousness have also been proven significant for both segments, while in-vehicle time is only significant for Comfort-Driven segment. The Comfort-Driven segment shows higher willingness-to-pay for reduced walking and transfer times, and prioritising comfort. Conversely, the Economy-Driven segment is more price-sensitive, focusing on value-for-money options that minimise both monetary and non-monetary costs. This study enhances the theoretical understanding of tourists’ transport service consumption and offers practical guidance with tailored strategies for encouraging low-carbon public transportation, hence it could facilitate the alignment of transport, tourism, and environmental sustainability goals.

Spatial Justice of Urban Park Green Space under Multiple Travel Modes and at Multiple Scales: A Case Study of Qingdao City Center, China

Improving the justice of public parks is of great significance to the well-being of residents, and it is also an important goal of green space planning. In this paper, the spatial justice of park green space under five travel modes and at three scales was analyzed using the travel-behavior-based Gaussian two-step floating catchment area method (TB-G2SFCA) and Gini coefficient method for Qingdao City Center. The main results are as follows: Under walking mode, walking–bus mode, and walking–subway mode, there were unserved areas in terms of urban park green space, while there were no unserved areas in the cases of cycling and driving. Residents’ choice of travel time and travel mode would affect the service scope of the park green space, and the increase in travel time would reduce the unserved areas in the urban park green space. The choice of travel time and travel mode affected the accessibility of urban park green space for residents in each residential patch, as well as the justice of the distribution of park green space resources at the scales of street blocks, districts, and the whole study area. The increase in residents’ travel speed and travel time could promote the equitable allocation of urban park green space to a certain extent. The results of this study provide a scientific basis for the planning and construction of urban park green space in Qingdao City.

Cycling, Walking, And Public Transport Versus Private Cars: An Empirical Investigation Of Travel Mode Choices For Shopping Trips In Mumbai Metropolitan Region

Cycling, Walking, And Public Transport Versus Private Cars: An Empirical Investigation Of Travel Mode Choices For Shopping Trips In Mumbai Metropolitan Region

How does multi‐modal travel enhance tourist attraction accessibility? A refined two‐step floating catchment area method using multi‐source data

AbstractIn the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities.

Statistical modelling of travel mode choice of public university students in Freetown, Sierra Leone: the case of three campuses

ABSTRACT Though much research has been conducted on students’ travel behavior around the world, not much is known about university students in Sierra Leone. This study investigates the most common transport modes amongst public university students, the factors influencing their preference, and the impact of their choice on the environment. Through a Revealed Preference survey, using the traditional paper-and-pencil method combined with a smartphone-assisted interviewing technique, 632 students were sampled. Discrete travel choice models were developed and estimated, and the methodology was demonstrated for the three campuses under the University of Sierra Leone. The study found that more than one-third of students preferred the paratransit mode, ‘podapoda’, followed by taxi, two and three-wheelers. According to the mode choice model results and exploratory analysis, sociodemographics, travel time, cost, and trip distance are the main factors considered by students’ for selecting a transport mode. Students varied widely in their assessment of how their mode choice affects the environment. These findings highlight the need for a sustainable transport system for university trips. Acquiring appropriate transport modes, promoting active travel, and developing student residences with shorter commutes will ensure long-term land management. This will promote the sociocultural, economic, educational, and ecological aspects of campus growth.

Modeling the joint choice behavior of commuters’ travel mode and parking options for private autonomous vehicles

Difficulty in finding parking spaces and high parking fees discourage private car usage. Fully autonomous vehicles (AVs) capable of self-parking away from destinations will likely remove this barrier. Despite extensive survey-based research on AVs in recent years, existing literature has not sufficiently addressed the potential impact of new parking options on the demand for these vehicles. This study explores commuters’ joint choice of travel mode and parking for private autonomous vehicles (PAVs). To this end, a stated choice (SC) experiment was designed and deployed in the city of Beijing, China. Attitudinal statements were also designed to measure four latent variables: perceived ease of use, perceived usefulness, perceived safety, and attitude toward waiting. Using a hybrid choice model framework, the estimation results reveal that the choice of letting the PAV self-park at a non-destination location is significantly influenced by the location of such parking, the potential delay in re-taking the vehicle, and the fuel/energy consumption to and from the non-destination parking place. Attitudes toward AVs also play a crucial role, with perceived safety and perceived usefulness having the greatest impact. Our results can help managers and planners understand how PAVs affect people’s travel mode choices and the corresponding parking options and assist them in developing strategies in preparation for the widespread use of AVs.

Does it matter if you like it? exploring the relationship between travel mode choice, preference, and satisfaction

Understanding the level of dissonance between travel mode and preference and its relationship with satisfaction can help develop transport strategies that encourage the use of sustainable modes. We study the difference in satisfaction levels for work and school trips of consonant travellers and dissonant travellers. The research uses a large-scale (N = 1,865) travel survey administered in Montreal, Canada. A binary logistic regression model reveals that both consonant and dissonant commuters have a high probability of satisfaction with their commute, except for dissonant car users. We find that consonant pedestrians have the highest probability of satisfaction when compared to all other groups, and that dissonant car users have the lowest probability of satisfaction. We further investigate the reasons preventing the use of preferred modes for dissonant car and transit users. Findings from this research help inform researchers and practitioners aiming to make sustainable mode choices the preferred one among travellers.

Assessing Impact Factors That Affect School Mobility Utilizing a Machine Learning Approach

The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified.

Urban Mobility Mode Shift to Active Transport: Sociodemographic Dependency and Potential Greenhouse Gas Emission Reduction

The aim of this study was to explore the association between travel mode choice and individual sociodemographic characteristics among urban city dwellers, as the selection of daily travel modes is influenced by several factors. The study collected 1,290 short daily trips data from 415 respondents living in Klang Valley using a random sampling technique. Logit regression models were utilized to identify the impact of sociodemographic traits on travel mode choices. Men, low education levels, students, and households without children and do not own private vehicles are more likely to choose active transportation. Besides, the study examines the potential for greenhouse gas (GHG) emission reduction. Based on Life Cycle Assessment (LCA), 142 kgCO2e or 108 kgCO2e/km of GHG were released by automobiles from the collected travel trip data. The result shows that active transport could potentially achieve 14.52% GHG emission reduction by stated preference and nearly 17% GHG emission reduction by criteria fulfillment. These findings could provide valuable information for developing practical planning policies aimed at reducing GHG emissions from the road transport sector.

Evolutionary dynamics of the travel mode choice behavior based on the replicator-mutator model

Evolutionary dynamics of the travel mode choice behavior based on the replicator-mutator model

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 INFLUENCE OF TRAVEL MODE CHOICE ON SUBJECTIVE WELL-BEING - A CASE STUDY

The correlation between mobility and subjective well-being (SWB) has received much attention lately. Previous researchers have studied the effect of health parameters or SWB on transport mode; however, there is a lack of study on the influence of travel mode choice (TMC) for daily activities on SWB. Besides, the prediction of TMC is critical for transport planning. Therefore, the current study aims to study the TMC and its influence on overall SWB. Data from 732 individuals and 191 households are collected using random sampling techniques, which represents 0.029% of the total population. Statistical Package for Social Sciences (SPSS) was used for descriptive statistics, whereas R software was used for the multilevel linear regression analysis. The model estimation results show a significant correlation among the variables (p < 0.05, R2 > 0.20). Besides, those who are exposed to public transport and tend to use non-motorized transport modes engage in more physical activities than those who use a private vehicle, which has a negative impact on SWB. The outcome of current research helps policymakers build policies to achieve a sustainable transportation system.

Assessment of the Current Bus Route Network in the Federal Capital City Abuja, Nigeria

The inadequacy of buses for the public transportation system in the Federal Capital City (FCC) of Abuja has resulted to the dominant use of private automobiles, which has created a lot of problems, such as, long waiting times, lateness to work, kidnapping and loss of man-hours. The effort of Government in providing high capacity public buses through Abuja Urban Mass Transit Company (AUMTCO), has not been satisfactory. Therefore, in this study, an evaluation of the existing bus route networks and the availability of public transport bus services in the FCC, including an assessment of the factors that determine the present choice of travel mode in the City is presented. The methodology adopted includes: manual traffic volume count, measurement of traffic data and scope of the current bus route network using GPSmap76Cx (GARMIN) receiver, and questionnaire administration. The results show that public bus is 0.12% of vehicular traffic volume in the metropolitan city, while private car has 86.81%, which carries significant consequences that impact different facets of its citizens' lives. The existing bus routes network does not cover Wuse II and Maitama districts. Mode choice influences by social demographic characteristics, such as; occupation, marital status, time spent going to work, bus inconvenience, time to leave home for work, and decision to patronize public mode if available are among the trip factors assessed. The mean peak travel speed on the bus routes ranges from 15km/hr to 23 km/hr, while average time spent from house to and at bus stop are 10 minutes and 20 minutes respectively for public bus. The results suggest that the existing bus route networks should be optimized to cover all the districts of the Federal Capital City for effective accessibility and services to commuters. This will improve the security indices of commuters in the Federal capital city.

Spatial-temporal behaviour of hikers in the southeastern margin of Qinghai-Tibet Plateau: insights from volunteered geographic information

Understanding the macroscopic spatial-temporal behaviour of hikers plays an important role in targeted planning, marketing and management for regional hiking tourism development. The southeastern margin of Qinghai-Tibet Plateau, with abundant outdoor tourism resources and several World Heritage Sites, was chosen as the study area, and the development characteristics of hiking tourism and the spatial-temporal behaviour of its hikers were explored by seasonal intensity index, kernel density analysis, line density analysis and K-means clustering with volunteered geographic information (VGI). The results showed that hikers differed significantly from traditional tourists in terms of travel time, travel mode and destination choice, which provide vital implications for sustainable policy-making and tourism management in general and in the southeastern margin of Qinghai-Tibet Plateau in particular.

The relationship between the degree of ethnic enclaves and travel mode choices

ABSTRACT This article aims to highlight how the degree of ethnic enclaves plays an important role in travel mode choices according to racial/ethnic groups in the Atlanta metropolitan areas in the US. This study finds that ethnic enclaves play a different role in travel mode choices according to the degree of ethnic enclaves, racial/ethnic groups, and travel purposes. For example, white people in low enclaves are more likely to take public transit for work (0.390), and black people in high enclaves are more apt to use household carpool, inter-household carpool, public transit, and walk/bike for school (1.545, 1.725, 1.205, 1.659, respectively) and public transit and walk/bike for other purposes (0.273 and 0.233, respectively). Asian people in high enclaves are more inclined to take household carpool for leisure (3.480), and Hispanic people in low enclaves are more likely to use inter-household carpool for shopping (6.377).