Public transport ticket distribution increasingly combines operator-controlled channels with reseller services, integrated ticketing systems, and Mobility-as-a-Service (MaaS) platforms. Existing research primarily emphasizes interoperability and coordination challenges but provides less explanation of when intermediary participation becomes commercially attractive and how alternative ticketing arrangements affect operators and passengers.This paper compares four ticket-distribution arrangements: direct distribution (D), reseller distribution (R), decentralized hybrid systems (RD), and coordinated platform systems (PD). The analysis focuses on several recurring mechanisms observed in public transport systems, including market expansion, channel overlap, intermediary-related burdens, and retained operator revenues.Intermediary participation becomes commercially attractive only when broader market access and lower booking frictions generate sufficient additional demand to compensate for commissions, settlement requirements, and transaction-related burdens. Once intermediary channels become viable, a different question emerges: how should direct and intermediary channels operate when both coexist? Stronger overlap increasingly redirects existing passengers across channels rather than generating genuinely new demand, weakening operator incentives and reducing the value of parallel ticket structures.Passenger and operator incentives do not necessarily evolve similarly. Integrated ticketing and MaaS environments can continue improving accessibility, multimodal access, and booking convenience even when operators retain smaller shares of intermediary-generated revenues. The incidence analysis suggests that passengers account for the largest share of welfare improvements, while operators continue contributing meaningfully and intermediary gains remain comparatively limited. Under the benchmark environments considered, disagreement between commercial and welfare outcomes primarily reflects differences between passenger and operator incentives rather than substantial intermediary rent extraction.Ticket-distribution systems therefore influence more than where tickets are sold. They determine how revenues, customer access, settlement responsibilities, and transaction activities are distributed across public transport systems.

Research on individuals’ participation in being a driver for ride-sourcing companies is rare and this decision has not been fully understood. This study uses stated choice experiment data collected in China through face-to-face interviews to explore the effects of job conditions, car ownership and socio-demographics on individuals’ decisions to become a driver for ride-sourcing companies. The results show that car owners and those who work in the tertiary industry have a higher interest in driving for a ride-sourcing company beyond regular working hours, but less interest in becoming a full-time driver. Social insurance is an important trigger of participation for individuals without a stable job. Middle-aged people have an interest in driving and prefer full-time driving over part-time driving or driving after work. These findings provide useful insights for improving driver recruitment and ride-sourcing sustainability.

This research focuses on multiple class association rule (MCAR) approach to study travel behaviour. In MCAR approach, how to utilise a top-k rule team set to build an effective classifier remains a challenge for travel behaviour research. Another challenge is that each top-k rule team has only one class label, which may lead to a few observations being predicted incorrectly. To tackle the challenges, this research utilises a CNN model and similarity between observations (SBOs) to obtain multiple class labels for a top-k rule team. Finally, a set of top-k rule teams with multiple class labels is utilised for developing a similarity-based MCAR (S-MCAR) model to predict transportation mode choices. To evaluate the effectiveness of the developed model, a travel dataset and 5 comparison models are utilised. In addition, in the S-MCAR and comparison models, a ten-fold cross-validation method and a grid search method are applied.

This paper investigates heterogeneity in the relationship between different life domain satisfactions including travel satisfaction and overall life satisfaction using data collected in Xi’an, China. A latent class structural equation model of the relationship between life domain satisfaction and overall life satisfaction is estimated to identify heterogeneous segments of respondents, controlling for the effects of socio-demographic characteristics, personality traits and typical commuting time on the latent variables. The results indicate that the segments differ in the weight attached to the different life domains. Interestingly, the direct effect of travel satisfaction on overall life satisfaction is not significant in the identified classes, but indirect effect of travel satisfaction on overall life satisfaction through other life domains is significant. It emphasizes the critical importance of estimating a more comprehensive model of life satisfaction when assessing the role of travel satisfaction.

Rapid improvements in autonomous driving technology and the availability of autonomous vehicles (AVs) are expected to change people’s habitual travel patterns. Fully autonomous vehicles (FAVs) do not need to be maneuvered by their users, implying users are allowed to participate in a number of non-driving in-vehicle activities (IVAs) when their FAV is bringing them to their destination. People can therefore use their travel time for working, relaxation, entertainment, communication and possibly other activities. Since FAVs provide a different environment than traditional travel modes, such as trains and busses, people’s preferences for conducting IVAs in FAV travel has become an emerging issue in transportation research. Understanding people’s preferences for conducting IVAs during FAV travel will generate important information for future vehicle interior design and the development of transportation policies. Hence, this paper presents the outcomes of a research study that aims at increasing our understanding of the intentions of individuals to conduct IVAs when travelling by FAV’s and the endogenous and exogenous factors and variables influencing these intentions. We designed an experiment and analyzed the response data using simultaneous equation modeling to examine the intentions to conduct IVAs during FAV travel and potential correlations that may exist across IVAs. The results show significant heterogeneity in IVA intentions and correlations between IVAs. Youngsters, high-education-level groups, and employed show a higher intention to engage in most IVAs. In addition, gender, household income, motion sickness, and license ownership affect people’s intentions. The estimated results suggest that the intentions to conduct IVAs depend on trip length. Moreover, the potential correlation between IVAs is confirmed. For example, respondents who have intentions to conduct to sleep show interest in eating or drinking and play games, but are not inclined to work with a computer. In contrast, respondents who intend to use social media during FAV travel are less likely to sleep when travelling by FAV.

E-bikes, shared and new mobility services such as Mobility-as-a-Service (MaaS) are emerging as sustainable and healthy alternatives to private cars, introducing complexities in household mobility decisions and potential substitution between transportation modes and services. However, existing studies primarily examined the potential long-term adoption of these emerging mobilities separately, leaving a gap in understanding the interplay among various emerging mobilities and conventional cars. This study therefore addresses this portfolio choice incorporating a stated portfolio choice experiment encompassing pedelecs, speed pedelecs, MaaS, Shared e-Mobilities, and electric and conventional cars. Results from a random effects error component mixed logit model, based on an online survey conducted in the Netherlands, indicate significant availability effects of shared and new mobility services on personal mobility ownership decisions, and a substantial demand for pedelecs. The findings contribute to facilitating the adoption of emerging mobilities with enhanced synergy, as shared and new mobility services are gradually becoming available.

To develop effective strategies for the supply of shared parking, the present study investigates factors influencing the willingness of private parking space owners to engage in shared parking. Apart from the attributes of shared parking options, unobserved latent variables measuring attitudes and personality traits, are assumed to play a role in the decision-making process. This study estimates a hybrid prospect theoretic model to investigate the willingness of parking space owners to share their parking space. The latent variables include personality traits and attitudes that are incorporated into a prospect theoretic choice model. Non-linear effects of the latent variables and the interaction effects between personality traits and attitudes are examined. Results indicate that non-linear effects and interactions significantly improve the overall explanatory power of the model. The research findings may help in developing shared parking policies and informing companies and governments how to promote shared parking schemes.

Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules that have high confidence but low support, the information gain (IG) concept is introduced in the model building process. The modeling process in this study first involves extracting frequent items from the data using the FP-Growth algorithm and deriving CARs from these frequent items. Next, the IG statistic is used to construct a novel model (named CARIG), which consists of a set of decision rules that formally represent behavioral scripts, for predicting individuals’ transportation mode choice. The performance of CARIG is compared with the performance of conventional class-based association rules (CBA), decision trees (DT), a convolutional neural network (CNN) and a logistic regression (LR) model. In addition, a 10-fold cross validation test using a grid search parameter optimization method is conducted to validate the proposed approach. The results show that the proposed method is promising in predicting transportation mode choices observed in the national travel survey data.

Shared parking is viewed increasingly important as a way to alleviate parking problems in urban areas. To maximize the effect of shared parking initiatives, it is critical to understand the decision of households to share private parking spaces. Current models of household decision-making fail to adequately address equity seeking/avoiding household dynamics, which may negatively affect model validity. In this study, a model, which overcomes this theoretical concern, is introduced and estimated to understand the household shared parking participation decision. Specifically, the concept of leadership personality is used, jointly with individual and household characteristics, to specify the decision weight of each spouse of a couple. A choice experiment, in which individual members of couples first answer the choice questions individually and independently, and then jointly complete the choice questions, is designed to estimate the model. Estimation results, based on data collected in Qingdao, China, support the proposed model. Results show that intra-household interactions influence the households’ shared parking participation decision and that households favor alternatives that provide higher equality. Age, leadership personality, household structure, and household financial management are significantly related to household member decision weights.

Physical inactivity remains a global public health challenge today. Determining why people stop regularly participating in sports is significant to develop targeted intervention strategies for sports promotion and healthy living. As sports participation is dynamic throughout life, a life-course perspective is needed to provide a more comprehensive understanding. This study adopts a life-course perspective to explore the determinants of the change from active participation in sports to becoming inactive. Based on online retrospective survey data collected in the Netherlands, a two-level binary logistic regression model is estimated to capture the effects of socio-demographics, sports motivations, life transitions, and neighborhood characteristics on sports dropout over the lifespan. Results show that dropout from sports is age-specific, and that people are less likely to discontinue sports participation when they have health and weight loss goals. Life transitions have different effects. The cessation of living with physically active people appears to be the most important event to make people stop sporting, followed by having a baby, and then owning the first car. Compared with education-related events, work-related events are more likely to cause people to stop sporting. Moreover, the probability of sports discontinuance may increase when residents feel unsafe doing physical activities in their neighborhoods or when the neighborhood has sufficient greenspace for walking. The findings have implications for supporting sports participants to continue exercising by addressing the barriers.

Demand responsive transport (DRT) although existed for decades, has recently become more attractive due to availability of real time (demand and supply) data and advanced matching algorithms. DRT is advantageous in reducing traffic and space occupancy if each service is simultaneously used by multiple travelers. Despite its benefits, travelers' willingness to adopt this transport service is essential for such a service to have a meaningful impact on the living environment. Apart from service characteristics such as travel cost, travel time, waiting time and convenience, the uncertainty involved in the service delivery can be an additional factor for travelers no to be eager in using such a service. In this study, a web-based stated adaptation experiment is designed to understand the travelers' choice of DRT in different contexts. Stated adaptation choice experiment first collect travel history and then expose respondents to two DRT options designed on the basis on the reported trip characteristics. A regret- rejoice based model is estimated to identify the relationship between the features of DRT service (including uncertain characteristics) and people's adaptation behavior.

To develop effective strategies for the supply of shared parking and study various theoretical choice models under uncertainty, this paper investigates private parking space owners' propensity to engage in shared parking schemes using a stated choice experiment that involves an uncertain key attribute. A hybrid expected utility-regret model incorporating rejoice is specified to explore the participation behavior. Equivalent models considering the perception of attribute differences are also estimated. Results show that socio-demographic characteristics, social influence, government's role, media attention, platform fee, and revenues are all important factors explaining private parking owners' propensity to engage in shared parking schemes. Besides, the model incorporating all these components, including the emotions of regret and rejoice and the perception of attribute differences, yields the best results. These findings could help promote the policy development toward increasing people's engagement in shared parking.

Electric bikes are considered an important sustainable alternative to private cars. This transportation mode competes with other new mobility modes, such as Shared Mobility and Mobility as a Service (MaaS). Because these services may not always be available, people may face a variety of choice options in different cities/regions. Despite their relevance, most studies of e-bike mode choice do not consider these availability effects, which may bias estimated acceptance rates and market shares of e-bikes. This paper reports the formulation and estimation results of a discrete portfolio choice model incorporating the availability effects of other sustainable mobility services to explore individuals' willingness to buy pedelecs and speed pedelecs. We designed a stated portfolio choice experiment considering varying choice set composition, where Shared Mobility or/and MaaS may not be available. An error component logit model was formulated to analyze the availability effects. The knowledge we gained regarding the willingness to buy e-bikes in the presence or absence of motorized shared mobility options has major practical implications as some pilot studies have evidenced decreasing use of active modes once motorized shared mobility becomes conveniently available.

The concept "work schedule arrangement" refers to the decision how many hours per week to work and how to allocate these hours across the days of the week. In two-adult households with children, the work schedule arrangement of parents is more complicated owing to the presence of children, which induces a series of activities that parents need to organize and coordinate. Besides considering personal preferences, parents also need to trade-off between working longer to generate more income and have better promotion opportunities, and working less hours to have enough time to organize and conduct other activities, especially children-related activities. In modeling work schedule arrangements of two-adult households with children, we first develop a random utility model to represent the work schedule decision-making process. It assumes that the utility of work arrangements is not only a function of work duration and other job characteristics, but also of the (joint) time to spend with or take care of the children. We therefore include the state of the children, which indicates the location where they are at any given moment in time (home, school/day care). Under the assumption that households maximize the utility derived from their work schedule, weekly work schedules for each spouse are generated, subject to observed daily and weekly total household and/or individual working hours. In order to evaluate the accuracy of the model, we compare the difference between the generated and observed work schedules of the households. The results show that the model accurately predicts observed work schedules in terms of start time, number of working hours and days of the week.

Multi-state supernetworks are capable of representing activity-travel patterns at a high level of detail and thus are a powerful tool for activity-travel scheduling (ATS) of multidimensional choice facets. To alleviate the limitations of the common deterministic network representation, travel time and activity duration uncertainty has been incorporated in multi-state supernetworks. However, the extension unrealistically assumed that all uncertain components are independent. This study suggests an approach of ATS considering spatially and temporally correlated travel times and activity durations in stochastic time-dependent (STD) multi-state supernetworks. Support points are used as a representation of the stochasticity of the STD multi-state supernetwork. ATS is formulated as a pathfinding problem subject to space–time constraints based on recursive formulations. A series of numerical experiments is implemented to demonstrate the applicability of the suggested approach.

Work schedules with their start and end times substantially affect traffic flows, particularly during peak hours. Therefore, the study of work schedules is important to understand daily activity travel patterns. Particularly adults in households with children need to trade-off the number of working hours versus their time expenditure on child care, household tasks and leisure. Work schedule preferences are reflected in job application decisions in the sense that people are less likely to apply if the job profile is incongruent with their time use preferences. Job application decisions may also be influenced by attitudes of members of a household's social network and peer groups. The aim of this study is to analyze unobserved heterogeneity and the effects of job properties and social influence on the decision to apply for a job in two-adult households with children. To that end, a stated choice experiment, in which respondents are asked whether they would apply for a job of a particular profile and a certain set of attitudes of members of their social network, is constructed and implemented. A latent class mixed logit model with two latent classes is applied to estimate the effects of work schedule attributes, social influence and socio-demographic characteristics on the probability of applying for a particular job. Results show that in both classes the effects of job attributes are more significant than social influence, and that the effects of the number of working hours and salary differ show considerable unobserved heterogeneity.

Mobility-as-a-Service (MaaS) can be considered the latest innovative technological solution to sustainable transportation. It offers travelers access to a pre-paid bundle of transportation modes on a single app with the additional convenience of the integration of planning, booking, and payment. Capturing the impact of this new technology on travel behavior remains a challenge, given the scarce number of comprehensive MaaS pilots. In this study, a stated adaptation experiment was designed to determine travelers' propensity toward using this new service. Such knowledge sheds light on the potential of MaaS to alter daily travel patterns.

This paper follows authors' previous work, which measured social influence in travel behavior using a sequential stated adaptation experiment, and aims to investigate issues that are not discussed in the previous one. Specifically, this paper supplements the previous work in two aspects. On the one hand, the previous work tried to model respondents' sequential choice in terms of a choice task with and without social network member's choice, and estimated the model sequentially without any clear evidences to show the difference between sequential and simultaneous estimation. On the other hand, the previous work did not clearly address two specific issues that come with the use of sequential choice experiments (i.e., choice consistency and similarity). The results of this study reveal that estimating the model simultaneously may lead to confounding bias and that taking the issues caused by the sequential choice experiment could provide more insights about social influence.

This paper presents an integrated framework for the optimal planning of public charging stations for plug-in electric vehicles (PEVs) in urban areas. The framework consists of two main components: (i) an out-of-home charging demand model based on an activity-based travel demand model, and (ii) a public charging station location-allocation model using a scenario-based stochastic programming (SP) approach. In order to capture the dynamic charging behaviour of PEV users, a chi-squared automatic interaction detector (CHAID)-based mixed effects decision tree is induced from multi-day activity diaries. Moreover, because the stochastic error of the micro-simulation approach brings about uncertainty, we adopted a two-stage stochastic mixed-integer programming (TSMIP) model, which measures uncertainty by means of a finite set of scenarios obtained from the derived decision rules underlying PEV charging. The proposed approach is demonstrated for the City of Eindhoven, The Netherlands, and benefits of the stochastic solution are discussed.

Physical inactivity remains a major public health challenge today. Understanding the determinants of changes in habitual leisure-time physical activity patterns by type across the life course is important for developing targeted interventions. This study presents a multiple discrete-continuous extreme value model to examine the determinants of habitual participation in and time allocation to multiple leisure-time physical activities over the life course. A comprehensive set of socio-demographics, life transitions, neighborhood characteristics, and time-related factors are considered as determinants of each activity type, including sports, recreational walking, cycling, outdoor playing, and dog walking. Results estimated on retrospective survey data collected in the Netherlands show significant differences in the determinants of the different types of leisure-time physical activity. Social-demographic factors have a strong influence on sports participation, followed by recreational walking, cycling, outdoor playing, and then dog walking. Life transitions have different effects. A change in marital status appears to be the most important life event for sports participation while changing jobs is the most important event for the other two activities. Neighborhood characteristics primarily affect participation in recreational walking, cycling, outdoor playing, and dog walking. As for time-related factors, they mainly impact sports engagement. The findings of this study could help develop effective interventions to promote leisure-time physical activity participation during life transitions and encourage healthy living.