Incorporating the extended theory of planned behavior in a school travel mode choice model: a case study of Shaoxing,China

Travel to and from school can have social, economic, and environmental implications for students and their parents. Therefore, understanding school travel mode choice behavior is essential to find policy-oriented approaches to optimizing school travel mode share. Recent research suggests that psychological factors of parents play a significant role in school travel mode choice behavior and the Multiple Indicators and Multiple Causes (MIMIC) model has been used to test the effect of psychological constructs on mode choice behavior. However, little research has used a systematic framework of behavioral theory to organize these psychological factors and investigate their internal relationships. This paper proposes an extended theory of planned behavior (ETPB) to delve into the psychological factors caused by the effects of adults’ cognition and behavioral habits and explores the factors’ relationship paradigm. A theoretical framework of travel mode choice behavior for students in China is constructed. We established the MIMIC model that accommodates latent variables from ETPB. We found that not all the psychological latent variables have significant effects on school travel mode choice behavior, but habit can play an essential role. The results provide theoretical support for demand policies for school travel.     

On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables

We develop an econometric framework for incorporating spatial dependence in integrated model systems of latent variables and multidimensional mixed data outcomes. The framework combines Bhat's Generalized Heterogeneous Data Model (GHDM) with a spatial (social) formulation to parsimoniously introduce spatial (social) dependencies through latent constructs. The applicability of the spatial GHDM framework is demonstrated through an empirical analysis of spatial dependencies in a multidimensional mixed data bundle comprising a variety of household choices – household commute distance, residential location (density) choice, vehicle ownership, parents’ commute mode choice, and children's school mode choice – along with other measurement variables for two latent constructs – parent's safety concerns about children walking/biking to school and active lifestyle propensity. The GHDM framework identifies an intricate web of causal relationships and endogeneity among the endogenous variables. Furthermore, the spatial (social) version of the GHDM model reveals a high level of spatial (social) dependency in the latent active lifestyle propensity of different households and moderate level of spatial dependency in parents’ safety concerns. Ignoring spatial (social) dependencies in the empirical model results in inferior data fit, potential bias and statistical insignificance of the parameters corresponding to nominal variables, and underestimation of policy impacts.     

The role of socio-economic,built environment and psychological factors in parental mode choice for their children in an Iranian setting

This research investigated the role of parental psychological and socio-economic factors as well as built environment for the choice of their children’s (primary school pupils, aged 7–9 years) travel mode to school in Rasht, Iran. A total of 1078 questionnaires were distributed (return rate of 80 percent) among pupils in nine primary schools in January 2014. A mixed logit (ML) model was employed due to its ability to test heterogeneity among parents and also to determine its possible sources. Results of random coefficient ML modelling showed that several psychological, socio-economic and built environment characteristics were significant factors in parental mode choice. Only walking time perception to school had a significant random normal distribution coefficient and no other psychological and socio-economic variable had a random effect. Further investigation by random coefficient analysis showed that the possible source of household preference heterogeneity could be to own two or more cars. Regarding psychological variables, strong parental worry about their children walking alone to school had a negative impact on allowing them to walk to school. Parents who evaluated poor contextual and design preconditions for walking tended to choose school service more than private car and walking. Parents with stronger environmental personal norms were more willing to allow their children to walk. The findings suggest that infrastructural measures, such as sidewalk facilities, neighborhood security and safety, encourage parents to allow children to walk to school. Information campaigns targeting environmental norms may increase walking among pupils in an Iranian setting.     

Investigating heterogeneity in social influence by social distance in car-sharing decisions under uncertainty: A regret-minimizing hybrid choice model framework based on sequential stated adaptation experiments

The present study is designed to investigate social influence in car-sharing decisions under uncertainty. Social influence indicates that individuals’ decisions are influenced by the choices made by members of their social networks. An individual may experience different degrees of influence depending on social distance, i.e. the strength of the social relationship between individuals. Such heterogeneity in social influence has been largely ignored in the previous travel behavior research. The data used in this study stems from an egocentric social network survey, which measures the strength of the social relationships of each respondent. In addition, a sequential stated adaptation experiment was developed to capture more explicitly the effect of social network choices on the individual decision-making process. Social distance is regarded as a random latent variable. The estimated social distance and social network choices are incorporated into a social influence variable, which is treated as an explanatory variable in the car-sharing decision model. To simultaneously estimate latent social distance and the effects of social influence on the car-sharing decision, we expand the hybrid choice framework to incorporate the latent social distance model into discrete choice analysis. The estimation results show substantial social influence in car-sharing decisions. The magnitude of social influence varies according to the type of relationship, similarity of socio-demographics and the number of social interactions.     

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers’ observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior.     

Impacts of parental gender and attitudes on children’s school travel mode and parental chauffeuring behavior: results for California based on the 2009 National Household Travel Survey

Research has shown that parental attitudes are a significant predictor of children’s active commuting (walking or biking) to school. However, the impact of parental gender on parental attitudes, and the link between parental attitudes and the gender gap in parental chauffeuring behavior have not received much attention. This paper examines these questions by applying discrete choice models to California data from the 2009 National Household Travel Survey while controlling for a wide range of variables characterizing parents, their children, households, schools, and the local built environment. Our results, conveyed via odds ratios, show that mothers are more likely to have higher concerns about traffic volume, which in turn reduces the likelihood that their children will walk or bike to school. Moreover, even though parental attitudes significantly influence parental chauffeuring behavior, their ability to explain the gender chauffeuring gap is limited. When holding equal concerns, mothers are still more likely than fathers to chauffeur their children to school. Finally, while distance to school and several land use measures (e.g., population density, urbanization level, and percentage of renters) are statistically significant, the impact of an objective measure of walkability is quite small. These results suggest that interventions targeting an increase in children’s walking and biking to school should focus on the concerns of mothers, especially as they relate to traffic characteristics.     

Place happiness: its constituents and the influence of emotions and subjective importance on activity type and destination choice

The way in which a person organizes his or her day, both temporally and spatially, is a highly important matter to travel behavior and travel demand modeling. Many times, the focus of these models is to accurately predict the “where” and “when”, without paying adequate attention to the “why.” The participation in activities, and therefore the selection of a place for these activities has been recently discussed within the framework of subjective well being. The motivation of happiness can be used to understand how and why people make the choices that they do. Many different criteria are used by individuals in the selection of destinations. These criteria range from attributes such as distance and cost, to attributes such as comfort, security and social aspects in determining the most rewarding destinations. Aspects contributing to a rewarding experience can also be viewed as those decision criteria that lead to the highest satisfaction. In this paper, several attributes of places and decision-making are explored for their potential to explain destination choices. First, a broader analysis of destination choice and criteria used helps us develop a geographic representation of attitudes and views regarding the area of Santa Barbara, California. Following this general evaluation of space, individual activity types are statistically analyzed in the importance different attributes play in the selection of a destination that leads to higher satisfaction.     

Street characteristics to encourage children to walk

An experiment tested whether physical disorder affected low to moderate income African–American children’s choice of street to walk on and their parents’ choice of a street for them to walk on. The experiment used an innovative desktop simulation in which 32 fourth and fifth grade African–American children and 30 parents viewed and explored pairs of virtual walk-through streets manipulated on disorder (across three contexts and two other street and sidewalk characteristics) and picked from each pair the one to walk on (child) or for the child to walk on (parent). Each participant was asked to report the reasons for the choices. The analysis revealed that children and their parents were more likely to walk (or have the child walk) on streets lower in disorder. Reported reasons for choices confirmed the importance of physical disorder in affecting walking choices. Low-cost improvements in order may make streets more desirable for recreational walking.     

Modeling children’s school travel mode and parental escort decisions

Understanding of the activity-travel patterns of children is becoming increasingly important to various policy makers. Further, there is also a growing recognition that intra-household interactions need to be explicitly accommodated in travel models for realistic forecasts and policy evaluation. In the light of these issues, this paper contributes towards an overall understanding of the school-travel behavior of children and the related interdependencies among the travel patterns of parents and children. An econometric model is formulated to simultaneously determine the choice of mode and the escorting person for children’s travel to and from school. The 2000 San Francisco Bay Area Travel Survey (BATS) data are used in the model estimation process. Empirical results indicate that the characteristics of child like age, gender, and ethnicity, and employment and work flexibility characteristics of the parents have strong impacts on the mode choice decisions. In addition, the impacts of some of these attributes on the choice of mode to school are different from the corresponding impacts on the choice of mode from school. The distance between home and school is found to strongly and negatively impact the choice of walking to and from school, with the impact being stronger for walking to school. Several land-use and built-environment variables were explored, but were found not to be statistically significant predictors.

Quantifying the transport-related impacts of parental school choice in England

School travel is becoming increasingly car-based and this is leading to many environmental and health implications for children all over the world. One of several reasons for this is that journey to school distances have increased over time. This is a trend that has been reinforced in some countries by the adoption of so-called ‘school choice’ policies, whereby parents can apply on behalf of their child(ren) to attend any school, and not only the school they live closest to. This paper examines the traffic and environmental impacts of the school choice policy in England. It achieves this by analysing School Census data from 2009 from the Department for Education. Multinomial logit modelling and mixed multinomial logit modelling are used to illustrate the current travel behaviour of English children in their journey to school and examine how there can be a significant reduction in vehicle miles travelled, CO₂ emissions and fuel consumption if the ‘school choice’ policy is removed. The model shows that when school choice was replaced by a policy where each child only travelled to their ‘nearest school’ several changes occurred in English school travel. Vehicle Miles Travelled (VMT) by motorised transport fell by 1 % for car travel and 10 % for bus travel per day. The reduction in vehicle miles travelled could lead to less congestion on the roads during the morning rush hour and less cars driving near school gates. Mode choice changed in the modelled scenario. Car use fell from 32 to 22 %. Bus use fell from 12 to 7 %, whilst NMT saw a rise of 17 %. With more children travelling to school by walking or cycling the current epidemic of childhood obesity could also be reduced through active travel.     

A non-compensatory model of transportation behavior based on sequential consideration of attributes

The proposed model of travel choice behavior is based upon an assumption that individuals compare their choice alternatives on a series of attributes ordered in terms of importance; they eliminate from consideration those alternatives which do not meet their expectation on one or more of the characteristics. The process is repeated with adjusted levels of expectation until only one alternative remains. The model thus incorporates a number of psychological decision axioms which have seldom been applied in models aimed at providing transportation planners with useful information from consumer survey data.Estimates of parameters defining distributions of expectation levels in a population of travelers are generated using a nonlinear optimization technique. The technique is demonstrated to provide estimates which replicate well the choices of travelers in two different contexts: choice of hypothetical concepts of small urban vehicles and choice of destination for shopping trips within an urban area.     

Values,attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice

Values lie at the heart of an individual’s belief system, serving as prototypes from which attitudes and behaviors are subsequently manufactured. Attitudes and behaviors may evolve over time, but values represent a set of more enduring beliefs. This study examines the influence of values on travel mode choice behavior. It is argued that personal values influence individual attitudes towards different alternative attributes, which in turn impact modal choices. Using data from a sample of 519 German commuters drawn from a consumer panel, the study estimates an integrated choice and latent variable model of travel mode choice that allows for hierarchical relationships between the latent variables and flexible substitution patterns across the modal alternatives. Results from the empirical application support the value-attitude-behavior hierarchical model of cognition, and provide insights to planners and policy-makers on how better to sell public transit as a means of travel.     

Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory

This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders.Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm and 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall.     

Intra-household bargaining for school trip accompaniment of children: A group decision approach

This paper tests a group decision-making model to examine the school travel behavior of students 6–18 years old in the Minneapolis-St. Paul Metropolitan area. The school trip information of 1737 two-parent families with a student is extracted from Travel Behavior Inventory data collected by the Metropolitan Council between the Fall 2010 and Spring 2012. The model has four distinct characteristics including: (1) considering the student explicitly in the model, (2) allowing for bargaining or negotiation within households, (3) quantifying the intra-household interaction among family members, and (4) determining the decision weight function for household members. This framework also covers a household with three members, namely, a father, a mother, and a student, and unlike other studies it is not limited to dual-worker families. To test the hypotheses we build two models, each with and without the group-decision approach. The models are separately built for different age groups, namely students 6–12 and 12–18 years old. This study considers a wide range of variables such as work status of parents, age and gender of students, mode of travel, and distance to school. The findings of this study demonstrate that the elasticities of the two modeling approaches differ not only in the value, but in the sign in some cases. In 63% of the cases the unitary household model underestimates the results. More precisely, the elasticities of the unitary household model are as much as 2 times more than that of the group-decision model in 20% of cases. This is a direct consequence of model misspecification that misleads both long- and short-term policies where the intra-household bargaining and interaction is overlooked in travel behavior models.     

Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model

In this paper, we apply Bhat and Dubey’s (2014) new probit-kernel based Integrated Choice and Latent Variable (ICLV) model formulation to analyze children’s travel mode choice to school. The new approach offered significant advantages, as it allowed us to incorporate three latent variables with a large data sample and with 10 ordinal indicators of the latent variables, and still estimate the model without any convergence problems. The data used in the empirical analysis originates from a survey undertaken in Cyprus in 2012. The results underscore the importance of incorporating subjective attitudinal variables in school mode choice modeling. The results also emphasize the need to improve bus and walking safety, and communicate such improvements to the public, especially to girls and women and high income households. The model application also provides important information regarding the value of investing in bicycling and walking infrastructure.     

Factors affecting children’s journeys to school: a joint escort-mode choice model

A child’s mode of travelling to school is influenced by, or dependent on, parental choices. Thus, an increasing proportion of car trips may reflect parental choices and constraints. Whether a parent can escort their children to school may depend on their scheduling and spatial constraints, e.g., work schedule and job location in relation to home and school locations. This research aims to understand the effect of household bundling constraints on a child’s escort-mode choice. In this study, school trip data are drawn from the 2001 SCAG (Southern California Association of Governments) Post Census Regional Household Travel Survey. The study area is the five-county Los Angeles region. Our findings show that the parents’, especially the mother’s, increased working hours and more distant job locations result in an increased likelihood of several alternative escort-mode choices. Mothers who work longer hours and further away from home are less likely to chauffeur their children. These trips have been substituted by alternative escort choices such as independent travel and being escorted by fathers, or alternative mode choices such as active commuting and busing. The effect of increased working hours may be offset by the option of flexible working hours, which allows parents to arrange more escort trips. This study elucidates an important aspect in explaining children’s changing mode choice in journeys to school and sheds light on current policy efforts in reducing children’s car dependency.     

Implicit choice set generation in discrete choice models: Application to household auto ownership decisions

Latent choice set models that account for probabilistic consideration of choice alternatives during decision making have long existed. The Manski model that assumes a two-stage representation of decision making has served as the standard workhorse model for discrete choice modeling with latent choice sets. However, estimation of the Manski model is not always feasible because evaluation of the likelihood function in the Manski model requires enumeration of all possible choice sets leading to explosion for moderate and large choice sets. In this study, we propose a new group of implicit choice set generation models that can approximate the Manski model while retaining linear complexity with respect to the choice set size. We examined the performance of the models proposed in this study using synthetic data. The simulation results indicate that the approximations proposed in this study perform considerably well in terms of replicating the Manski model parameters. We subsequently used these implicit choice set models to understand latent choice set considerations in household auto ownership decisions of resident population in the Southern California region. The empirical results confirm our hypothesis that certain segments of households may only consider a subset of auto ownership levels while making decisions regarding the number of cars to own. The results not only underscore the importance of using latent choice models for modeling household auto ownership decisions but also demonstrate the applicability of the approximations proposed in this study to estimate these latent choice set models.     

A mixed logit model for predicting exit choice during building evacuations

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns is the escape route. The choice of a route may involve local decisions on alternative exits from an enclosed environment. This study investigates the effect of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1503 participants is obtained and a mixed logit model is calibrated using these data. The model shows that the presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model indicates that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main aim of this study is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.     

Modeling the modal choice decision process

Most models of modal choice are macroanalytic in nature — focusing on the behavior of large groups of travelers — and have limited explanatory power. Transportation managers need to know more about the decision processes of individual travelers in selecting a mode for a particular trip, if they are to be able to develop strategies for influencing these decisions. A microanalytic model of modal choice is therefore developed in flow-chart form, clarifying the stages in the modal choice decision process for any given trip. Individual consumers are seen as trying to satisfy a particular travel need by first specifying the characteristics of the trip itself and then specifying the “ideal” modal attributes required for this trip. Next, the perceived characteristics of a limited number of modes are evaluated against this “ideal” solution and the consumer is assumed to select that mode which provides the best match. The model explicitly recognizes the impact of psychological variables on modal choice as well as the consumer's need for information if he or she is to evaluate realistically all alternatives.     

Mode choice and travel distance joint models in school trips

We test a copula-based joint discrete–continuous model to unravel mode choice and travel distance decisions in a joint framework for school trips. This framework explicitly accounts for common unobserved factors that may affect both the mode choice and travel distance. Joint estimation of the models makes a significant difference in the effect of travel distance on willingness to walk to school. The absolute value of the travel distance coefficient in the mode choice model increases by 22% when a joint formulation is adopted instead of the conventional single estimations. We find a significant decrease of 19% in the coefficient of travel safety perception in the joint mode choice model compared to the single model. This underscores the impact of model specification, in terms of the variable effect interpretation and policy assessments. The effect magnitude of several policy-sensitive variables is discussed and compared with previous studies. Particularly, we indicate that the probability of walking is reduced by 0.85% due to a 1% increase in travel distance; accordingly, it propels parents to select non-active modes, particularly school bus. This study also demonstrates how addressing parental concerns about travel safety could double the propensity to walk to school.