Activity-travel Behavior Research Articles

Promoting activity participation and well-being among children and adolescents: a systematic review of neighborhood built-environment determinants.

The objective of this review was to identify, evaluate, and synthesize the findings on built-environment determinants and their relation to participation in different domains of activities, including physical activity, recreational and social activities, and well-being in childhood and adolescence. Creating supportive environments for children and adolescents is a priority in society. To ensure informed decision making and policy changes, initiatives need to rely on systematic development and the use of evidence-based knowledge. Thus, it is necessary to critically review the current evidence on the relationship between features of the built environment and health in a more specific and detailed manner to better understand the health-promoting potential of neighborhood built environments. This review included studies on children and adolescents between five and 18 years of age, which examined relationships between one or several neighborhood built-environment determinants and participation in activities and/or well-being. The studies had to report test statistics for associations between built-environment determinants and the outcomes, which means that descriptive cross-sectional studies were not eligible for inclusion. A four-step search strategy was utilized to identify peer-reviewed studies within six databases. The search was limited to English articles published since January 2010. A data extraction form was developed and used to mine the descriptive details of each included study. The included studies were further assessed for methodological quality by three reviewer pairs independently, using the standard critical appraisal tools from JBI. Due to the methodological heterogeneity of the included studies, a narrative summary of the quantitative findings was conducted. The 127 studies included in the review were mainly cross-sectional (87.4%). The built environment was most extensively studied in relation to the outcomes active travel (n = 54) and unspecified physical activity (n = 46). The evidence suggests that a composite determinant of facilities and amenities is related to more unspecified physical activity. Furthermore, less traffic exposure and more safety features, pedestrian infrastructure for walking and cycling, shorter distances to facilities and greater walkability supported active travel behavior. Fewer studies (n = 11) examined the built-environment determinants of organized sports and well-being, and limited, as well as contradictory, evidence existed for the relationship between the built environment and well-being. The following determinants potentially support active travel behavior: less traffic exposure and more safety features, pedestrian infrastructure for walking and cycling, shorter distances to facilities and greater walkability. A high facility and amenity index might promote unspecified physical activity. Policies and planning processes should consider these determinants to strengthen children's and adolescents' health and well-being. However, there are remaining research gaps and important avenues for future research that need to be addressed before more specific and robust conclusions can be drawn.

Stated preferences for housing within walking distance to shopping areas

A city's points of interest reflect the character and identity of urban environments, setting the scene for various functions and activities. Built form, active travel behaviour and residential pre...

CULTURE, COMPANIONSHIP AND ACTIVITY-TRAVEL BEHAVIOR: A COMPARISON BETWEEN BEIJING (CHINA) AND UTRECHT (THE NETHERLANDS)

There is a long history of drawing a large cultural distinction between West and East based on the constructs of individualism and collectivism. This coincides with a growing awareness of urban spatial change from the perspective of activity-travel behavior. However, limited attention has been paid to this issue. This paper contributes to the literature on how to examine cultures by investigating companion choice in travel behavior based on activity diary surveys collected in Beijing (China) and Utrecht (the Netherlands). The results show that participants in Beijing travel more often with family members, whereas those in Utrecht tend to travel alone or with friends. These important differences are mediated through sociodemographic, travel purpose and transport mode. The findings provide evidence that collective behavior is popular in Eastern society, whereas individualistic behavior is prevalent in Western society. These results will hopefully stimulate further analysis of cultural differences in transportation policy.

Understanding travel behavior: A study of school excursion motivations, constraints and behavior

A more detailed understanding of actual travel behavior is crucial to better understand travel patterns and determine the attractiveness of target markets. Educational tourism, and school excursion tourism in particular, is a relatively under researched and poorly understood segment of the tourism industry. This paper examines the influence of socio-demographics, motivations and constraints on planned and actual school excursion behavior. A total of 1,314 schools from across Australia participated in an online survey. Analysis revealed the importance of interpersonal constraints at explaining both planned and actual travel. Socio-demographics including the number of staff and students at the school, and a dedicated school excursion planner helped to explain the average length of stay of school excursions. The push motive of ‘new experiences’ positively influenced the number of overnight excursions undertaken, while the push/pull motive of ‘a reward’ helped explain longer length of stay and distance travelled, alongside socio-demographic factors. Practical implications are outlined for both destinations and attractions interested in targeting school excursions. Future research avenues are also outlined based on the research findings and limitations.

Machine learning applications in activity-travel behaviour research: a review

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

A microeconomic framework for integrated agent-based modelling of activity-travel patterns and energy consumption

Abstract The sophistication in the demand management approaches in both transport and energy sectors and their interaction call for modelling approaches that consider both sectors jointly. For agent-based microsimulation models of travel demand and energy consumption, this implies the necessity to ensure consistent representation of user behaviour with respect to mobility and energy consumption behaviours across the model components. Therefore this paper proposes a microeconomic framework, termed the HOT model (Home, Out-of-home, Travel) grounded in the goods-leisure paradigm, but extended to incorporate emerging activity-travel behaviour patterns and their energy consumption implications. We discuss how the model can be operationalised and embedded within agent-based frameworks with a case study using time use and energy consumption data from the UK.

Carer-employees’ travel behaviour: Assisted-transport in time and space

Assisted-transport demand is a daily caregiving task that affects carer-employee’s activity-travel behaviour; however, little is known about such behaviour and the types of constraints that impact carer-employee health. Combining the principles of Hägerstrand’s time geography and Mckie et al.’s caringscape terrain, this research develops a mixed-methods framework to classify the travel behaviour of carer-employees based on their travel experience and the space-time fixity of their weekly schedules. The mixed-methods framework consists of sentiment analysis and k-means clustering, both which are used to analyze 25 randomly selected participants within the Greater Toronto-Hamilton Area (GTAH). Participants were asked to reflect on their recorded one-week trips in a trip summary questionnaire. Sentiment analysis was used to thematically describe carer-employees’ travel behavior, whereas, k-means clustering generated travel behaviour profiles. “Time”, “pressure”, “parents”, “run”, and “long” were several thematic keywords describing the carer-employees’ travel behaviour. K-means clustering identified three relative types of carer-employees’ travel behaviours: 1) flexible, 2) between flexible and fixed, and; 3) fixed. These results provide critical information for the establishment of custom transport programs, such as maximum monthly telecommuting allotment; such programs are useful for employers to use in order to alleviate assisted-transport demand on their employees.

Clustering Activity–Travel Behavior Time Series using Topological Data Analysis

Over the last few years, traffic data have been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new divide and combine-based approach to do K-means clustering on activity–travel behavior time series using features that are derived using tools in time series analysis and topological data analysis. Our approach facilitates a case study, where each individual’s daily activity–travel behavior is characterized as a categorical time series consisting of three different levels. Clustering data from five waves of the National Household Travel Survey ranging from 1990 to 2017 suggests that activity–travel patterns of individuals over the last 3 decades can be grouped into three clusters. Results also provide evidence in support of recent claims about differences in activity–travel patterns of different survey cohorts. The proposed method is generally applicable and is not limited only to activity–travel behavior analysis in transportation studies. Driving behavior, travel mode choice, household vehicle ownership, when being characterized as categorical time series, can all be analyzed using the proposed method.

Modelling of travel behaviour of students using artificial intelligence

Travel demand models are required by transportation planners to predict the travel behaviour of people with different socio-economic characteristics. Travel behaviour of students act as an essential component of travel demand modelling. This behaviour is reflected in the educational activity travel pattern, the timing, sequence and mode of travel of students. Roads in the vicinity of schools are adversely affected during the school opening and closing hours. It enhances the traffic congestion, emission and safety problems around schools. It is necessary to improve the safety of school going children by understanding the present travel behaviour and to develop efficient sustainable traffic management measures to reduce congestion in the vicinity of schools. It is possible only if the travel behaviour of educational activities are studied. This travel behaviour is complex in nature and lot of uncertainty exists. Selection of modelling technique is very important for modelling the complex travel behaviour of students. This leads to the importance of application of artificial intelligence (AI) techniques in this area. AI techniques are highly developed in twenty first century due to the advancements in computer, big data and theoretical understanding. It is proved in the literature that these techniques are suitable for modelling the human behaviour. However, it has not been used in behaviourally oriented activity based modelling. This study is aimed to develop a model system to predict the daily travel behaviour of students using artificial intelligence technique, ANN. These ANN models were then compared with the conventional econometric models developed. It was observed that artificial intelligence models provide better results than econometric models in predicting the activity-travel behaviour of students. These models were further applied to study the variation in activity-travel behaviour, if short term travel-demand management measures like promoting walking for educational activities are implemented. Thus the study established that artificial intelligence can replace the conventional econometric methods for modelling the activity-travel behaviour of students. It can also be used for analysing the impact of short term travel demand management measures.

What moves us: Subjective and objective predictors of active transportation

BackgroundThe majority of commuting trips in the United States are taken by motor vehicle. Yet, lack of regular physical activity has been identified as one of its most significant public health issues, and globally, risks due to physical inactivity are increasing. However, we believe current studies offer an unclear picture of the complex role of the environmental or psychological influences in active travel behavior across urbanicity classifications. AimsTo (1) determine an association between objective or subjective measures of the built environment and active transportation, and (2) characterize the associations within both urban and non-urban contexts among a statewide sample of 862 adults residing in Wisconsin. MethodsMultivariate logistic regression analysis of subjective data from the Survey of the Health of Wisconsin (SHOW) and data from the Wisconsin Assessment of the Social and Built Environment (WASABE), a direct observation audit tool, to test the impacts of subjective perceptions and objective built environment predictors on active transportation in both urban and rural contexts. ResultsMales, non-married, physically active individuals, those living in urban areas and with higher education levels had greater odds of using active transportation. Bicycle friendliness (aOR = 2.43; 95% CI: 1.4–4.2) and presence of non-residential destinations (aOR = 1.03; 95% CI: 1.01–1.05) were the strongest objective predictors of active transportation overall. Subjective measures were especially predictive of active transportation in non-urban areas, specifically for participants identifying presence of trails (adjusted odds = 3.71; 95% CI: 1.5–9.2) and non-residential destinations nearby (adjusted odds = 2.65; 95% CI: 0.99–7.06). ConclusionsActive transportation is associated with built environment characteristics and perceived environmental factors, but the specific predictors and strength of associations vary by context. Results suggest perceived bicycle friendliness may be important in urban areas and access to trails for walking and biking may provide important opportunities to promote active transportation in more suburban or rural areas.

Can an Experience with No Car Use Change Future Mode Choice Behavior?

In order to determine whether an experimentally induced experience has the potential to change future travel mode choice, we recruited 10 families living in a middle-sized city who used a car at least four times a week, and made them stop using the car for one month. Each adult family member kept a travel diary and interviews were conducted prior to the experiment, after one month without a car, and then three months and one year after the experiment ended. The results suggest that the participants’ attitudes towards different transportation modes did not change during the period of the study, but their actual travel behavior did. In this respect, several factors were identified that influence travel mode choice, once the participants are made aware of the decision process and break the habit of car use.

Longer, More Active Commute, but Still not Very Active: Five-Year Physical Activity and Travel Behavior Change in a University Population.

Active travel can support the achievement of recommended levels of physical activity. Monitoring travel behavior of university students and staff provides a useful insight into patterns of regional travel and population level changes in physical activity. This study sought to evaluate current travel and physical activity behaviors in a university population and to determine whether these changed over time. An online survey of travel behavior and physical activity was conducted at the University of Sydney, Australia. The survey was actively promoted for three weeks prior to the release of the survey among staff and students, which asked about travel behavior on a specific day in September 2017. The survey questions were the same as those used in a similar online survey conducted across the University in 2012. In total, 4359 People completed the survey, representing 10.8% of staff and 4.1% of students. Approximately two thirds of survey respondents were students, in both the 2012 and 2017 surveys. Compared with 2012, there was an increase in active travel to the University in 2017 from increased walking and train travel. Compared to 2012, in 2017 there was an increase in average minutes walked by about nine minutes, and less time spent sitting. Trip lengths increased, with 68% of trips taking longer than 30 min in 2017. The amount of time spent in low–moderate levels physical activity increased between 2012 and 2017, potentially related to active travel behavior. Citywide changes towards a system-wide transport fare structure was the biggest change in the transport environment between the two surveys and may have contributed to increased train travel.

Novel Divide and Combine-Based Approach to Estimating Mixture Markov Model for Large Categorical Time Series Data: Application to Study of Clusters using Multiyear Travel Survey Data

Over the last few years, as with many other fields, the transportation discipline has been swept by the big data revolution. This revolution has not only brought about tremendous opportunities for conducting interesting data-driven analysis, it has also highlighted challenges associated with using traditional analytical methods to analyze these large datasets. To this end, this paper proposes a new Divide and Combine-based approach to estimating Mixture Markov models for analyzing large categorical time series data. The validity of this approach is demonstrated using a simulation study. Further, the feasibility and applicability is highlighted by conducting a clustering analysis of large activity–travel sequences using multiyear travel survey datasets. In the case study, each individual’s daily activity–travel behavior is characterized as a categorical time series that attempts to capture multiple aspects of travel and activity engagement simultaneously while also incorporating the timing and the schedule of different episodes. The proposed Divide and Combine-based Mixture Markov models are then used to cluster the large data. Subsequently, cluster compositions are explored to understand within and between-cluster differences and their associations with generational cohort factors, socioeconomic attributes, and demographic variables. As a preliminary exploration, the results suggest that travel patterns of individuals over the last three decades can be categorized into three types of travel patterns. Results also provide evidence in support of recent claims about different generational cohorts and their activity–travel behaviors.

Active travel for active ageing in China: The role of built environment

China has been witnessing prominent demographic ageing because of its sustained low fertility (one-child policy) and mortality rates. In 2017, nearly one in four elderly adults in the world live in China. The rapid increase of the elderly population is supposed to dramatically influence the urban and transportation system. Active travel plays an important role for the ageing Chinese population to sustain their mobility and wellbeing. To provide suitable policy implications for age-friendly travel environments in China, this study investigates how the built environment affects active travel behavior. Particularly, we explore the influences of built environment on daily active travel frequency and time expenditure while taking into account travel attitudes. A zero-inflated ordered probit model and a Cox proportional hazards model are respectively estimated based on the Nanjing Travel Survey data. Results show that the social and cultural contexts exert pronounced impacts on the travel pattern of Chinese older people. Specifically, it is found that the living pattern of co-residence, and the proximity to market, park/square, and chess/card room are influential in shaping active travel patterns. In addition, the built environment shows larger effects on the active travel behavior of older adults than on that of young people. Attitudes towards active travel are not significant in explaining the senior's travel behavior, indicating limited self-selection effects. The findings will offer insights to establish effective and appropriate land use strategies and public facility distribution for the elderly during the Chinese urban renewal process.

Fragmentation in Daily Schedule of Activities using Activity Sequences

A new method of sequence analysis to measure fragmentation in activity participation is presented in this paper. We applied this method to a sample of residents in the Central Coast of California that participated in the California Household Travel Survey in 2012–2013. This method explores sequences of daily activity and travel employing techniques from the sequencing of events in the life course of individuals. Studying sequences of daily episodes (each activity and each trip) is preferable to other techniques of studying activity-travel behavior because sequences include the entire trajectory of a person’s activity during a day while at the same time considering the number of activities, order of activities in a day, and their durations jointly. We found substantial fragmentation in activity participation among persons with children and in specific age groups (25–65) amplified by the presence of children in the household. We also found poverty plays an important inhibiting role. Examinations of the days of the week showed significant and substantial differences among the days with both Sundays and Saturdays being distinct, but also substantial differences among the weekdays. The paper provides details about this new technique and the statistical analysis of fragmentation. It also provides a discussion about future steps.

Studying the relationship between activity participation, social networks, expenditures and travel behavior on leisure activities

In the context of an increasing interest in understanding travel for non-mandatory activities, such as recreation and socializing, this work focuses on studying the relationships between activity participation, social networks, and expenditures in daily travel patterns associated with leisure activities in order to understand people’s strategies for performing activities in daily life. Using a 7-day time use diary from a resident sample of Concepcion, Chile, along with information about people’s socio-demography, social network and expenditure behavior, structural equations models were estimated to study the role of social networks on people’s space–time and monetary patterns. The results suggest a positive relationship between people’s interaction with their social networks, their expenditure levels, and their space–time activity patterns. The analysis adds empirical evidence towards a better understanding of people’s decision-making processes by using a time use and a social networks approach. The model results reveal that out-of-home leisure time has a strong impact on the interactions with alters and monetary expenditures. In this context, “with whom” and how much time someone spends doing a specific activity act as key intermediary dimensions to explain leisure activity participation and travel behavior.

Using parental active travel behavior and beliefs to predict active travel to school among children

Active travel (AT) is associated with improved health in adults and children, and children’s AT to school has steadily declined in recent years. Understanding influences on AT will inform intervention development to increase AT among youth. Therein, this study aimed to examine the relationship between AT of children to school and parental AT behavior and beliefs. This was a cross-sectional study of employed adults conducted via an online survey. Participants reported the number of times per week their children, themselves and their spouses actively traveled. Beliefs for AT were also measured. Logistic regressions predicted child AT to school based on parental AT behavior and beliefs. Parents (n = 344) were primarily white (85.8%), female (66.3%), overweight or obese (48.0%) and married (82.6%), with high levels of income and education and reported that 12.2% (n = 41) of their children were active travelers. Children had higher odds of AT to school if the parent actively traveled (Odds ratio [OR] = 1.23 [1.12–1.34], p<.001) or the spouse was an active traveler (OR = 1.28 [1.14–1.45], p<.001). Speaking with children about reasons for AT (OR = 1.61 [1.19–2.18], p=.002), children being eager to actively travel to school (OR = 4.20 [2.82–6.25], p<.001), and parental AT as a child (OR = 1.29 [1.03–1.60], p=.02) were associated with more AT, and odds of children actively traveling to school were lower with higher parent body mass index (OR = 0.87 [0.79–0.96], p=.005) and more cars in the household (OR = 0.47 [0.30–0.72], p=.001). These findings suggest that children’s AT to school is directly influenced by the travel behavior, attitudes, and beliefs of parents.

Determinants of Pro-Environmental Activity-Travel Behavior Using GPS-Based Application and SEM Approach

Technological advancement in automobile and infrastructure sector encourages more and longer distance travel and the way people travel to perform their daily tasks creates environmental problems. Activity-travel behavior of individual have a significant potential to be influenced to reduce environmental issues, however, the underlying factors need to be investigated. This paper investigates pro-environmental activity-travel behavior using recorded GPS-based travel-activity diaries and individual personal traits using online questionnaire and estimating a structure equation model borrowed from theory of planned behavior. The results of the study verified that individual mobility decisions were highly influenced by the attitude one has about specific travel behavior. The results are helpful in devising effective behavioral intervention.

How will automated vehicles shape users’ daily activities? Insights from focus groups with commuters in the Netherlands

Automated Vehicles (AVs) are expected to allow their users to engage in a broad range of non-driving activities while travelling, such as working, sleeping, playing games. The impact of this possibility on the satisfaction with travel and on travel demand has been extensively discussed in the literature. However, it has been hardly recognised that the availability of on-board activities influences the (time-geographic) constraints of daily activities and may alter the selection, location, and sequencing of other activities in the day. This hampers correct representation of travel behaviour in activity-based models aiming to predict the effects of AVs on mobility and environment (e.g., greenhouse gas emissions). To help fill this gap, we gathered and analysed qualitative data from focus groups, in which 27 commuters discussed their expectations concerning on-board activities and daily schedules in the AV-era. Among the core insights are the following three. First, it is useful to separate in modelling the satisfaction with travel and the potential for on-board activities during travel: they have different determinants and different consequences for activity schedules and individual travel demand. Second, on-board activities may be classified in 4 quadrants according to their novelty and priority level: this classification is helpful in understanding the potential re-arrangements of daily activities. Third, performing new activities during travel may lead to complex re-arrangements of daily activity patterns; the re-arrangements may ease or also increase time pressure. These, and other reported insights may facilitate more realistic representation of activity-travel behaviour in future travel behaviour models.

The xenophobic tourist

Addressing the global rise of xenophobia, the authors provide the first investigation of tourist xenophobia (TXO) and its role in shaping tourist behavior. Drawing on research from evolutionary and social psychology, they define TXO as a tourist’s perceptual discomfort and anxiety associated with strangers encountered at foreign destinations. The authors develop a reliable, valid and parsimonious TXO scale, and document its importance empirically. The results show that TXO is an important driver behind many, often neglected, behavioral phenomena, such as tourists’ preference for travel-related vaccination, willingness to try local food, preference for group travel, travel insurance and agency-booked travel, willingness to travel and actual travel behavior. The authors conclude by discussing implications of the findings for research, practice and policy.