Activity-travel Behavior Research Articles

Estimating pro-environmental potential for the development of mobility-based informational intervention: a data-driven algorithm

Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual’s mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention.

The effect of workplace relocation on individuals’ activity travel behavior

On working days, homes and workplaces can be seen as anchor locations at the heart of daily mobility patterns, as well as being central to an employee’s activity pattern. In this study, we investigate how workplace relocation affects the entire daily activity-travel chain. While past research has shown that workplace decentralization is often associated with higher car use for the commuting trip, little is known about the effect on the whole activity travel pattern. Two waves of a two-week travel diary were completed by 43 employees of the University of Luxembourg: one before and one after the relocation of their office. Using descriptive statistics as well as standard deviational ellipses (SDE) theory combined with the results of a clustering analysis showed that workers’ activity spaces (represented by the standard deviational ellipses) were significantly modified due to the relocation of a single anchor activity location, i.e., their workplace.

Children's Independent Mobility: Current Knowledge, Future Directions, and Public Health Implications.

Environmental changes significantly impact health behavior. Active travel behavior is mostly affected by increasing motorization, urban sprawl, and traffic safety. Especially for children, active and independent travel can contribute to physical activity, social and motor development, and other health-related outcomes. A reduced number of children engaging in independent mobility over the last 20 years demanded researchers to further examine the construct of children’s independent mobility. By examining relevant literature, this narrative review aims to provide the current state of knowledge on children’s independent mobility, and identify future directions in research, as well as practical implications. From a public health perspective, considering children’s independent mobility in intervention programs is recommended, since it is associated with numerous health and environmental benefits. To develop interventions, multilevel socio-ecological influences on children’s independent mobility are widely examined; however, evidence is limited due to heterogeneous measurements and a lack of high-quality prospective studies. To oppose the decline in children’s independent mobility, further analysis using comparable measures is needed to understand the determinants of children’s independent mobility and to enable international comparison.

Using virtual accessibility and physical accessibility as joint predictors of activity-travel behavior

In this study, we propose a conceptual and analytic framework anchored on the concepts of physical and virtual accessibility (the “ease” with which opportunities or activities can be reached in the physical and in the virtual space, respectively) to investigate the rich interplay between virtual and physical activity engagements in multiple activity purposes, while controlling for information and communication technology (ICT) use measures, physical accessibility measures, and demographics. Our framework considers that activity-travel choices are consequences of individual, household, and work characteristics that are mediated by virtual accessibility and physical accessibility. As part of our analysis, we also analyze activity chaining characteristics during travel to study any fragmentation impacts caused by ICT use on activity engagement and scheduling. We use data from the 2011 and 2012 National Travel Survey in Great Britain and apply Bhat’s (2015) generalized heterogeneous data model (GHDM) to jointly model multiple activity and travel outcomes. Our results provide important insights for social welfare, work-life balance, and equity policies, and suggest that decisions regarding virtual activity participations and in-person out-of-home activity participations are determined as a package. Ignoring this package nature of choices can lead to misleading inferences about the effects of virtual activity participations on in-person out-of-home activity participations.

Factors associated with adolescent active travel: A perceptive and mobility culture approach – Insights from Ho Chi Minh City, Vietnam

Sustainable transport modes, such as walking and cycling, have great benefits but the uptake is decreasing in Vietnam due to motorisation. Individual perceptions of travel and wider mobility cultures are increasingly seen as an important aspect in shaping travel patterns, in particular active travel. The understanding of this in youth active travel in Vietnam is not clear. The aim of this study is to understand how social factors and the physical environment influence adolescent active travel in Ho Chi Minh City (HCMC). A survey in HCMC targeting adolescents aged 11–16 (n = 525) across nine districts around the city centre was conducted. We adapted a comprehensive conceptual framework of active travel behaviour in the context of data availability and the study area. We also included variables pertaining weather, traffic safety, street vendors and road accident experiences, which are more relevant in powered two-wheeler (PTW) traffic saturated, tropical cities in a South East Asian setting. The results show positive association between favourable built environments (obstruction free pathways, food attractions and tree cover), social interactions (peer groups, parents and neighbourhoods) and negative association with traffic, air pollution and prior accident experiences. The findings highlight the importance of considering the interrelations of variables, whilst also identifying future research directions. Policy makers in HCMC, or in similar cities, should consider developing intervention strategies to improve the attractiveness of active travel.

Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach.

Accessibility is a major method for evaluating the distribution of service facilities and identifying areas in shortage of service. Traditional accessibility methods, however, are largely model-based and do not consider the actual utilization of services, which may lead to results that are different from those obtained when people’s actual behaviors are taken into account. Based on taxi GPS trajectory data, this paper proposed a novel integrated catchment area (ICA) that integrates actual human travel behavior to evaluate the accessibility to healthcare facilities in Shenzhen, China, using the enhanced two-step floating catchment area (E2SFCA) method. This method is called the E2SFCA-ICA method. First, access probability is proposed to depict the probability of visiting a healthcare facility. Then, integrated access probability (IAP), which integrates model-based access probability (MAP) and data-based access probability (DAP), is presented. Under the constraint of IAP, ICA is generated and divided into distinct subzones. Finally, the ICA and subzones are incorporated into the E2SFCA method to evaluate the accessibility of the top-tier hospitals in Shenzhen, China. The results show that the ICA not only reduces the differences between model-based catchment areas and data-based catchment areas, but also distinguishes the core catchment area, stable catchment area, uncertain catchment area and remote catchment area of healthcare facilities. The study also found that the accessibility of Shenzhen’s top-tier hospitals obtained with traditional catchment areas tends to be overestimated and more unequally distributed in space when compared to the accessibility obtained with integrated catchment areas.

Understanding child and parent perceptions of barriers influencing children\u2019s active school travel

BackgroundPhysical activity plays a fundamental role in the health and well-being of children. Walking is the most common form of physical activity and the journey to and from school provides an opportunity for children to be active every day. This study examines how child and parent perceptions of barriers to active school travel influences children’s behaviour.MethodsParticipants were recruited from 48 elementary schools in Southwestern Ontario, Canada. The study sample includes 1296 children (ages 9–14 years) who live within walking distance of their school, defined as 1.6 km network distance. Chi-square analysis examined differences between child and parent perceptions of barriers to active school travel. Logistic regression models examined how parent and child perceptions of barriers influence active school travel behaviour, while controlling for key intrapersonal, interpersonal, and physical environment factors.ResultsThe results indicate that there are significant differences in how parents and children perceive barriers to active school travel. Model results find older children, children without siblings, households with no vehicles, and children who live closer to school are most likely to use active school travel. Parent perceptions of barriers are found to have a greater influence on children’s active school travel behaviour than children’s perceptions. Different perceptions of barriers influence active school travel to school compared to returning home from school.ConclusionsChild and parent perceptions of barriers to active school travel differ and have different impacts on children’s travel behaviour. Understanding how child and parent perceptions of barriers differ can help policymakers and practitioners develop specialized interventions aimed at increasing children’s use of active school travel and children’s overall physical activity. Interventions used to promote active school travel should focus on safety, as well as perceptions of distance to break parental habits of routinely driving their children to school. Overall, this study highlights the importance of considering both child and parent perceptions to create a safe and accessible environment to allow for an increase in active school travel behaviour among elementary school children who live within walking distance of their school.

Impacts of an active travel intervention with a cycling focus in a suburban context: One-year findings from an evaluation of London’s in-progress mini-Hollands programme

BackgroundMore evidence is needed on the impacts of building infrastructure for walking and cycling. A knowledge gap and an implementation gap have been mutually reinforcing. This paper reports on a longitudinal study examining the impacts of the still in progress ‘mini-Hollands programme’, which seeks to transform local environments for walking and cycling, in three Outer London boroughs. Compared to Inner London, Outer London has low levels of cycling and low levels of walking, and is relatively car dependent. MethodsWe conducted a longitudinal study of 1712 individuals sampled from households in mini-Holland boroughs (intervention sample) and from non mini-Holland Outer London boroughs (control sample). The intervention sample was further divided, a priori, into those living in “high-dose neighbourhoods”, where substantial changes to the local walking and cycling infrastructure had been implemented, versus “low-dose neighbourhoods” where such improvements had not (yet) been made. At both baseline (2016) and one-year follow-up (2017), we administered an online survey of travel behaviour and attitudes to transport and the local environment. ResultsOne year’s worth of interventions was associated with an increase in active travel among those living in areas defined as ‘high-dose’ neighbourhoods. Specifically, those in high-dose areas were 24% more likely to have done any past-week cycling at follow-up, compared to those living in non mini-Holland areas (95% CI, 2% to 52%). The mid-point estimate for increase in active travel (walking plus cycling) time for the same group was an additional 41.0 min (95% CI 7.0, 75.0 min). Positive changes in views about local environments were recorded in intervention areas, driven by a perceived improvement in cycling-related items. Controversy related to the interventions is expressed in a growth in perceptions that ‘too much’ money is spent on cycling in intervention areas. However, intervention areas also saw a reduction in perceptions that ‘too little’ money is spent (the latter view being common both at baseline and Wave 1 in control areas). ConclusionOverall, the findings here suggest that programme interventions, while controversial, are having a measurable and early impact on active travel behaviour and perceptions of the local cycling environment.

An efficient hierarchical model for multi-source information fusion

In urban and transportation research, important information is often scattered over a wide variety of independent datasets which vary in terms of described variables and sampling rates. As activity-travel behavior of people depends particularly on socio-demographics and transport/urban-related variables, there is an increasing need for advanced methods to merge information provided by multiple urban/transport household surveys. In this paper, we propose a hierarchical algorithm based on a Hidden Markov Model (HMM) and an Iterative Proportional Fitting (IPF) procedure to obtain quasi-perfect marginal distributions and accurate multi-variate joint distributions. The model allows for the combination of an unlimited number of datasets. The model is validated on the basis of a synthetic dataset with 1,000,000 observations and 8 categorical variables. The results reveal that the hierarchical model is particularly robust as the deviation between the simulated and observed multivariate joint distributions is extremely small and constant, regardless of the sampling rates and the composition of the datasets in terms of variables included in those datasets. Besides, the presented methodological framework allows for an intelligent merging of multiple data sources. Furthermore, heterogeneity is smoothly incorporated into micro-samples with small sampling rates subjected to potential sampling bias. These aspects are handled simultaneously to build a generalized probabilistic structure from which new observations can be inferred. A major impact in term of expert systems is that the outputs of the hierarchical model (HM) model serve as a basis for a qualitative and quantitative analyses of integrated datasets.

Who adopts the Airbnb innovation? An analysis of international visitors to Western Australia

ABSTRACTAirbnb is the most prominent example of novel peer-to-peer networks in tourism. This new form of accommodation provision may alter demand structures in tourism destinations and has led to uncertainty amongst established accommodation providers and destination marketers. To gain a better understanding of Airbnb adopters, this paper systematically compares actual travel behaviour of Airbnb users in a specific destination with that of visitors staying in traditional accommodation. Due to this controlled comparison, the paper is the first one to offer a robust comparative profiling of Airbnb users. The analysis is based on a logistic regression of data from the standardised International Visitor Survey for 2015 in Western Australia. Results indicate that Airbnb users in Western Australia differ from visitors staying in traditional accommodation, but that it might be wrong to conceive Airbnb adopters to be ‘alternative tourists’ beyond the mainstream.

Identifying activity-travel points from GPS-data with multiple moving windows

Where GPS technology can precisely register the spatiotemporal elements of activity-travel behaviour, travel characteristics need to be imputed from the data. As such, throughout the last decade, a plethora of methods has been developed to identify trips and activities from raw GPS trajectories. However, rule-based methods that use dwell time for the classification of activity and trip episodes may disregard short activities. In addition, more advanced methods, such as machine learning classifiers, are typically used for travel mode recognition. This paper therefore aims to systematically compare the relative performance of four machine learning algorithms to classify GPS points into activity points and travel points. To account for the underlying spatial structure of the data, attribute information is supplemented by moving multiple spatial windows over preceding and succeeding points to derive local densities. Because ground truth is essential to evaluate the accuracy of the algorithms, a set of 200 artificial GPS activity-travel sequences with varying noise levels was generated to test the performance of the algorithms. The results indicate that especially the random forest algorithm can lead to high accuracies and that local densities are important variables to the accuracy of the classifiers.

Activity start time and duration: incorporating regret theory into joint discrete–continuous models

ABSTRACTActivity start time and duration decisions are two key elements of activity-travel behaviour. As an effort towards a more realistic representation of individuals’ decision behaviour, this paper presents a new copula-based joint model of activity start time and duration. We incorporate the regret theory into the joint modelling framework, which assumes that individuals tend to avoid the situation where non-chosen alternatives outperform the chosen one in one or more attributes. The proposed joint model comprises a hybrid utility-regret model as the discrete component to estimate activity start time and a hazard duration model as the continuous component to estimate activity duration. The comparative analysis of estimation results reveals that the proposed structure is statistically superior to the utility-based joint model and independent models. We also found that applying the regret-based decision rule for variables of travel time and travel time variability significantly improves the model.

Investigating inter-generational changes in activity-travel behavior: a disaggregate approach

Investigation into the long-term trends in travel behavior is crucial for strategic development of transport systems and decisions about investment in transport infrastructures. There has been a debate about if there is a constant amount of time allocated for travel and thus an upper limit to daily travel demand. Recent studies have suggested a stagnated and even declining trend of car ownership and travel demand in developed countries (the so-called ‘peak car’ and ‘peak travel’ phenomenon). This study aims at exploring the possible long-term trends in activity-travel behavior in Hong Kong to shed some light on the ‘peak travel’ debate. We have acquired two large-scale datasets from Hong Kong’s Travel Characteristics Survey conducted a decade apart and applied propensity score matching to match individuals of similar socioeconomic backgrounds from different time periods and compare their activity-travel behavior. Results indicate that households and individuals with similar socioeconomic backgrounds at the two periods have distinctively different car ownership levels and daily travel and activity behaviors.

Understanding and Modeling the Activity-Travel Behavior of University Commuters at a Large Canadian University

AbstractThis paper examines the daily activity-travel behavior of undergraduate students, graduate students, faculty, and staff at a large university to better understand their activity travel dema...

Faktory volby dopravního prostředku – podstatný aspekt proměny současných měst

The aim of the paper is to answer questions related to the explanation of the travel mode choice used by people for regular commuting and what role in their decision making can be attributed to location of their residence, i.e. how the travel mode choice differs in case of traditional urban residents and suburban residents. The reason why to study everyday commuting is the fact that people currently living in western cities spend a lot of time and money commuting and the aggregate of everyday individual mobility generates a significant environmental impact. I study the travel mode choice from two perspectives: a) statistical and b) behavioural. Using the statistical analysis I explain the travel mode choice based on situational constraints and differences between social groups. The statistical analysis, however, is unable to explain how the situational constraints lead to different behaviour in different individuals and also the part of behaviour that cannot be explained using the constraints, thus I add a qualitative study. Summarizing both types of findings, I conclude that the everyday travel mode choice depends to a large extent on constraints that cannot be influenced by individuals: i) the amount of time and money available to the individual, ii) public transport services, civic facilities in the place of residence, and iii) required mobility for job related reasons. In the qualitative study I focus on what type of residence people prefer. Looking into preferred characteristics of people’s place of residence and actual travel behaviour I generate general classification of rationality related to the travel mode choice: 1) travel mode choice rationality based on constraints, 2) a consistent lifestyle including travel mode as well as nature of residence choice, and 3) the travel mode choice as an unreflected habit. According to the three types of rationality we can identify different factors of individual transport development: a) differences in the nature of individual travel modes and their attractiveness related to individual economic and professional constraints, b) real willingness of many people to live in residential locations with a lower accessibility and lower level of services, and c) a habit to drive a car automatically regardless of conditions as a natural part of habitus of economic middle and upper classes. Differentiation of these factors leads to different implications when considering possibilities of overturning the current increasing trend of car transport. Firstly, it is a question of price or other regulation changing attractiveness of the individual alternatives. Secondly, there are factors related to preference of suburban lifestyle including not only normative and cultural patterns but also significant differences in real estate prices in internal cities and suburban locations. In the last case of the habit to use a car regardless of available alternatives as a part of habitus of economic middle and upper classes the discussion about possible changes is the most demanding. However, this study shows that majority of people make their travel mode choices taking into account real possibilities and constraints and thus that it is possible to influence their behaviour through economic and other types of regulation.

Design of a feedback intervention to increase travel related physical activity of CVD patients

Cardiovascular disease (CVD) patients benefit from an active lifestyle with a certain level of physical activity. Assessing the physical activity level of patients in an objective manner can be problematic. Self-reporting tends to be biased and lack of therapy adherence has a negative influence on managing disease risk factors. In this paper we propose a digital framework which collects the level of physical activity of CVD patients with an app and processes this data to obtain an objective measure of physical activity, which is visualized in a dashboard available for the caretakers. By exploiting behavioural theories and combining them with this objective measure of physical activity, patients are classified according to their attitude towards active travel behaviour. Based on this knowledge, caretakers can propose a more active lifestyle to patients by identifying opportunities in making the daily trips of the patients more active. For example, short distances done by car can be suggested to be replaced by walking or biking. The behaviour theories also allow to assess the risk of not adhering to the prescribed therapy. This tool will help in providing a more tailored care and approach to persons with CVD.

Connotative meaning of travel modes and activity-travel behavior

Abstract This paper aims to present results of a study regarding how the meaning of travel modes influences on the performance of activities and trips. Connotative meaning of travel modes was measured using semantic differential scales, which collect the suggestive significance of the terms used to name current travel modes. More than 400 people provided that information together with values and other types of attitudes, characteristics of their usual companions, socio-demographics, and 2-days activity-travel diary that includes a weekend day and a weekday. Descriptive analysis, which include frequency distributions, Factor Analysis, and correlations, have been developed to find potential relations among the studied variables. Maintenance activities are the most frequent, followed by discretionary activities. Only 10% of the total activities are mandatory. Approximately one third of trips are carried out by private vehicle. Motorized travel modes are characterized as noisy and powerful, in particular cars, while walking and cycling are simple and quiet.

Activity-Travel Behaviour, and Daily and Global Well-Being

Using a hierarchical SEM and multidimensional 3-week household time-use and activity diary, this study investigated how interaction of individuals’ daily travel parameters, time-use and activity participation and percentage of undertaking passive leisure within various activity participation, life circumstances, and geographical conditions shape individuals’ daily and global subjective well-being. This study confirms that life circumstances insignificantly shape people’s well-being as argued as well in previous studies. Moreover, daily subjective well-being or people daily context in which contains how people organizes their daily activity-travel behaviour positively shape people life satisfaction as hypothesised. This study also confirms that different daily activity participation tends to shape different level of people’s daily subjective well-being. Spending more time-use for leisure, sport and grocery shopping tends to positively correlate with having better daily subjective well-being. Having better mental and social health are found to positively shape people’s daily and global well-being, respectively. For policy implementations, this study can say that providing more opportunities for undertaking out-of-home activities such as out-of-home leisure, sport and grocery shopping with time-use policy and denser land use planning.

Pro-Environmental Potential in Activity-Travel Routine of Individuals: A Data Driven Computational Algorithm

Informational interventions are considered important to bring positive changes in attitudes and perception about pro-environmental life styles among individuals. In relation to mobility aspects, it is vital to identify relatively easier changes that have potential to reduce negative impacts of mobility on environment and individual health. This paper provides a comprehensive methodological framework and developed a computation algorithm that helps identify such an easy changes in the travel behavior of an individual. The development of algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data. A variety of rules that are part of the computational algorithm are taken from the transport modelling literature, where constraints and factors were examined for various activity-travel decisions. Three major aspects of activity-travel behavior such as lesser car use, cold start of car engines and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to data collected, using citizens from Hasselt and their pro-environmental potential is determined, which has been found significant.

“How Come You Are Here?” Considering the Context in Research on Travel Decisions

Travel decision research still struggles to explain a large portion of the variance in travel choices. We argue that advances in this domain must originate from a shift in the kinds of questions we ask travelers to understand what triggers their decisions. The proposed shift from “Why did you . . . ?” to “How come . . . ?” changes the emphasis from retrospective sense giving to a contextual understanding of travel choice, focusing in particular on the constellations that produce actual travel behavior. This shift opens research avenues of a new theoretical and methodological nature and has fundamental implications for consumer research as well as destination marketing practices.