Activity-travel Behavior Research Articles

Exploring heterogeneity in travel time expenditure of aging populations in the Netherlands: results of a CHAID analysis

The aim of this paper is to contribute to the increasing literature on travel behavior and time use of the elderly. The Dutch National Travel Survey, administered in 2009, was used as a data source. First, various facets of activity-travel patterns of the elderly were compared against overall sample averages. Results indicate that the new generation of elderly people do not differ that much from other age groups in terms of their activity-travel behavior. Differences in behavior can be largely understood in terms of constraints acting on agendas. Moreover, travel patterns of elderly are affected by socio-demographic variables. Second, to further qualify the average findings, the Chi-square automatic interaction detection (CHAID) method was applied to explore heterogeneity among the elderly in terms of travel time expenditure. It is analyzed how differences in travel time co-vary with socio-demographics, in addition to activity type, activity duration and travel aspects. The results suggest that the aging population can be systematically broken down into several homogeneous cohort segments. Travel time of elderly groups depends significantly on transport modes, travel motivation, and seven socio-demographic variables (gender, age, living environment, personal net-income, household size and season). Moreover, there is less heterogeneity in travel time of elderly who are older than 75years old. However, for younger elderly people, especially the group aged from 65 to 74years old, heterogeneity affects their travel.

Analysis of influence of fuel price on individual activity-travel time expenditure

Fluctuation in fuel prices may lead to adaptations in people′s activity-travel behavior. Compared to other triggers of behavioral change, the impact of fuel prices has received only scant attention in the literature, especially with respect to short-run change in activity-travel behavior. To gain insight into this issue, travel diaries of a representative sample of individuals in the Netherlands who use the car for daily travel were analyzed. Seemingly unrelated regression analysis was used to examine the effects of fuel price on people′s travel time expenditures for different kinds of activities, differentiating between weekdays and weekends. The results indicate that fuel price is negatively correlated with travel time expenditures by car, and that this relationship differs between weekdays and weekends. When faced with increasing fuel prices, people seem to prefer reducing travel time expenditure by car for compulsory trips more than for leisure trips.

Residents’ activity-travel behavior variation by communities in Beijing, China

China’s transition from a planned economy to a market economy has resulted in many changes in its urban structure and society and provided an opportunity for a quasi-longitudinal case study on the relationship between the built environment and activity-travel behavior. This paper draws upon data from an activity diary survey conducted in Beijing in 2007. The survey sample comprised 652 residents living in Danwei (work unit), commodity housing, and affordable housing neighborhoods. On the basis of the three-dimensional geo-visualization analysis of the space-time path and statistical multivariate regression models of daily travel and leisure time, it was found that both residential spatial factors and socio-demographics influence residents’ daily behaviors. The findings show that Danwei residents have less daily travel time than those who live in commodity housing, but people living in affordable housing endure the longest travel time. Daily leisure time is associated more with individual attributes. We argue that although China’s transition is currently gradual, the Danwei system may continue to play significant roles in daily life, and it might provide a valuable model for neighborhood spatial planning.

Built environments, social environments, and activity-travel behavior: a case study of Hong Kong

The influence of individuals’ living environment such as the built environment on their activity-travel behavior has been an important topic in activity-travel behavior research. Not much research attention has been paid to the social aspect of the living environment, which may impact activity-travel decision making or be an important mediating factor for the built environment. This paper makes an attempt to fill the gap by simultaneously considering the built and social environment in studying their impacts on activity-travel behavior. Using the sample data from an activity-travel diary survey conducted in Hong Kong in 2010, this study employs a structural equations model to investigate the effects of built and social environments on social contacts and activity-travel behavior in time allocation between in-home and out-of-home activities, trip generation and travel time. The study reveals that people living in public and private housing in Hong Kong have significantly different built and social environments, and that built environments significantly determine social environments and exert significant influences on social contacts and activity-travel behavior.

Multi-day activity scheduling reactions to planned activities and future events in a dynamic model of activity-travel behavior

Modeling multi-day planning has received scarce attention in activity-based transport demand modeling so far. However, new dynamic activity-based approaches are being developed at the current moment. The frequency and inflexibility of planned activities and events in activity schedules of individuals indicate the importance of incorporating those pre-planned activities in the new generation of dynamic travel demand models. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that takes into account possible influences of pre-planned activities and events. This paper describes the theory and shows the results of simulations of the extension. The simulation was conducted for six different activities, and the parameter values used were consistent with an earlier estimation study. The results show that the model works well and that the influences of the parameters are consistent, logical, and have clear interpretations. These findings offer further evidence of face and construct validity to the suggested modeling approach.

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables.

Wearable Cameras in Health

The relationships between lifestyle behaviors and health outcomes usually are based on self-reported data. Such data are prone to measurement error. In response, there has been a movement towards objective forms of measurement that have low participant and researcher burden. The papers in this theme issue in the American Journal of Preventive Medicine assess the utility of a new form of objective measurement in health research, namely wearable cameras. These devices can be worn all day and automatically record images from a first-person point of view, requiring no intervention or attention from the subject or the researcher. The most mature visual lifelogging device is Microsoft's SenseCam, a wearable camera worn via a lanyard around the neck. The SenseCam has been increasingly used in health-related research for several years. These theme papers report current research into wearable cameras in health, as presented at the SenseCam 2012 Symposium. Wearable cameras and their associated software analysis tools have developed to the point that they now appear well suited to measure sedentary behaviour, active travel, and nutrition-related behaviours. Individuals may recall events more accurately after reviewing images from their wearable cameras. Aspects of their immediate cognitive functioning may also improve. Despite the benefits of wearable cameras, there are still challenges remaining before their use becomes widespread. Ethical and privacy concerns are important issues that need to be addressed, as well as easy access to devices. In response, an ethical framework and smartphone-based wearable camera capture platform are proposed. In sum, this body of work suggests that the use of wearable cameras will soon be appropriate to understand lifestyle behaviours and the context in which the occur.

Documenting outdoor activity and travel behaviour in persons with neurological conditions using travel diaries and GPS tracking technology: a pilot study in multiple sclerosis

Objective: Persons with multiple sclerosis (PwMS) experience several physical and cognitive problems which can influence their travel behaviour. This study aimed to document the number of activities, the activity type and the transport mode of the related trips that are daily made by PwMS. Their outdoor activity and travel behaviour was studied in relation to disease-related disability. Methods: Thirty six PwMS (Expanded Disability Status Scale, EDSS, 1.5–8.0, age 27–63) and 24 healthy controls (age 25–62) were studied, using activity-related travel diaries and GPS tracking devices. Information about overall disability characteristics was gained by standard clinical tests and questionnaires. PwMS were further divided in three subgroups based on EDSS cut-off scores 4.5 and 6.5. Results: Persons with mild ambulatory dysfunction (EDSS 1.5–4.0, n = 17) showed similar travel characteristics to healthy controls, with few restrictions during travelling. Statistically significant changes in activity and travel behaviour were detected in the moderate (EDSS 4.5–6.5, n = 8) and severe MS subgroups (EDSS > 6.5–8.0, n = 11) compared with healthy controls: driving independently became less frequent, significant more trips were made with company and the duration of performed activities had increased. Conclusion: The combination of self-reported travel diaries and objective GPS loggers offered detailed information about the actual outdoor travel behaviour of PwMS, which was significantly changed in PwMS with EDSS greater than 4.Implications for RehabilitationActivity and travel behaviour changes significantly in persons with multiple sclerosis (MS) with moderate to severe disability (EDSS greater than 4).Behavioural therapy could help to develop better coping and problem-solving skills to overcome anxiety in the making of trips by persons with MS with a mild severity.Enhancing community environments could serve as a promising approach to increase the outdoor participation of persons with (more severe) impairments.

Annotating mobile phone location data with activity purposes using machine learning algorithms

Individual human travel patterns captured by mobile phone data have been quantitatively characterized by mathematical models, but the underlying activities which initiate the movement are still in a less-explored stage. As a result of the nature of how activity and related travel decisions are made in daily life, human activity-travel behavior exhibits a high degree of spatial and temporal regularities as well as sequential ordering. In this study, we investigate to what extent the behavioral routines could reveal the activities being performed at mobile phone call locations that are captured when users initiate or receive a voice call or message. Our exploration consists of four steps. First, we define a set of comprehensive temporal variables characterizing each call location. Feature selection techniques are then applied to choose the most effective variables in the second step. Next, a set of state-of-the-art machine learning algorithms including Support Vector Machines, Logistic Regression, Decision Trees and Random Forests are employed to build classification models. Alongside, an ensemble of the results of the above models is also tested. Finally, the inference performance is further enhanced by a post-processing algorithm. Using data collected from natural mobile phone communication patterns of 80 users over a period of more than one year, we evaluated our approach via a set of extensive experiments. Based on the ensemble of the models, we achieved prediction accuracy of 69.7%. Furthermore, using the post processing algorithm, the performance obtained a 7.6% improvement. The experiment results demonstrate the potential to annotate mobile phone locations based on the integration of data mining techniques with the characteristics of underlying activity-travel behavior, contributing towards the semantic comprehension and further application of the massive data.

Semantic Annotation of Global Positioning System Traces

Because of the rapid development of technology, larger data sets on activity travel behavior have become available. These data sets often lack semantic interpretation. This lack of interpretation implies that annotation of activity type and transportation mode is necessary. This paper aims to infer activity types from Global Positioning System (GPS) traces by developing a decision tree–based model. The model considers only activity start times and activity durations. On the basis of the decision tree classification, a probability distribution and a point prediction model were constructed. The probability matrix described the probability of each activity type for each class (i.e., combination of activity start time and activity duration). In each class, the point prediction model selected the activity type that had the highest probability. Two types of data were collected in 2006 and 2007 in Flanders, Belgium (i.e., activity travel data and GPS data). The optimal classification tree constructed contained 18 leaves. Consequently, 18 if–then rules were derived. An accuracy of 74% was achieved when the tree was trained. The accuracy of the model for the validation set (72.5%) showed that overfitting was minimal. When the model was applied to the test set, the accuracy was almost 76%. The models indicated the importance of time information in the semantic enrichment process. This study contributes to future data collection in that it enables researchers to infer activity types directly from activity start time and duration information obtained from GPS data. Because no location information is needed, this research can be easily and readily applied to millions of individual agents.

Exploring the Dynamics in Travel Time Frontiers

Understanding people's travel time expenditures and the factors contributing to such expenditures can provide valuable insights into activity travel demand under a range of policy scenarios. The role of time in activity travel behavior is therefore of great interest to researchers. While the travel time expenditure constitutes the actual time spent traveling in a day, the expenditure does not necessarily constitute the upper limit of the amount of time that people are able and willing to dedicate to travel. In this context, it may be feasible to define an unobserved travel time frontier that represents an upper bound (under usual conditions) on the amount of time that people are able to dedicate to travel. A stochastic frontier modeling approach is adopted to estimate the travel time frontier, as this is an unobserved behavioral characteristic. Models are estimated for a sample of households from 3 years of the National Household Travel Survey in the United States to explore the dynamics of the frontier over a 15-year period. The variations in frontier values—as well as trends in the ratio of expenditure to frontier values, which may be interpreted as a metric of time pressure—differ considerably across sociodemographic groups; this finding calls for the formulation of policies that consider the unique mobility needs of different market segments.

Incorporating Intrahousehold Interactions into a Tour-Based Model of Public Transport Use in Car-Negotiating Households

Intrahousehold interactions in travel are fundamental to an understanding of activity travel behavior, as reflected by the substantial percentage of regional travel that is made jointly. The development of travel demand models that incorporate intrahousehold interactions is crucial to a credible analysis of traveler response to policies. A tour-based modeling framework is used to examine intrahousehold interactions in travel mode choice with a focus on public transport use in households having differences in car availability. An important distinction is made between car-sufficient households (in which there are at least as many cars in the household as license holders) and car-negotiating households (households that have fewer cars than license holders). Intrahousehold interactions and temporal–spatial constraints are explicitly represented by different patterns of joint household tours, with home-based tours as the unit of analysis. The empirical analysis is based on a nested logit model that was developed to integrate intrahousehold interactions with tour-based mode choices; Sydney Household Travel Survey data are used. The results show that joint household travel accounts for more than half of weekday home-based tours in Sydney, Australia. The arrangement of joint household tours is shown to depend on household context, situational factors, and social constraints. Mode choice associated with different joint tour patterns is influenced by household and individual characteristics, tour attributes, and transport-related fringe benefits.

Modeling Gender-Based Differences in Mode Choice considering Time-Use Pattern: Analysis of Bicycle, Public Transit, and Car Use in Suzhou, China

Activity-travel behavior differs between women and men. Previous researches dealing with gender differences mainly focus on travel in itself rather than the interaction between time-use pattern and travel mode choice. Based on the activity-travel survey data of Suzhou, China, multigroup structural equation modeling is adopted to explore the reason for gender-based differences in mode choice through comparing the interactions among sociodemographics, time-use pattern, and mode choice. The results indicate that gender-based differences do exist in mode choice. Women prefer traveling by bicycle while men prefer traveling by car in Suzhou. And compared to women, men's mode choice is not so easy to be affected by other travel modes. Besides, gender-based differences exist in the magnitude or the sign of the interrelations among socio-demographics, time-use pattern, and mode choice. It is better to explain gender-based differences in mode choice by including time-use pattern endogenously than through socio-demographics alone. Furthermore, the study shows that by examining the direct, indirect, and total effects in the model system simultaneously, we are able to better capture the differences in mode choice across genders and further able to understand the reason for those differences. Finally, some dedicated suggestions are presented for planners and government to ensure a healthy transportation system.

Environmental Correlates of Active Travel Behavior of Children

This study explored the participation of children in walking and bicycling for transportation, school, and various leisure purposes, and the relation with social and physical environmental characteristics and sociodemographics. Detailed individual travel data, including all walking and bicycling trips from a random sample of 4,293 children in the primary-school-age category in the Netherlands were investigated. Specifically, a Bayesian belief network was proposed that derives and represents all direct and indirect relations between the variables. The participation in active travel behavior has a direct relationship with all trip characteristics such as travel time and distance, and trip purpose, and is related to the car possession of the household. The degree of urbanization also is an important explanatory variable for participation in walking and bicycling by children. All the other social and physical environmental characteristics have an indirect influence on travel mode choice.

The dual role of weather forecasts on changes in activity-travel behavior

A deeper understanding of how human activity-travel behavior is affected by various weather conditions is essential for both policy makers and traffic managers. To unravel the ambiguity in findings reported in the literature, the main objective of this paper is to obtain an accurate assessment of how weather forecasts trigger changes in Flemish activity-travel behavior. To this end, data were collected by means of a stated adaptation experiment, which was administered both on the Internet and via traditional paper-and-pencil questionnaires. To address the main research question of this paper, two statistical techniques were adopted. The first technique is the computation of Pearson chi-square independence tests. The second approach is the estimation of a GEE-MNL-model. The results from both techniques underscore the dual role of weather forecasts on changes in activity-travel behavior. On the one hand, the results clearly illustrate the significant effect of forecasted weather; the likelihood of changes in activity-travel behavior significantly depends on the weather forecasted. On the other hand, different methods of acquiring weather information (exposure, media source, or perceived reliability) do not impact the probability of behavioral adaptations. This duality may be partially attributable to the discrepancy that exists between weather forecasts and true traffic and roadway conditions. Therefore, the implementation of a road weather information system that is directly linked to the weather forecasts is recommended.

The relation between push motivation and activity consumption at the destination within the framework of a destination card

Abstract This research analyses the influence of tourist’ psychological motivation visiting a destination on their actual travel behaviour and the use of this information for bundling tourism attractions and services in a destination card. The relation between push motivation and activity consumption at the destination is recognized in the literature. The paper extends this evidence by introducing activeness indicators measured according to the amount and type of activity participation normalized to the length of stay. Regarding destination cards the paper investigates, through the use of ordered logistic regressions, four requirements (monetary, timesaving, informational and customization), defined as the benefits of a card tourists may find important. The model results show a significant relation between card requirements and both activeness and motivation, suggesting that destination cards need to be based on natural and cultural attractions, whereas entertainment, sport and social activities can be only additional benefits on discounted price. The data has been collected in the Ticino region, Switzerland. The sample refers to 586 face to face interviews with tourists visiting the area.

Gender-Role Interaction in Activity Participation between Nuclear Household Heads: A Case Study of Suzhou

Based on the 1235 cases of data collected in the survey of Suzhou, a structure equation model is developed to evaluate how gender-role interactions occur in two levels: attribute-activity and activity participation itself. The results show that those two kinds of interactions between two household heads do exist and strongly affect each other’s subsistence, maintenance and leisure activity participations. Household attributes and children’s age are found to have different effect on male and female heads. It indicates that one important reason for male and female differ in activity-travel behavior is that they receive different interactions in household from counterparts. As expected, those results show that different TDM policies should be made aiming at women and men. It will help to better reflect the behavioral responses of household heads to changes in demographic characteristics and to get a deeper understanding of gender difference in activity behavior in developing countries.

The design of a strategy to encourage voluntary travel behaviour change: a cap and save programme

The objective of this work is to explore the contribution of daily activity-travel patterns to carbon emissions, and to define the steps for the implementation of an effective behavioural strategy to encourage voluntary travel behavioural changes. This work proposes an extensive review of the most relevant strategies implemented to achieve sustainable objectives. In particular, the focus is on those strategies aimed at changing human behaviour, debated both in transportation (Structural strategies) and in sociological and psychological (Cognitive-Motivational strategies) fields. Further, international experiences of Voluntary Travel Behavioural Changes programs, as opposed to compulsory measures (i.e. taxation, restrictions etc.), are investigated. Finally, the work describes the results of a pilot survey held in Cagliari (Italy) to test a behavioural strategy called "Cap and Save". The basic idea of the Cap and Save is that voluntary travel behavioural changes are more likely when the individuals are able to recognize a personal profit. The Cap and Save program combines a number of key aspects from behavioural strategies reviewed in the literature (i.e. Tradable Exploitation Rights, Personal Journey Planning etc.): first, individuals are free to modify their behaviour; second, a cognitive-motivational process is set forth to increase awareness of sustainable behaviours. Third, each individual receives an annual emissions limit (cap) and a monetary incentive (save) to reduce emissions; fourth, a set of personalized alternatives is tailored for the individual in order to reduce weekly mileage. Finally, the Cap and Save program relies on an accurate analysis of activity-travel behaviour before and after policy intervention. The initial test of the Cap and Save programme was conducted during a two-week survey (July-October 2009), which involved a group of students from the University of Cagliari (Italy). The first week, the students were invited to record their actual daily activity-travel patterns. The second week, they were asked to repeat the survey, this time they were challenged to maintain a weekly cap of kilometres travelled thereby saving the corresponding resources (i.e. environmental and monetary). Each student was provided with a set of personalized alternatives, which (if followed) would result in a 20% reduction of kilometres travelled. The comparison of before and after strategy implementation highlights the implications of Cap and Save on a wide range of individual daily activities and, specifically, on personal car usage.

Activity based travel demand models as a tool for evaluating sustainable transportation policies

India is in the course of an economic transition. The economic growth nurtured the life in the cities and cities have become a major livelihood destination for everyone. This migration of people contributed to the increased urbanization of Indian cities. The booming economy fostered the well-being and shaped the lifestyle of people in such a way that the dependency on private vehicle has become an unavoidable affair. Along with population growth, the increased vehicle ownership gave rise to overall spurt in travel demand. But the supply side lagged behind the demand adding to many of the transport related externalities such as accidents, congestion, pollution, inequity etc. The importance of sustainability is understood in the current urban transport scenario leading to the development and promotion of sustainable transport polices. The core agenda of these polices is to target the travel behavior of people and change the way they travel by creating a different travel environment. However, the impacts of many such policies are either unknown or complex. Hence, before adopting and implementing such policies, it is important for the decision makers to be aware of the impacts of them. The role of travel demand models comes here as they predict the future travel demand under different policy scenarios. This paper reviews the ability of travel demand models applied in India in analyzing the sustainable transport policies. The study found that the conventional model system in India, which is trip based four step aggregate methodology, is inadequate in analyzing the sustainable transport policies. A review of alternative approach, known as activity based travel demand modeling found that they are capable of handling such policies better than conventional models and are assistive to the decision makers in arriving at right mix of polices specific to the situations. Since there is no operational activity based travel demand model system developed in India, the study at the end envisaged a conceptual framework of an integrated activity based travel demand model based on the requirements identified from the review. This can potentially replace the existing travel demand models and can be used for planning applications once the modification & validation have been done according to the existing activity-travel behavior of individuals.

A Structural Equation Model to Analyze Interrelationship between Activity Participation and Trip-Chaining of Nuclear Household

Travel is derived from the necessity to engage in spatially-separated activities. Previous researches have demonstrated that socio-demographics have effects on activity-travel behavior, but few of them focus on the interactions between activity and trip-chaining, especially the influence of trip-chaining on activity. Based on the activity-travel survey data of Suzhou, China, in 2009, complex interactions between activity participation and trip-chaining behavior of nuclear household are explored using structural equation model. Model estimation results show that trade-off and complementarity exist among different types of activities and trip chains. Besides, trip-chaining generation is deeply affected by activity participation. Subsistence activity negatively affects trip-chaining characteristics, while maintenance and leisure activities positively affect it. Furthermore, feedbacks from trip-chaining characteristics to activity participation do exist. Travel time and the number of trip-chaining have significant effects on activity duration.