Activity-travel Patterns Research Articles

Exploring the links between personal networks, time use, and the spatial distribution of social contacts

Although the study of the role of the social context in travel behavior and activity patterns has recently gained attention, the empirical evidence supporting the relationship between social networks and the temporal and spatial characteristics of social activities is still limited. With this motivation, this paper studies the link between “longer term” (social networks) and “shorter term” (social activities) social decisions, by exploring the intertwined relationship between the individuals’ personal networks attributes, and the spatiotemporal characteristics of their daily social activities. The paper contributes to the literature by adding two key aspects to the study of the role of social networks on travel behavior: the social networks’ structure, and the spatiality of all individuals participating on the social activities. Based on data which link people’s personal networks and time use, and using a structural equation modeling approach, the paper studies the influence of individual and interactional attributes on the duration, distance, and number of people involved in social daily activities. The results show that aspects such as tie social closeness, gender and age similarity, and network density, help to understand social activity duration and distance, complementing traditional socio-demographic aspects such as income, occupation, and accessibility to services. In this way, socio-demographic attributes are not enough to explain the spatiotemporal dimension of daily activities which makes necessary to include variables related to the social context to explain with a higher level of accuracy both the duration and distance traveled to the activity.

Location choice in the context of multi-day activity-travel patterns: model development and empirical results

Multi-day activity-based models of travel demand are receiving increasing interest recently as successors of existing single-day activity-based models. In this article, we argue that predicting activity location choice-sets can no longer be ignored when multi-day time frames are adopted in these models. We develop a model to predict activity location choice-sets and choices from these sets conditionally upon varying activity schedule contexts. We propose a method to estimate parameters of the involved utility functions that do not require observations or imputation of choice-sets. This is achieved by using Bayes’ method to transform the likelihood of chosen locations into a likelihood of attribute profiles of chosen locations. An application of the method using a national travel diary dataset illustrates the approach.

Analysis on Residents’ Travel Activity Pattern in Historic Urban Areas: A Case Study of Historic Urban Area of Yangzhou, China

Travel behaviors and activity patterns in the historic urban area of a city are expected to be different from the overall situations in the city area. The primary objective of this study is to analyze the residents’ travel activity patterns in historic urban area. Based on survey data conducted in the historic urban area of Yangzhou, the travel activities of local residents in a whole day were classified into five types of patterns. The multinomial logit (MNL) model was developed to evaluate the impacts of explanatory variables on the choices of activity patterns. The results showed that the choice of activity pattern was significantly impacted by five contributing factors including the gender, age, occupation, car ownership, and number of electric bikes in household. The other variables, which were the family population, preschoolers, number of conventional bikes in household, motorcycle ownership, and income, were found to be not significantly related to the choice of activities. The results of this study from historic urban area were compared to findings of previous studies from overall urban area. The comparison showed that the impacts of factors on activity pattern in the historic urban area were different from those in the overall area. Findings of this study provide important suggestions for the policy makings to improve the traffic situations in historic urban areas of cities.

Learning and Affective Responses in Location-Choice Dynamics

In this paper we discuss the development of a dynamic agent-based model which simulates how agents search and explore in nonstationary environments and ultimately develop habitual, context-dependent, activity–travel patterns. Conceptually, the creation of a choice set is context dependent. Individuals are assumed to have aspiration levels associated with location attributes that, in combination with evaluation results, determine whether the agent will start exploring or persist in habitual behavior. An awareness level of each location determines whether or not it is included in the awareness set in the next time step. An activation level of each location determines whether or not it is qualified as a habitual choice, and an evaluation (utility) function allows individuals to evaluate each location given current beliefs. By implementing choices, agents may observe the differences between actual experience and expectation, which may give rise to negative or positive emotions that influence the awareness of locations and the evaluation, and hence trigger choice change. Principles of reinforcement and Bayesian belief learning are used to simulate the dynamics. The result of these behavior mechanisms is the evolution of choice sets and choice patterns, reflecting emergent behavior in relation to nonstationary environments. We report the results of a case study, implemented in an agent-based microsimulation system, of dynamic decision making of avoiding higher uncertainty in location choice, distinguishing habitual, exploitation, and exploration modes of choice behavior. Simulations indicate that solutions generated by the model are sensitive to rational and emotional considerations in decision making in well-interpretable ways. The suggested approach is scalable in the sense that it is applicable to study areas of large size (eg, region wide).

Modeling the Connection between Activity-Travel Patterns and Subjective Well-Being

Transportation models are currently unable to reflect adequately the impacts of policy and investment decisions on people's well-being and overall quality of life. This paper presents a multivariate ordered-response probit model that is able to capture the influence of activity-travel characteristics on subjective well-being while accounting for unobserved individual traits and attitudes that predispose people in relation to their emotional feelings.

A Statistical Synthetic Population Calibration for Activity-Based Model with Incomplete Census Data

Synthetic population generator is the core component of the microsimulation in activity-based travel demand model. Typically, synthetic population is used in the way that their decisions on activity-travel pattern are simulated. Traditionally, household sample survey data is used to synthesize the population. The estimated results can be biased due to such as low-sampling size and inaccurate household sample data. To deal with this issue, a statistical maximum-likelihood method to calibrate synthetic population using the roadside observations (link counts) is proposed. Statistical performances of the proposed method are evaluated on the illustrative network and real network with census and household sample survey data. Multi-day link counts are simulated from (true) activity-based model parameters and synthetic population. Tests are carried out assuming different numbers of observations and observation variations. The results illustrate the efficiency of the model calibration based on link counts and its potential for large and complex applications.

Uncertainty in Predicted Sequences of Activity Travel Episodes

The application of uncertainty analysis requires appropriate statistics that capture the degree of uncertainty in model forecasts. The prediction of activity-based models of travel demand relates to (a) aggregate performance indicators, (b) origin–destination tables and corresponding traffic flows, (c) individual space–time trajectories, and (d) the sequence of activities that are conducted during the day. Because measures to quantify uncertainty in the sequence of activities have not been developed yet, the aim of this paper is to propose a method to measure model uncertainty in predicted activity travel sequences. The proposed method involves generating predicted activity travel patterns for a set of model runs and quantifying the uncertainty in the sequential information embedded in these patterns by calculating the average effort across all possible pairs of predicted sequences to align these multidimensional sequences. The effort is quantified in a multidimensional extension of the Levenshtein distance. Because computational costs may become prohibitive in large-scale applications of this method, several heuristic approaches are suggested and examined. Results indicate that the suggested heuristics (a) can represent uncertainty in predicted activity travel sequences quite well, (b) tend to approximate the calculated uncertainty on the basis of all possible sequences, and (c) do not however necessarily produce asymptotically more accurate results. Implications of these findings are discussed.

A path analysis of social networks, telecommunication and social activity–travel patterns

The aim this study is to analyse the relationships between socio-demographic and land-use variables, social networks, the use of information and communication technology (ICT) and social activity–travel patterns. Using path analysis, hypotheses on these links are tested. The analyses are based on a unique data set collected in 2008 in the Eindhoven region in the Netherlands among 747 respondents, using a 2-day social interaction (and travel) diary and a questionnaire. The results of the path analysis show many significant effects of socio-demographic and land-use variables. In addition, the results suggest that social networks generate social activities and travel. Regarding the relationship between ICT-use and social travel the results indicate that ICT-use has a substitutional (or neutral) effect on social travel, whereas the effect of social travel on ICT-use is neutral or complementary.

A Cognitive Learning Model for Dynamic Activity-travel Patterns

As individuals travel and receive information about the urban and transport environment through information and communication technologies, they will update their mental representation of the built environment, which in turn may influence their activity-travel choices. Thus, in this paper, we propose a model that simulates probabilities based on individual observations in the built environment, explain our model framework, and then present the results of the simulations that are designed to test the face validity of the model.

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.

Assessment of model uncertainty in destinations and travel forecasts of models of complex spatial shopping behaviour

This paper reports the results of an analysis of model uncertainty in the context of shopping behaviour. This analysis is positioned in a discussion on the evolution of models of spatial shopping behaviour. For different generations of shopping models, the potential of uncertainty analysis is discussed. The actual analysis is based on an application of the Albatross model to the city of Rotterdam, The Netherlands. Characteristic of this model is that shopping trips are embedded in the prediction of daily more comprehensive activity-travel patterns, adding complexity and realism to traditional models of spatial shopping behaviour, such as the Huff and spatial interaction model. Results show that overall the uncertainty associated with the aggregate outcomes of the model is small. It is higher for the total number of visits to selected shopping areas.

Integration of population mobility in the evaluation of air quality measures on local and regional scales

By focussing on air pollutant concentration levels only, the variation in population mobility is not taken into account when assessing the exposure. Transportation policies have an impact on both concentration levels and mobility patterns. The impact of a fuel price increase policy on population exposure to elemental carbon (EC) was evaluated and compared to the base scenario (current situation), taking into account time-activity patterns – including time in commute. We assessed the effect on exposure of both the change in concentrations and whereabouts. The decrease in exposure due to the fuel price increase using residential information only was limited to areas near highways and urban centres. Integrating population movement, exposures to EC were higher and the decrease in exposure was no longer limited to areas near traffic hotspots. For inhabitants of urban areas, the exposure integrating time-activity patterns was more similar to the residential exposure, as they spent more time in their own neighbourhood. For people living further away from traffic hotspots, the estimated impact of the policy was higher than expected for residential exposure. These people profited both from the higher decrease in concentrations at their work/shop/leisure destinations in more urban areas and, as they have to travel longer, also had a larger gain from the high decrease in concentrations during transport. Therefore, the impact of changing concentrations is underestimated when using residential exposure only. These results show the importance of taking into activity-travel patterns when planning future actions.

Tracking household routines using scheduling hypothesis embedded in skeletons

This study addresses complex daily activity-travel routines of households with young children and their proper representation in a computational process model of travel demand using family skeletons expressed as family sequence patterns. Building on qualitative interview research findings, an a priori classification of family types is defined according to the distribution of care and work responsibilities in the household on a typical weekday. Enriched census data are examined to calculate the share of each family type in the region of Flanders in Belgium. Next, individual activity-travel sequence patterns are drawn for children and adults. Finally, these individual sequences are combined to family sequence patterns, yielding a concise representation of skeletal information in activity-travel patterns of household members and their interrelationships. This process is tested and the method offers a promising approach to both household activity-travel analysis and travel demand modelling.

Choice set formation with multiple flexible activities under space–time constraints

In classical time geography, an individual travel path is composed of a chain of visits, with each visit being a flexible activity between two fixed activities at two known stations. In reality, individuals tend to carry out trips with much variation and complexity, with multipurpose trips being a prominent and pervasive phenomenon. There is limited research to date on multipurpose trips in time-geographic analysis by geographic information system (GIS) scientists, or more specifically, multiple flexible activities between two fixed stations. To fill this gap, this article proposes four models for identifying the choice set with multiple flexible activities under space–time constraints. The models are derived through set-theoretic formalism based on the concept of trip chaining. The structure of the four models establishes a theoretical framework for conceptualizing trip-chaining behaviour with respect to the fixity of activities and the number of fixed stations as destinations or origins. They provide fundamental and rigorous apparatus for studying complex individual activity–travel patterns in many applied contexts when multipurpose trips are involved. This article also describes implementation of the models with a real transportation network as a way of validation.

Probabilistic potential path trees for visualizing and analyzing vehicle tracking data

Vehicle tracking data are often used to explore human travel behavior and activity patterns. Time geography is a useful approach for analyzing such datasets, as it provides a means for identifying the set of possible routes and stops for a vehicle between known locations, which is termed a potential path tree. This research extends the utility of the time-geographic approach by developing a method to generate probabilistic potential path trees that represent the space–time potential of a vehicle’s movements. First, this research provides the mathematical formulation of the new technique, network-based time-geographic density estimation (TGDE), and demonstrates the computation using a hypothetical tracking dataset and road network. Its formulation operates as a network adaptation of classical TGDE, which has been previously employed to analyze the movements of objects travelling in continuous, Euclidean space. Second, network-based TGDE is applied in the context of analyzing vehicle tracking data collected by GPS and filtered to protect an individual’s privacy. The method was used to map and quantify the vehicle’s most likely routes, origins, intermediate stops, and final destinations. The results indicate network-based time-geographic density estimation provides a powerful approach for both geovisualizing and analyzing vehicle tracking data.

Scalable Activity-Travel Pattern Monitoring Framework for Large-Scale City Environment

In this paper, we introduce Activity Travel Pattern (ATP) monitoring in a large-scale city environment. ATP represents where city residents and vehicles stay and how they travel around in a complex megacity. Monitoring ATP will incubate new types of value-added services such as predictive mobile advertisement, demand forecasting for urban stores, and adaptive transportation scheduling. To enable ATP monitoring, we develop ActraMon, a high-performanceATP monitoring framework. As a first step, ActraMon provides a simple but effective computational model of ATP and a declarative query language facilitating effective specification of various ATP monitoring queries. More important, ActraMon employs the shared staging architecture and highly efficient processing techniques, which address the scalability challenges caused by massive location updates, a number of ATP monitoring queries and processing complexity of ATP monitoring. Finally, we demonstrate the extensive performance study of ActraMon using realistic city-wide ATP workloads.

Distinguishing Universal Truths from Country-specific Influences on Children's Sustainable Travel: A Comparison of Children in the UK and the Osaka Regional Area

The analysis of children’s activity-travel patterns has gained increasing attention in a variety of research fields, including in transportation, health, and urban planning domains. Most of the previous studies suggested that more compact development promotes less car-dependent travel among children, but many questions regarding culture, car ownership, and wealth were left unanswered. A comparison between those studies highlighted interesting behavioural differences between children’s travel patterns in English developed countries and other developed countries such as Japan and Germany. Unfortunately comparisons through existing literature on children’s travel are hindered by different trips and models being examined. The aim of this paper is to help bridge this gap of knowledge. Using the UK National Travel Survey and the Osaka Regional Area person trip datasets, this paper compares children’s travel behaviour in a region of Japan and the UK. This paper discusses both consistencies and discrepancies of travel behaviours between the countries to highlight what factors are consistent despite cultural differences, and what may vary within cultures or different built environments.

Intrapersonal variability in leisure activity-travel patterns: the case of one-worker and two-worker households

Using multi-week travel diary data from Germany, this study examines intrapersonal variability in leisure activity-travel behavior. In particular, it investigates whether individuals from one-worker households have greater intrapersonal variability in leisure activity-travel patterns than those from two-worker households. The paper examines differences in intrapersonal variability behaviors concerning trip frequency, activity duration, travel time and travel distance by comparing females from one-worker households and females from two-worker households in the context of leisure travel and activity. Similar analyses were also conducted for males. The results of analysis suggest that intrapersonal variability in leisure activity-travel patterns varies across individuals and households. The results further confirm the hypothesis that individuals from one-worker households had greater intrapersonal variability for several behavioral patterns than those from two-worker households. Social characteristics, auto ownership and location were found to statistically affect intrapersonal variability in leisure activities and travels.

Daily Commute Time Prediction Based on Genetic Algorithm

This paper presents a joint discrete‐continuous model for activity‐travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm (GA) is employed for optimizing the parameters in the hazard model. The joint model is estimated using data collected in Beijing, 2005. With the developed model, departure and travel times for the daily commute trips are predicted and the influence of sociodemographic variables on activity‐travel timing decisions is analyzed. Then the whole time allocation for the typical daily commute activities and trips is derived. The results indicate that the discrete choice model and the continuous model match well in the calculation of activity‐travel schedule. The results also show that the genetic algorithm contributes to the optimization and thus the high accuracy of the hazard model. The developed joint discrete‐continuous model can be used to predict the agenda of a simple daily activity‐travel pattern containing only work, and it provides potential for transportation demand management policy analysis.

The effect of accessibility improvement on tourist excursion behaviors

One of the most important issues in the current tourism in Japan is to identify the ways to build attractive cities and/or towns and to provide satisfying experience of tourism products. It is therefore necessary to understand the kind of needs and wants potential tourists can obtain from their touring experience. Particularly effective for this purpose is an Area Marketing and Management Approach (AMMA). This paper introduces a basic framework of the AMMA and proposes a practical procedure for quantitatively measuring the effect of accessibility improvement on tourist travel demand and activity patterns. It discusses a practical method for measuring accessibility improvement effect, focusing on the increase of the number of visitors to the tourism areas where the level of service in accessing transport facilities is planned to be improved. Also added in the paper is an empirical case study with a tourism area in Japan.