Activity-travel Patterns Research Articles

The influence of activity-travel patterns on the success of VTBC

ABSTRACTThe objective of this work is to verify how the complexity of activity-travel patterns may influence the propensity to change travel behaviour in the context of a Voluntary Travel Behaviour Change (VTBC) programme. Data used in this work was drawn from a VTBC programme implemented in Cagliari, Italy between 2011 and 2012, for promoting the use of an underutilised Light Rail service (LR). A descriptive comparative analysis of activity–travel patterns recorded before and after the delivery of a personalised travel plan was reported. In addition to the descriptive analysis, a panel Probit model is proposed to further understand the influence of complex trip-chaining behaviours on the propensity to change travel behaviours. The results indicate that when individuals are presented with a convenient transport alternative that allows them to flexibly chain their activities, the propensity to use a sustainable mode of transport increases.Abbreviations: ABA: activity – based analysis; AW: after work tour; BW: before work tour; CMS: casteddu mobility styles; CW: complex working day; HWC: home to work commute tour; LR: light rail; NHB: non home based tour; NNW: non work tour; NW: non working day; P&R: park and rider; PP&R: prospective park and rider; PTP: personalised travel plan; SW: simple working day; VTBC: voluntary travel behaviour change; WB: work based tour

Weather variability and travel behaviour – what we know and what we do not know

ABSTRACTGiven that severe weather conditions are becoming more frequent, it is important to understand the influence of weather on an individual’s daily activity-travel pattern. While some previously rare events are becoming more common, such as heavy rain, unpredicted snow, higher temperatures, it is still largely unknown how individuals will change and adapt their travel patterns in future climate conditions. Because of this concern, the number of research studies on weather and travel behaviour has increased in recent decades. Most of these empirical studies, however, have not used a cost–benefit analysis (CBA) framework, which serves as the the main tool for policy evaluation and project selection by stakeholders. This study summarises the existing findings regarding relationships between weather variability and travel behaviour, and critically assesses the methodological issues in these studies. Several further research directions are suggested to bridge the gap between empirical evidence and current practices in CBA.

Dynamics in social activity-travel patterns: Analyzing the role of life-cycle events and path dependence in face-to-face and ICT-mediated social interactions

Social activity-travel accounts for an increasing share of total travel distance and is important for people's quality of life. Although our understanding of the role of transport on social interactions has recently increased, the dynamics of social activities still need further evidence. The objective of this paper is therefore to study the dynamics in social interaction frequency. Analyses are based on a sample of 139 respondents who were surveyed in 2008 and 2014 in Eindhoven, the Netherlands. The data collection instrument included a questionnaire and a two-day social interaction diary to collect data on face-to-face social interactions and social interactions mediated by communication technology. A path analysis model is estimated to predict the number of face-to-face and ICT-mediated social interactions in 2014 as a function of personal socio-demographic characteristics, mobility behavior and life cycle events experienced between 2008 and 2014 and the number of face-to-face and ICT-mediated social interactions in 2008. The results show path dependence effects for both face-to-face and ICT-mediated social interaction frequencies, suggesting that history can partly explain current behavior. Socio-demographics, mobility characteristics and life-cycle events also play a role in face-to-face social interaction frequency, whereas no effects of life-cycle events were found for ICT-mediated social interaction frequency.

Daily activity-travel scheduling behaviour of non-workers in the National Capital Region (NCR) of Canada

This paper uses household travel survey data (of the National Capital Region of Canada) and a comprehensive random utility maximizing travel options modelling approach to investigate non-workers’ activity-travel scheduling behaviour. The empirical model reveals that the presence of children shapes the daily activity-travel patterns of non-workers by reducing the flexibility of out-of-home activity-type choices. Availability of private cars increases flexibility in travelling and increases the spread of spatial locations of out-of-home activities of non-workers. Income plays a significant role in non-workers’ activity-travel behaviour and it seems that non-workers from lower to middle-income households are less active (return home early) than those living in higher income households. In general, it is found that male non-workers are less active than the female non-workers and it is also evident that non-workers living in single detached houses are less active (return home early) than those living in condos/apartments. These findings have an implication to health issues as the average age of non-workers is over 50years and the majority of detached houses are far from the central business district.

Analysis of the Impact of Technology Use on Multimodality and Activity Travel Characteristics

Smartphone ownership and use are becoming increasingly prevalent around the world. However, little is known about the impacts of this technology on activity travel choices. The objective of this study was to determine the extent to which smartphone ownership and use influence activity travel demand, after controlling for other lifestyle and demographic attributes. The impacts of smartphone ownership and use on multiple travel dimensions were examined through the specification and estimation of a joint model system that explicitly accounts for self-selection effects arising from lifestyle preferences (such as green lifestyle propensity and technology savviness). Specifically, the impacts of smartphone ownership and use on the following choice dimensions were estimated: ( a) use of multiple modes of transportation, ( b) pursuit of complex trip chains with a large number of intermediate stops, ( c) engagement in tours that have a recreational activity, and ( d) participation in joint tours that involve an accompanying person. Travel survey data from the 2014–2015 Puget Sound (Washington) Regional Travel Study were used. The results show substantial and statistically significant effects of smartphone ownership and use on activity travel patterns, even after controlling for lifestyle preferences. Smartphone ownership and use were found to increase the likelihood of using multiple modes of transportation and participating in complex tours, joint tours, and recreational tours.

Activity Duration Analysis: Discrete-Time Approach for Recurrent Events, Competing Risks, and Multiple States

Most existing activity-based travel demand models are typically implemented on a tour-based microsimulation framework. Two approaches are available to describe people’s daily decision-making processes on activity and travel in the activity-based model framework: rubber banding and growing scheduling. The growing-scheduling approach requires a series of linked dynamic discrete choices of activity episodes, locations, and travel modes to build incrementally an entire day’s activity-travel patterns for individuals in households. To incorporate explicitly time dependencies into activity episode choice and duration, a hazard-based duration model in a discrete-time framework is introduced; this framework is essentially the same as a discrete choice model with temporal dummies as the simplest form of dynamic discrete choice models. In the application of the discrete–time duration model to activity duration analysis, complex situations of activity-travel data are considered; these include multiple states of origin activity, competing risks of destination activity, and a multilevel structure for recurrent activity episodes within individuals. In addition, a circular, or periodic, variable is introduced as a combination of sine and cosine to model time-of-day effects.

Research on Modeling Intrahousehold Interactions from the Perspective of Space-Time Constraints

Interactions among family members can yield valuable information for interpreting individual travel decisions. Typically, each family member plays a set role and travel decisions are made by considering the combined needs of household members. This study investigates both multiactivity and multiperson interactions in urban nuclear families and proposes the novel concepts of “activity-restriction degree” and “activity-constraint niche” to quantify the degree of space-time constraints within time geography. A structural equation model is employed to analyze intrahousehold interactions based on individual activity-travel patterns during the workday. The results indicate that the links between family members reflect behavioral responses (with constraints) between individuals and other family members. Household interaction constraints not only influence individual travel decisions but also affect the realization of the household activity for everyone. These interactions lead to reasonable adjustments and mutual support and to the identification of efficient activity patterns that meet the demands of the entire household.

Variability in daily activity-travel patterns: the case of a one-week travel diary

Understanding temporal rhythms in travel and activity patterns has been recognized as an important issue for the effective management of urban congestion. Research issues related to this topic concern the degree to which travel behaviour varies from one day to another, the differences between weekday and weekend travel, and the determinants of variability. Thanks to a seven-day travel diary collected for 707 individuals in the city of Ghent (Belgium) in 2008, this study goes further by studying this variability according to various time periods within the week and by analysing interpersonal and intrapersonal variations according to the varying attributes of activity-travel patterns. Different variance indicators and the sequential alignment method are applied for the measurement of variability of travel-activity behaviour. Moreover, the influence of individual characteristics on these variations is examined. The overall picture of a large intrinsic variability in travel behaviour (i.e. trip or home-based tour generation) is confirmed. There is more difference in the number of trips per day for a given individual depending on the various days of week than there is between individuals per se, not including the weekend period, and this aspect is reinforced when considering home-based tours. Unlike the case of trip generation, there is greater difference between persons in their daily time allocation to various activities than between days for a given person in general, either during working days or during the weekend. This is also the case for daily activity sequence. Finally, the influence of socio-demographic characteristics on intrapersonal variability is weak, whether for daily trips, tours, time use or activity sequence. The large level of intrapersonal variability in daily trip numbers already demonstrated in the literature is confirmed. Systematic day-to-day variability is shown to have an extremely low share in intrapersonal variability. The global picture is that intrapersonal variability is large while systematic day-to-day variability is marginal. Moreover, a striking result is that socio-demographic characteristics are mostly unable to explain the level of intrapersonal variability. The results reveal that individual behaviour is neither completely habitual nor completely random. On the one hand, intrapersonal variability is more important than the interpersonal one as regards daily trip numbers for the realization of mobility needs. On the other hand, activity time allocation and sequencing show an inverse trend, which can be linked with the habitual part of behaviour and the social role of the individual (through e.g. work, childcare and other activities).

Estimating changes in transport CO2 emissions due to changes in weather and climate in Sweden

There is a considerable body of studies on the relationship between daily transport activities and CO2 emissions. However, how these emissions vary in different weather conditions within and between the seasons of the year is largely unknown. Because individual activity–travel patterns are not static but vary in different weather conditions, it is immensely important to understand how CO2 emissions vary due to the change of weather. Using Swedish National Travel Survey data, with emission factors calculated through the European emission factor model ARTEMIS, this study is a first attempt to derive the amount of CO2 emission changes subject to the change of weather conditions. A series of econometric models was used to model travel behaviour variables that are crucial for influencing individual CO2 emissions. The marginal effects of weather variables on travel behaviour variables were derived. The results show an increase of individual CO2 emissions in a warmer climate and in more extreme temperature conditions, whereas increasing precipitation amounts and snow depths show limited effects on individual CO2 emissions. It is worth noting that the change in CO2 emissions in the scenario of a warmer climate and a more extreme temperature tends to be greater than the sum of changes in CO2 emissions in each individual scenario. Given that a warmer climate and more extreme weather could co-occur more frequently in the future, this result suggests even greater individual CO2 emissions than expected in such a future climate.

Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.

Modelling joint activity-travel pattern scheduling problem in multi-modal transit networks

Over the past decades, many activity-based travel behaviour models have been proposed based on individuals’ independent decision making. The modelling of individuals’ joint activity/travel choices, however, has received less attention. In reality, both independent and joint activities/travels form individual’s normal daily activity-travel patterns. Travel surveys have indicated that joint activity/travel constitutes an important part in individuals’ daily activity-travel patterns. On this basis, explicit modelling of joint activity/travel choices is an essential component for long-term transport planning. In this study, an activity-based network equilibrium model is proposed for scheduling two-individual joint activity-travel patterns (JATPs) in congested multi-modal transit networks. The proposed model can be used to comprehensively investigate individuals’ activity choices (e.g. activity start time and duration, activity sequence) and travel choices (e.g. departure time, route and mode) in multi-modal transit networks, including both independent ones and joint ones. The time-dependent JATP choice problem is converted into an equivalent static user equilibrium model by constructing a joint-activity-time-space (JATS) super-network platform. Joint travel benefit is modelled by incorporating a commonality factor in the JATP utility. A solution algorithm without prior JATP enumeration is proposed to solve the JATP scheduling problem on the JATS super-network. Numerical results show that individuals’ independent and joint activity/travel choices can be simultaneously investigated by the proposed model. The impacts of joint travel benefit on individuals’ independent and joint activity-travel choices are explicitly investigated.

A user equilibrium model for combined activity–travel choice under prospect theoretical mechanisms of decision-making under uncertainty

ABSTRACTRather than considering single trips as the unit of analysis, the activity-based modeling paradigm of travel demand analysis has led to reconceptualizations and innovations in traffic flow models by focusing on complete daily activity–travel patterns. The vast majority of these travel demand and traffic flow models have either implicitly or explicitly assumed that travelers choose between alternatives by maximizing their utility under a deterministic representation of the choice alternatives. While this behavioral assumption leads to tractable, easy-to-apply models, the validity of the assumption largely went untested. This paper investigates the user equilibrium of activity–travel patterns under uncertainty from the perspective of prospect theory. A formulation of the static activity-based user equilibrium model is proposed. In particular, we adopt the concept of a multi-state supernetwork to represent the choice space of activity–travel patterns. A numerical example using hypothetical scenarios is presented to illustrate the proposed model and solution algorithm.

Dynamic activity-travel assignment in multi-state supernetworks under transport and location capacity constraints

ABSTRACTA unified framework coupling activity-based modelling and dynamic traffic assignment has recently been proposed. It formulates dynamic activity-travel assignment (DATA) in multi-state supernetworks as a dynamic user equilibrium. Choices of departure time, route, mode, activity sequence, and activity/parking location are determined endogenously, reflected in time-dependent activity-travel patterns (ATPs). However, capacity constraints associated with activities, parking, and public transit vehicles have not taken into account. This paper extends this approach by formulating these constraints and incorporating them into the DATA framework. Three numerical examples are presented to illustrate the effectiveness of the approach. It is shown that capacity constraints have significant effects on the choice facets.

Relationships among discretionary activity duration, its travel time spent and activity space indices in the Jakarta Metropolitan Area, Indonesia

This study examines the interdependencies among an individual's time allocation for different activities and the travel time spent on a given day, socio-demographic and built environment variables on these in-home and out-of-home discretionary activities time duration, and how interaction of those variables on discretionary activities time duration influences an individual's activity space indices in the Jakarta Metropolitan Area (JMA), Indonesia. The 3SLS model and the 2004 SITRAMP household travel survey were used to achieve this objective. The results show that the time allocation for certain discretionary activities significantly influences the time allocation for other discretionary activities. Workers, students and non-workers have different complex mechanisms pertaining to how they allocate time across different activities and journeys. This unique trade-off mechanism gives an individual a unique distribution of activity locations and spatial movement patterns. This is observed via his/her activity space indices throughout time and space. For example, the estimation result shows that workers' and students' time-use allocation, activities participation and activity space indices are highly influenced by their engagement in mandatory activities. However, this is not the case for non-workers. Furthermore, the mandatory travel time variable has a stronger impact on an individual's discretionary activities time-use pattern than the duration of mandatory activities. This may lead to the argument that, in order to provide more opportunities and flexibilities among the JMA's workers and students for undertaking discretionary activities, travel time reduction policies can play more significant role in shaping the discretionary activity-travel patterns than reduction in working/school hour policies. Additionally, in-line with previous findings in developed countries, locating grocery shops closer to residential areas in the CBD and in suburban areas creates more opportunities for workers and students to spend more time on out-of-home maintenance activities; with a shorter travel time, especially on Fridays.

Modeling duration choice in space–time multi-state supernetworks for individual activity-travel scheduling

Multi-state supernetworks have been advanced recently for modeling individual activity-travel scheduling decisions. The main advantage is that multi-dimensional choice facets are modeled simultaneously within an integral framework, supporting systematic assessments of a large spectrum of policies and emerging modalities. However, duration choice of activities and home-stay has not been incorporated in this formalism yet. This study models duration choice in the state-of-the-art multi-state supernetworks. An activity link with flexible duration is transformed into a time-expanded bipartite network; a home location is transformed into multiple time-expanded locations. Along with these extensions, multi-state supernetworks can also be coherently expanded in space–time. The derived properties are that any path through a space–time supernetwork still represents a consistent activity-travel pattern, duration choice are explicitly associated with activity timing, duration and chain, and home-based tours are generated endogenously. A forward recursive formulation is proposed to find the optimal patterns with the optimal worst-case run-time complexity. Consequently, the trade-off between travel and time allocation to activities and home-stay can be systematically captured.

Accessibility measures for robustness of the transport system

Accessibility is a key concept in transport and land use policies, and infrastructure-based measures are one important category of accessibility measures. Recently, there has been a significant increase in the attention paid in both academic literature and policy documents to the robustness of the transport system. However, there is not a mature body of literature on the infrastructure-based accessibility measures expressing this concept. This paper proposes a family of accessibility measures to express the robustness of the transport system. These have in common that they express the number of travel options between a given origin and destination, or to conduct activities. The family of measures is conceptualized by using the multi-state supernetwork approach. With respect to the travel options, we discuss (1) the overlap of travel routes, and (2) the choices of travel modes (e.g. car versus train; multimodal). With respect to the activities, we discuss (1) the choice of OD pairs as opposed to activity-travel patterns, and (2) fixed versus flexible activity locations and fixed versus flexible order of activities. We illustrate the travel options and robustness in a multi-modal transport system between the city centers of the Hague-Rotterdam-Dordrecht corridor (The Netherlands).

Investigating the interactions between travellers' familiar areas and their multi-day activity locations

A mental map is considered a representation of an individual's spatial cognition. It defines an individual's choice set of plausible activity locations and influences his/her daily activity-travel patterns. Despite its importance, how individuals' activity travel patterns interact with their mental maps on a daily basis is largely unknown, mainly due to data, operation, and measurement issues. The aim of this study is to address this. A total of 57 individuals in Stockholm were asked to record a two-week travel diary and draw their familiar areas in the specified maps. The familiar areas, which in this study are considered as representative of individual mental maps, were manually drawn and transferred from graph to ASCII code in ArcGIS for modelling purposes. The recently visited activity locations were used to construct the individuals' activity spaces. The crucial determinants that related to these activity spaces and familiar areas were investigated. The marginal effect of each key variable was calculated in order to understand the magnitude of influence of each variable to the individuals' activity spaces and familiar areas. The results show that an individual's activity space is partially or completely located within the individual's familiar areas and they are strongly correlated to each other. Large activity centres, such as Stockholm's central areas and areas of Huvudsta have higher probabilities to be included in both individuals' familiar areas and activity spaces than other areas that are closer to home.

Rural location-based activity generation: a case study of Iran villages

Efficacious transportation, as prerequisites of sustainable development of rural areas, should receive pertinent attention. This needs more attention in developing countries as nearly half of population in these countries reside in rural areas. However, still little is known about the activity and travel characteristics of rural residents. This study focuses on activity-location (Ac-Lo) patterns of rural residents in Iran. An interview of place-based questionnaire was designed and data about activity-travel characteristics of rural residents was collected. In total, 663 respondents were used for analyzing out-of-home Ac-Lo patterns. Based on a descriptive analysis of Ac-Lo characteristics of the sample, 10 Ac-Lo patterns were determined, differentiated according to the primary and secondary activity type and the primary activity location of rural residents. To explore the relations between Ac-Lo patterns and socio-demographic characteristics of rural residents and the type of village, logit models were used. Based on specification test, two nested logit structures were identified modeling the joint decision of choosing activity and location. The first structure was nested based on activity location, inside or outside the village, while the second was nested based on primary activity type, work, education and other activities. The selected structure emphasized the role of activity location in activity-travel patterns in rural areas. The results also demonstrated the important role of socio-demographic variables and job type of rural residents on joint Ac-Lo decisions. Village type of either near to city or far from it was the only variable appeared in the higher level of the nested structure, and had a significant influence on individuals’ membership of the inside the village Ac-Lo patterns.

Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

ABSTRACTGlobal Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.

Examination of the Impacts of Telecommuting on the Time Use of Nonmandatory Activities

How and to what extent telecommuting engagement affects time allocation among nonmandatory activities are examined to help understand the impacts of telecommuting on daily activity–travel patterns. Five categories of nonmandatory activities are considered: shopping, maintenance, discretionary, escort, and in-home shopping. The hypothesis is that telecommuting relaxes the temporal and spatial constraints related to work activities at the regular workplace, and telecommuters may allocate some of the time budget to other nonmandatory activities, which may or may not lead to additional travel. The structural equations model approach is applied to capture the impacts of telecommuting as well as the interactions among the nonmandatory activities. The activity durations by type along with the number of total daily trips are considered as endogenous (dependent) variables. By incorporating work hours at the regular workplace and daily telecommuting hours as exogenous variables, the models can reveal how people may reallocate their time among different nonmandatory activities given different levels of telecommuting engagement (either part-day or full-day). All types of telecommuting arrangements increased nonmandatory activity durations (compared with those of nontelecommuters). Full-day telecommuters have higher durations of discretionary activities, while part-day telecommuters have higher durations of maintenance and out-of-home shopping errands. Telecommuting also increased total daily trip rates for both telecommuters and their household members. This study used data obtained from the 2010–2011 Regional Household Travel Survey in the New York metropolitan region.