Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process

Traditionally, the parking choice/option is considered to be an important factor in only in the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models.     

Social networks,social influence and activity-travel behaviour: a review of models and empirical evidence

ABSTRACT

The study of social networks in activity-travel research has recently gained momentum because social activities and social influence were relatively poorly explained in activity-based models of travel demand. Over the last decade, many scholars have shown interest in identifying personal social networks that constitute an important source of explanation of activity-travel behaviour. This paper seeks to review two research streams: social networks and activity-travel behaviour, and social influence and travel decisions. We classify models, summarise empirical findings and discuss important issues that require further research.     

An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty

An understanding of the interaction between individuals’ activities and travel choice behaviour plays an important role in long-term transit service planning. In this paper, an activity-based network equilibrium model for scheduling daily activity-travel patterns (DATPs) in multi-modal transit networks under uncertainty is presented. In the proposed model, the DATP choice problem is transformed into a static traffic assignment problem by constructing a new super-network platform. With the use of the new super-network platform, individuals’ activity and travel choices such as time and space coordination, activity location, activity sequence and duration, and route/mode choices, can be simultaneously considered. In order to capture the stochastic characteristics of different activities, activity utilities are assumed in this study to be time-dependent and stochastic in relation to the activity types. A concept of DATP budget utility is proposed for modelling the uncertainty of activity utility. An efficient solution algorithm without prior enumeration of DATPs is developed for solving the DATP scheduling problem in multi-modal transit networks. Numerical examples are used to illustrate the application of the proposed model and the solution algorithm.     

Lifestyle classifications with and without activity-travel patterns

Trip-based approach and activity-based approach are two extremes in the use of activity related information when developing travel demand models. Creating lifestyle clusters for a population is a compromise between the two. On the one hand, it has taken into account travel-activity patterns in the development of the clusters. On the other hand, the clusters represent homogenous groups of individuals and simple activity-based travel demand models can be developed for each cluster. However, the development of such clusters requires knowledge of activity-travel patterns of individuals, which can only be obtained from a large-scale survey. It is still an open question how to create travel/activity-related lifestyle clusters using readily available socio-demographic data (such as census data) alone. This paper attempts to answer this question by proposing a procedure of lifestyle classification that moves from specific surveys to a general population. This paper first studies issues related to the development of homogeneous clusters using socio-economic, demographic and activity-travel data. The second part of the paper addresses the issue of data insufficiency and points out that in order to use the clusters developed for travel demand estimation, it is important to know how to allocate individuals in the population to the developed clusters. As a first attempt, this paper proposes to use a recently developed technique called, Support Vector Machine (SVM), to develop classification functions that based on readily available information only. The methodologies proposed are applied to a sub-urban area in Hong Kong. Six lifestyle clusters are first produced using factor analysis and cluster analysis. SVM is then used to develop classification functions that are based on fewer variables. Results show that the two sets of lifestyle clusters are similar and that the SVM outperforms other traditional classification methods.     

An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation

Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children’s travel and activity participation during the post-school period have direct implication for adults’ activity-travel patterns. A better understanding of children’s after school activity-travel patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. In this paper, data from the 2002 Child Development Supplement of the Panel Study of Income Dynamics is used to undertake a comprehensive assessment of the post-school out-of-home activity-location engagement patterns of children aged 5–17 years. Specifically, this research effort utilizes a multinomial logit model to analyze children’s post-school location patterns, and employs a multiple discrete–continuous extreme value model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others’ activity-travel pattern characteristics impact children’s after school activity engagement patterns.     

Day-to-day variability in travellers’ activity-travel patterns in the Jakarta metropolitan area

Using four consecutive days of SITRAMP 2004 data from the Jakarta metropolitan area (JMA), Indonesia, this study examines the interactions between individuals’ activity-travel parameters, given the variability in their daily constraints, resources, land use and road network conditions. While there have been a significant number of studies into day-to-day variability in travel behaviour in developed countries, this issue is rarely examined in developing countries. The results show that some activity-travel parameter interactions are similar to those produced by travellers from developed countries, while others differ. Household and individual characteristics are the most significant variables influencing the interactions between activity-travel parameters. Different groups of travellers exhibit different trade-off mechanisms. Further analyses of the stability of activity-travel patterns across different days are also provided. Daily commuting time and regular work and study commitments heavily shape workers’ and students’ flexibility in arranging their travel time and out-of-home time budget, leading to more stable daily activity-travel patterns than non-workers.     

Trend analysis of activity generation attributes over time

Activity generation models are relatively poorly developed in activity-based travel demand modelling frameworks. This research investigates whether observed distributions of activity attributes (activity frequency, start time and duration) used as inputs in the activity generation component of an activity-based travel demand model have changed over time. This research empirically examines changes in the distributions of activity generation attributes over time in the Greater Montreal area (GMA), Quebec, Canada. It also focuses on how these attributes vary with peoples’ socio-demographic characteristics. This research relies on the 1998, 2003 and 2008 origin–destination (O–D) household travel surveys of the GMA. The comparative analysis at three time points in a 10-year period clearly reveals that distributions of activity attributes are significantly changing over time. Work and school activities show similar trends; frequency “1” has increased and frequency “2+” has decreased over time. The occurrence of shopping activity on weekdays is decreasing over time. Start time and duration distributions for each activity have also changed significantly over time. The research allows preparing activity attributes for the application of an activity-based model, TASHA, such that they reflect temporal changes in travel behaviour of the GMA.     

An activity-based approach for scheduling multimodal transit services

This paper proposes a new activity-based transit assignment model for investigating the scheduling (or timetabling) problem of transit services in multi-modal transit networks. The proposed model can be used to generate the short-term and long-term timetables of multimodal transit lines for transit operations and service planning purposes. The interaction between transit timetables and passenger activity-travel scheduling behaviors is captured by the proposed model, as the activity and travel choices of transit passengers are considered explicitly in terms of departure time choice, activity/trip chain choices, activity duration choice, transit line and mode choices. A heuristic solution algorithm which combines the Hooke–Jeeves method and an iterative supply–demand equilibrium approach is developed to solve the proposed model. Two numerical examples are presented to illustrate the differences between the activity-based approach and the traditional trip-based method, together with comparison on the effects of optimal timetables with even and uneven headways. It is shown that the passenger travel scheduling pattern derived from the activity-based approach is significantly different from that obtained by the trip-based method, and that a demand-sensitive (with uneven headway) timetable is more efficient than an even-headway timetable.     

A structural equation model based analysis of non-workers’ activity-travel behaviour from a city of a developing country

This paper presents an empirical analysis of non-workers’ activity-travel behaviour from Bangalore city, India. The paper builds a causal model—to describe the relationships among socio-demographics, activity-participation, and travel behaviour of non-workers—following structural equation modelling methodology. The results indicate that in-home maintenance activity-duration drives the time allocation decisions of non-workers. The model also shows the presence of ‘time-budget’ effects i.e., excess travel time cuts into in-hhome discretionary activity duration, implying the trade-off between daily travel time and in-home discretionary activity duration. The out-of-home activity durations of non-workers are found to be insensitive to travel time—an important finding of this research. The model also suggests that mixed residential development reduce travel distance and indirectly contribute to more trips. An indirect effect of mixed residential development on daily travel distance offsets the direct effect, which leads to a limited total effect of this variable on travel distance. The basic model was expanded further by separating the time spent on others’ activity (children and elders) from in-home maintenance activity duration. The stable model reveals that the time spent on others’ activity also influences in-home and out-of-home activities, and travel behaviour. This indicates that the time spent on others’ activity is an important time allocation of its own.     

An activity-based approach of investigating travel behaviour of older people

The paper presents the results of an investigation on daily activity-travel scheduling behaviour of older people by using an advanced econometric model and a household travel survey, collected in the National Capital Region (NCR) of Canada in 2011. The activity-travel scheduling model considers a dynamic time–space constrained scheduling process. The key contribution of the paper is to reveal daily activity-travel scheduling behaviour through a comprehensive econometric framework. The resulting empirical model reveals many behavioural details. These include the role that income plays in moderating out-of-home time expenditure choices of older people. Older people in the highest and lowest income categories tend to have lower variations in time expenditure choices than those in middle-income categories. Overall, the time expenditure choices become more stable with increasing age, indicating that longer activity durations and lower activity frequency become more prevalent with increasing age. Daily activity type and location choices reveal a clear random utility-maximizing rational behaviour of older people. It is clear that increasing spatial accessibility to various activity locations is a crucial factor in defining daily out-of-home activity participation of older people. It is also clear that the diversity of out-of-home activity type choices reduces with increasing age and older people are more sensitive to auto travel time than to transit or non-motorized travel time.     

Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)

Studies of urban travel behaviour typically focus on weekday activities and commuting. This is surprising given the rising contribution of discretionary activities to daily travel that has occurred during the last few decades. Moreover, current understanding of the relationship between travel behaviour and land use remains incomplete, with little research carried out to explore spatial properties of activity-travel behaviour during the off-peak and weekend time periods. Weekend behaviours, for example, influenced by the availability of time and the spatiotemporal distribution of “weekend” destinations, likely produce spatially and temporally distinct activity-travel patterns. Using data from the first wave of the Toronto Travel-Activity Panel Survey (TTAPS), this paper examines an area of research that has received little attention; namely, the presence of spatial variety in activity-travel behaviour. The paper begins by looking at the extent to which individuals engage in spatially repetitive location choices during the course of a single week. Area-based measures of geographical extent and activity dispersion are then used to expose differences in weekday-to-weekend and day-to-day activity-travel patterns. Examination of unclassified activities carried out over a 1 week period reveals a level of spatial repetition that does not materialise across activities classified by type, travel mode, and planning strategy. Despite the inherent spatial flexibility offered by the personal automobile, spatial repetition is also found to be surprisingly similar across travel modes. The results also indicate weekday-to-weekend, and day-to-day fluctuations in spatial properties of individual activity-travel behaviour. These findings challenge the utility of the short-run survey as an instrument for capturing archetypal patterns of spatial behaviour. In addition, the presence of a weekday-to-weekend differential in spatial behaviour suggests that policies targeting weekday travel reduction could have little impact on travel associated with weekend activities.

A retrospective and prospective survey of time-use research

The central basis of the activity-based approach to travel demand modeling is that individuals' activity-travel patterns are a result of their time-use decisions within a continuous time domain. This paper reviews earlier theoretical and empirical research in the time-use area, emphasizing the need to examine activities in the context or setting in which they occur. The review indicates the substantial progress made in the past five years and identifies some possible reasons for this sudden spurt and rejuvenation in the field. The paper concludes that the field of time-use and its relevance to activity-travel modeling has gone substantially past the "tip of the iceberg", though it certainly still has a good part of the "iceberg" to uncover. Important future areas of research are identified and discussed.     

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

Given 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.     

Incorporating space–time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling

Activity-travel scheduling is at the core of many activity-based models that predict short-term effects of travel information systems and travel demand management. Multi-state supernetworks have been advanced to represent in an integral fashion the multi-dimensional nature of activity-travel scheduling processes. To date, however, the treatment of time in the supernetworks has been rather limited. This paper attempts to (i) dramatically improve the temporal dimension in multi-state supernetworks by embedding space–time constraints into location selection models, not only operating between consecutive pairs of locations, but also at the overall schedule at large, and (ii) systematically incorporate time in the disutility profiles of activity participation and parking. These two improvements make the multi-state supernetworks fully time-dependent, allowing modeling choice of mode, route, parking and activity locations in a unified and time-dependent manner and more accurately capturing interdependences of the activity-travel trip chaining. To account for this generalized representation, refined behavioral assumptions and dominance relationships are proposed based on an earlier proposed bicriteria label-correcting algorithm to find the optimal activity-travel pattern. Examples are shown to demonstrate the feasibility of this new approach and its potential applicability to large scale agent-based simulation systems.     

Synthesis of first practices and operational research approaches in activity-based travel demand modeling

Regional travel models in the United States are clearly evolving from conventional models towards a new generation of more behaviorally realistic activity-based models. The new generation of regional travel demand models is characterized by three features: (1) an activity-based platform, that implies that modeled travel be derived within a general framework of the daily activities undertaken by households and persons, (2) a tour-based structure of travel where the tour is used as the basic unit of modeling travel instead of the elemental trip, and (3) micro-simulation modeling techniques that are applied at the fully-disaggregate level of persons and households, which convert activity and travel related choices from fractional-probability model outcomes into a series of discrete or “crisp” decisions.While the new generation of model has obvious conceptual advantages over the conventional four-step models, there are still numerous technical issues that have to be addressed as well as a better understanding of practical benefits should be achieved before the new generation of models can fully replace conventional models. The paper summarizes the recent successful experience in the development and application of activity-based demand models for Metropolitan Planning Organizations in the US. Moving activity-based approaches into practice is analyzed in a broad context of travel demand modeling market tendencies and policy implications.     

Dynamic activity-travel assignment in multi-state supernetworks

The integration of activity-based modeling and dynamic traffic assignment for travel demand analysis has recently attracted ever-increasing attention. However, related studies have limitations either on the integration structure or the number of choice facets being captured. This paper proposes a formulation of dynamic activity-travel assignment (DATA) in the framework of multi-state supernetworks, in which any path through a personalized supernetwork represents a particular activity-travel pattern (ATP) at a high level of spatial and temporal detail. DATA is formulated as a discrete-time dynamic user equilibrium (DUE) problem, which is reformulated as an equivalent variational inequality (VI) problem. A generalized dynamic link disutility function is established with the accommodation of different characteristics of the links in the supernetworks. Flow constraints and non-uniqueness of equilibria are also investigated. In the proposed formulation, the choices of departure time, route, mode, activity sequence, activity and parking location are all unified into one time-dependent ATP choice. As a result, the interdependences among all these choice facets can be readily captured. A solution algorithm based on the route-swapping mechanism is adopted to find the user equilibrium. A numerical example with simulated scenarios is provided to demonstrate the advantages of the proposed approach.     

Micro-simulation of daily activity-travel patterns for travel demand forecasting

The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals' daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns. This revised version was published online in June 2006 with corrections to the Cover Date.     

Effects of task complexity and time pressure on activity-travel choices: heteroscedastic logit model and activity-travel simulator experiment

This paper derives, estimates and applies a discrete choice model of activity-travel behaviour that accommodates potential effects of task complexity and time pressure on decision-making. To the best of our knowledge, this is the first time that both factors (task complexity and time pressure) are jointly captured in a discrete choice model. More specifically, our heteroscedastic logit model captures potential impacts of task complexity and time pressure through the scale of the utility of activity-travel options. We collect data using a novel activity-travel simulator experiment that has been specifically designed with the aim of testing our model. Results are in line with expectations, in that higher levels of task complexity and time pressure are found to result in a smaller scale of utility. In other words, higher levels of task complexity and time pressure lead to more random choice behaviour and as a consequence to less pronounced differences in choice probabilities between alternatives. An empirical illustration suggests that choice probability-differences between models that do and those that do not capture these effects, can be very substantial; this in turn suggests that failing to capture the effects of task complexity and time pressure in discrete choice models of activity travel decision-making might lead to serious bias in forecasts of the effects of transport policies.