Estimating a model of dynamic activity generation based on one-day observations: Method and results

In this paper we develop and explore an approach to estimate dynamic models of activity generation on one-day travel-diary data. Dynamic models predict multi-day activity patterns of individuals taking into account dynamic needs as well as day-varying preferences and time-budgets. We formulate an ordered-logit model of dynamic activity-agenda-formation decisions and show how one-day observation probabilities can be derived from the model as a function of the model’s parameters and, with that, how parameters can be estimated using standard loglikelihood estimation. A scale parameter cannot be identified because information on within-person variability is lacking in one-day data. An application of the method to data from a national travel survey illustrates the method. A test on simulated data indicates that, given a pre-set scale, the parameters can be identified and that estimates are robust for a source of heterogeneity not captured in the model. This result indicates that dynamic activity-based models of the kind considered here can be estimated from data that are less costly to collect and that support the large sample sizes typically required for travel-demand modeling. We conclude therefore that the proposed approach opens up a way to develop large-scale dynamic activity-based models of travel demand.     

Making time count: Traveler activity engagement on urban transit

In practice, travel time is assigned a cost and treated as a disutility to be minimized. There is a growing body of research supporting the hypothesis that travel time has some value of its own, and the proliferation of information and communication technology (ICT) may be contributing to that value. Travelers’ attitudes are confounded with their mode choice, and as telecommunications mediate travel behavior, analysts must recognize the interaction between time use and customer satisfaction for appropriate travel demand management. To that end, this paper presents results from jointly estimated models of travelers’ latent satisfaction and on-board activity engagement using Chicago transit rider data gathered in April 2010. The simple questionnaire and small sample corroborate the findings of past research indicating travel attitudes and activity engagement have potential to influence travelers’ value of time, and many transit riders consider transit a better use of time and/or money than driving. The findings affirm the need for a more holistic understanding of value of time for travel demand management and infrastructure valuation. As time use has an influence on users’ valuation of the transit mode, offering opportunities to conduct certain leisure activities could improve the perceived value of travel time.     

Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour

This paper presents a comprehensive econometric modelling framework for daily activity program generation. It is for day-specific activity program generations of a week-long time span. Activity types considered are 15 generic categories of non-skeletal and flexible activities. Under the daily time budget and non-negativity of participation rate constraints, the models predict optimal sets of frequencies of the activities under consideration (given the average duration of each activity type). The daily time budget considers at-home basic needs and night sleep activities together as a composite activity. The concept of composite activity ensures the dynamics and continuity of time allocation and activity/travel behaviour by encapsulating altogether the activity types that are not of our direct interest in travel demand modelling. Workers’ total working hours (skeletal activity and not a part of the non-skeletal activity time budget) are considered as a variable in the models to accommodate the scheduling effects inside the generation model of non-skeletal activities. Incorporation of previous day’s total executed activities as variables introduces day-to-day dynamics into the activity program generation models. The possibility of zero frequency of any specific activity under consideration is ensured by the Kuhn-Tucker optimality conditions used for formulating the model structure. Models use the concept of random utility maximization approach to derive activity program set. Estimations of the empirical models are done using the 2002–2003 CHASE survey data set collected in Toronto.

An activity-episode generation model that captures interactions between household heads: development and empirical analysis

In this paper, we develop an approach for modeling the daily number of non-work, out-of-home activity episodes for household heads that incorporates in its framework both interactions between such members and activity setting (i.e. independent and joint activities). Trivariate ordered probit models are estimated for the heads of three household types – couple, non-worker; couple, one-worker; and couple, two-worker households – using data from a trip diary survey that was conducted in the Greater Toronto Area (GTA) during 1987. Significant interactions between household heads are found. Moreover, the nature of these interactions is shown to vary by household type implying that decision-making structures and, more generally, household dynamics also vary by household type. In terms of predictive ability, the models incorporating interactions are found to predict more accurately than models excluding interactions. The empirical findings emphasize the importance of incorporating interactions between household members in activity-based forecasting models.     

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.     

On altruists and egoists in activity participation and travel: who are they and do they live together?

Formulation and specification of activity analysis models require better understanding of time allocation behavior that goes beyond the more recent within household analyses to understand selfish and altruistic behavior and how this relates to travel behavior. Using data from 1,471 persons in a recent 2-day time use/activity diary and latent class cluster analysis we identify 11 distinct daily behaviors that span from the intensely self-serving to intensely altruistic. Predicted cluster membership is then used to study within household interactions. The analysis shows strong correlation exists between social role and patterns of altruistic behavior. However, a substantial amount of heterogeneity is also found within social roles. In addition, travel behavior is also very different among altruistic and self-serving time allocation groups. At the household level, a substantial number of households contain persons with similar behavior. Another group of households contains a mix of self-serving and altruistic persons that follow specialized household roles within their households. The majority of households, however, are populated by altruistic persons. Single person households are more likely to be in the self-serving groups but not in their entirety. Altruism at home is directed most often toward the immediate family members. This is less pronounced when we examine altruistic acts outside the home. Konstadinos G. Goulias is a professor of Geography at the University of California Santa Barbara, has been a professor of Civil Engineering at the Pennsylvania State University from 1991 to 2004, and he is the founder and chair of the TRB task force on moving activity-based approaches to practice. Kriste M. Henson is a technical staff member at Los Alamos National Laboratory in the Decision Applications Division and is currently pursing a Ph.D. in Geography at the University of California—Santa Barbara.     

The effect of prices and income on car travel in the UK

The objective of this paper is to analyse the factors determining household car travel, and specifically the effects of household income and the prices of cars and motor fuels, and to explore the intertemporal pattern of adjustment. The question of asymmetry in the response to rising and falling income is also addressed. Such asymmetry may be caused by habit or resistance to change or the tendency to acquire habits to consume more easily than to abandon them. The impact of prices, the speed of adjustment and the resistance to change will be important in determining the possibility of influencing travel behaviour and specifically car use. The study utilises repeated cross-section data from the annual UK Family Expenditure Surveys and employs a pseudo-panel methodology. The results are compared with those for car ownership estimated on the basis of similar models.     

Impacts of Information and Communication Technologies (ICT) on time use and travel behavior: a structural equations analysis

The objective of this paper is to contribute an empirical study to the literature on transportation impacts of Information and Communications Technologies (ICT). The structural equation model (SEM) is employed to analyze the impacts of ICT usage on time use and travel behavior. The sample is derived from the travel characteristic survey conducted in Hong Kong in 2002. The usage of ICT is defined as the experience of using e-mail, Internet service, video conferencing and videophone for either business or personal purposes. The results show that the use of ICT generates additional time use for out-of-home recreation activities and travel and increases trip-making propensity. Individuals at younger age or with higher household income are found to be more likely ICT users. The findings of this study provide further evidence on the complementarity effects of ICT on travel, suggesting that the wide application of ICT probably leads to more, not less, travel. The study also demonstrates the importance of considering the interactions between activity and travel for better understanding of the nature and magnitude of the impacts of ICT on time use and trip making behavior.     

Effect of land use on decisions of shopping tour generation: A case study of three traditional neighborhoods in WA

This study investigates the relationship between land use and shopping tour generation using an activity-based shopping model that captures the effects of land use patterns on household decisions of shopping tour frequency, tour scheduling and mode choice. The model was calibrated using travel data collected in three traditional neighborhoods located in the Puget Sound region, WA, and shopping travel patterns across seven common household structures were analyzed. The results reveal that land use patterns have virtually no impact on overall shopping tour frequency. However, land use does seem to be associated with decisions about the type of shopping tours undertaken. For example, households with poorer accessibility tend to make fewer one-stop shopping tours, and are more likely to combine shoppingtrips with other trips to form multi-stop shopping tours as a means of compensating for locational deficiencies. Finally, we also found that traditional neighborhood residents who live closer to the neighborhood commercial street, and thus, have greater accessibility, are more inclined to use non-auto modes for one-stop shopping tours.     

Environmental impacts of transformative land use and transport developments in the Greater Beijing Region: Insights from a new dynamic spatial equilibrium model

This paper reports the insights into environmental impacts of the ongoing transformative land use and transport developments in Greater Beijing, from a new suite of dynamic land use, spatial equilibrium and strategic transport models that is calibrated for medium to long term land use and transport predictions. The model tests are focused on urban passenger travel demand and associated emissions within the municipality of Beijing, accounting for Beijing’s land use and transport interactions with Tianjin, Hebei and beyond. The findings suggests that background trends of urbanization, economic growth and income rises will continue to be very powerful drivers for urban passenger travel demand across all main modes of transport beyond 2030. In order to achieve the dual policy aims for a moderately affluent and equitable nation and reducing the absolute levels of urban transport emissions by 2030, road charging and careful micro-level coordination between land use, built form and public transport provision may need to be considered together for policy implementation in the near future.     

Residential location,vehicle ownership and travel in Asia: a comparative analysis of Kei-Han-Shin and Kuala Lumpur metropolitan areas

The study examines the relationships between residential location, vehicle ownership and mobility in two metropolitan areas of Asia, Kei-Han-Shin area of Japan and Kuala Lumpur area of Malaysia. It shows that, behind apparent similarities of household auto ownership and travel time expenditure per household member, there are many causal relationships that are distinct between the areas. The similarities and differences between the two areas point to the conjecture that the evolution of a metropolitan area may be unique and path dependent, being heavily influenced by the history and culture of the locale, spatial and geographical constraints, and historical progression in infrastructure development.

Understanding engagement and disengagement from pro-environmental behaviour: The role of neutralization and affirmation techniques in maintaining persistence in and desistance from car use

Despite mounting evidence that car use is a prime culprit of global warming, our love affair with the car persists. General awareness of the environmental consequences of car usage is high but fails to correspond to moderated car use. This paper contributes to an understanding of how university students’ environmental beliefs affect decisions to engage in continued car use (persistence) and/or to discontinue or reduce car use (desistance). The aim of the research presented here was to explore the range of neutralizations and counter-neutralizations (affirmations) employed by students and to examine the ways in which they are used to justify and maintain either persistence or desistance in car use. The research consisted of six focus group sessions with thirty-four UK-based Higher Education students. Analysis of the study’s data highlights the range of neutralizations and counter-neutralizations employed by students in social settings. The article discusses the usefulness of neutralization theory in accounting for actual and/or intended non-environmentally friendly behaviour such as car use. In addition, the study’s findings are discussed in relation to prior research and to potential implications for public policy interventions which favour moderating car usage.     

The impact of high-speed rail investment on economic and environmental change in China: A dynamic CGE analysis

This study investigates the impact of high-speed rail investment on the economy and environment in China using a computable general equilibrium (CGE) model. The analysis is implemented in a dynamic recursive framework capturing long-run capital accumulation and labor market equilibrium. A national level impact was simulated through direct impact drivers including land use conversion, output expansion, cost reduction, productivity increase, transport demand substitution and induced demand. The results suggest that rail investment in China over the past decade has been a positive stimulus to the economy, while the effect on CO₂ emissions generation has been large. Overall, the economic impacts of rail investment are achieved primarily through induced demand and output expansion, whereas the contribution from a reduction of rail transportation costs and rail productivity increases were modest. In addition, negligible negative impacts were found from land use for rail development and the substitution effect among other modes. Emissions reduction from substitution of rail for other modes was small and offset by output expansion due to lowered rail transport costs and induced demand.