Italo Meloni

A Model of the Dynamic Process of Time Allocation to Discretionary Activities

This paper proposes an activity-based methodology for representing the allocation of time to discretionary activities during their programming and scheduling, based on the premise that both phases are to be considered contextually and two aspects of the same decision process. The aim of this work is to extend the treatment of utility maximization associated with carrying out two activities to J activities, so as to be able to segregate the time spent traveling from the total amount of time dedicated to out-of-home activities. The global structure of the model takes the form of a nested Tobit, particularly suited for reproducing a sequence of coupled choices. The first choice concerns dividing up overall discretionary time between activities inside and outside the home, then the second choice, subordinate to the first, involves rebudgeting the time between in- and out-of-home activities and trips. Thus the proposed model enables us to analyze the effects that each explicative variable exerts on trips segregated from activities outside the home and, last, during demand forecasting, the direct consequences of allocating discretionary time to trips following changes to an individuals time budget. A database created from a large-scale time-use survey (ISTAT 1988--1991) has been used for calibrating the model coefficients.

Behavioral Analysis of Choice of Daily Route with Data from Global Positioning System

This work develops a methodology for converting data from the Global Positioning System (GPS) into observed routes (routes actually taken) to characterize intraindividual and interindividual variability in route choice and to compare observed and minimum-cost routes. Exploration of observed route choice behavior is crucial because the underlying decision-making process is more complex and dynamic for route choice than for other travel choice dimensions. Furthermore, the difficulties associated with collection of data on route choice are reflected in the scarcity of studies on observed behavior and the major simplifications made in traffic assignment models developed for the most common commercial software. The present study analyzes a GPS-based database of 679 routes, collected by a personal probe system called the activity locator over a period of 2 weeks for a sample of 12 students from the University of Cagliari in Italy. In particular, variability in the daily route (for the same individual and for several individuals) and observed deviations of the route from minimum-cost routes have been examined in depth. The results indicate that higher levels of intraindividual variability are found for discretionary trips, whereas higher levels of interindividual variability, as well as greater deviation from minimum-cost routes, are associated with work or study trips.

Activity-Based Model of Women's Activity-Travel Patterns

The daily schedule of women is dictated by various requirements, and consequently their activity–travel patterns are often more complex and heterogeneous than those of their male counterparts. The problems associated with womens mobility have highlighted the need to treat males and females separately when travel behavior is represented, especially when transportation policy interventions are concerned. Because of the substantial differences between mens and womens activity and travel behavior, they may respond differently to policy changes. This paper describes an activity-based approach to womens travel behavior analysis, where time is allocated to in-home and out-of-home nonwork activities. A mixed joint probit–Tobit model was developed for daily time allocation to a set of nonwork activities; it allows for two-stage choice: first a woman decides whether to engage in activities outside the home (discrete choice), then she chooses which nonwork activities to participate in and allocates the time for them (discrete–continuous choice). Data used for model estimation were from the Mobility and Equal Opportunities Survey conducted in Cagliari, Italy, in 2006 by Centro Interuniversitario Ricerche Economiche e Mobilità in collaboration with Cagliari Provincial Authoritys Department of Gender Equality. The survey results highlight the dual roles of women who have to divide their time between work and family and indicate how transportation system efficiency plays a pivotal role in womens schedules and imposes constraints on their leisure time more than does any other individual or household characteristic.

Effects of Physical Activity on Propensity for Sustainable Trips

Recent statistics about the low level of participation by individuals in physical activities as well as a generalized propensity to use private vehicles have broadened the scope of transport studies to the sphere of health and well-being. The current shift in travel demand modeling to the activity-based paradigm is central to investigating which population segment is more likely to opt for environmentally friendly and energy-efficient vehicles, alternative modes of transport, and a rational use of the motor car. A mixed, joint Tobit–probit model analyzes the effects of time allocation for daily activity on the propensity to sustainable trips. The model is applied to a sample of workers and students age 14 and older, drawn from a time-use survey conducted in Turin, Italy, and its metropolitan area. The analysis suggests the presence of self-selection effects between active lifestyles and sustainable mandatory trips. The model predictions highlight the substantial contribution of transportation interventions in getting individuals to engage in healthier behaviors.

Modeling the Propensity to Use a Sustainable Mode in the Context of a Program of Voluntary Change in Travel Behavior

This paper proposes a modeling approach for evaluating the effect of a personalized travel plan on a sustainable mode choice. A panel binary probit was estimated by using the approach of composite marginal-likelihood estimation. The formulation modeled the choice of using a light rail service (versus that of not using it) by means of daily individual panel observations collected in the context of a program of voluntary travel behavior change (VTBC) before and after the provision of a personalized travel plan. In this regard, a VTBC program was a policy measure that used communication and information to encourage individuals to use more sustainable travel modes. In this study, the VTBC program was implemented by providing car users with personalized information about how to introduce the light rail service into their travel patterns.

Random Utility-Based Microeconomic Model for Discretionary Activity Time Allocation

This paper proposes a microeconomic model for activity time allocation analysis that recognizes the random nature of daily time allocation, while also focusing on activities as a direct source of individual utility. More specifically, different levels of satisfaction (or utility) are gained from activity participation depending on the amount of time allocated to and the type of goods utilized in daily activities. The proposed model represents individual utility as a function of activity participation, accounts for the budget constraint in activity time allocation, allows for the interindi-vidual and interactivity variation of utility, and considers the entire sphere of discretionary activities (in-home as well as out-of-home activities and trips). The aim of this study is to show how including a budget constraint in random utility models avoids overestimating the times allocated to activities and trips estimated by earlier random utility-based models that consider only time constraints. In particular, estimates of in-home and out-of-home discretionary activity and trip times for a sample of Italian single individuals from the 2003 National Institute of Statistics (ISTAT) Time Use Survey are presented. Compared with the estimates obtained using the simply time-constrained model with no budget constraint, the empirical findings show the variable effects to be interpreted in the same way. A greater number of significant variables in the double-constrained model denote greater complexity of the phenomenon to be represented. Incorporating the budget constraint diminishes the effect of some variables on activity time allocation.

Cross-Clustered Model of Frequency of Home-Based Work Participation in Traditional Off-Work Hours

A study was done to shed light on the determinants of working from home beyond the traditional office-based work hours. The frequency of work participation from home was examined for individuals who also have a traditional work pattern of traveling to an out-of-home workplace and a fixed number of work hours at the out-of-home workplace. The sample for the empirical analysis was drawn from the 2002 to 2003 Turin, Italy, survey of time use, which was designed and administered by the Italian National Institute of Statistics. The methodology recognizes both spatial and social clustering effects by using a cross-clustered ordered response structure to analyze the frequency of work participation from home during off-work periods. The model is estimated through the use of the inference technique of composite marginal likelihood, which represents a conceptually, pedagogically, and implementationally simpler procedure relative to traditional frequentist and Bayesian simulation techniques.

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

ABSTRACT The 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

Estimating Route Choice Models from Stochastically Generated Choice Sets on Large-Scale Networks

Route choice is one of the most complex decision-making contexts to represent mathematically, and the most frequently used approach to model route choice consists of generating alternative routes and modeling the preferences of utility-maximizing travelers. The main drawback of this approach is the dependency of the parameter estimates from the choice set generation technique. Bias introduced in model estimation has been corrected only for the random walk algorithm, which has problematic applicability to large-scale networks. This study proposes a correction term for the sampling probability of routes extracted with stochastic route generation. The term is easily applicable to large-scale networks and various environments, given its dependence only on a random number generator and the Dijkstra shortest path algorithm. The implementation for revealed preferences data, which consist of actual route choices collected in Cagliari, Italy, shows the feasibility of generating routes stochastically in a high-resolution network and calculating the correction factor. The model estimation with and without correction illustrates how the correction not only improves the goodness of fit but also turns illogical signs for parameter estimates to logical signs.

Estimating Route Choice Models from Stochastically Generated Choice Sets on Large-Scale Networks: Correcting for Unequal Sampling Probability

Route choice is one of the most complex decision-making contexts to represent mathematically, and the most frequently used approach to model route choice consists of generating alternative routes and modeling the preferences of utility-maximizing travelers. The main drawback of this approach is the dependency of the parameter estimates from the choice set generation technique. Bias introduced in model estimation has been corrected only for the random walk algorithm, which has problematic applicability to large-scale networks. This study proposes a correction term for the sampling probability of routes extracted with stochastic route generation. The term is easily applicable to large-scale networks and various environments, given its dependence only on a random number generator and the Dijkstra shortest path algorithm. The implementation for revealed preferences data, which consist of actual route choices collected in Cagliari, Italy, shows the feasibility of generating routes stochastically in a high-resolution network and calculating the correction factor. The model estimation with and without correction illustrates how the correction not only improves the goodness of fit but also turns illogical signs for parameter estimates to logical signs.