A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

How do ocean carriers organize the empty containers reposition activity in the USA?

The routing of empty containers is an unavoidable activity of the intermodal chain. In fact, import containers that arrive at a national port are sent toward a multiplicity of interior destinations. Then the empty containers must return to a port to accomplish an export voyage. The purpose of this paper is to analyse the organizational choice of ocean carriers to reposition their empty containers in the USA. Ocean carriers have four options to relocate their empty containers: the spot organization and the adoption of three different renewable contracts to frame the externalization. How do ocean carriers choose their organization form to relocate their empty containers?     

Lessons learned from a personalized travel planning (PTP) research program to reduce car dependence

Voluntary travel behavior change programs have been implemented worldwide since the late 1990s at a personal and community level. Most of the now completed programs were commissioned by local authorities to commercial firms, in an attempt to reduce private car use. In this context, the evaluation and review of the reliability of these policy measures have been at the core of most of the debates and studies in this field. In this paper, we describe the lessons learned from a research program funded by the Sardinian Government (Italy), aimed at testing a soft transport policy measure for reducing car dependence. In particular, the work reviews in detail the methodological approach and participants’ feedback on a personalized travel plan (PTP). After implementation of the soft measure, the PTP participants were divided into two groups depending on whether they had reduced car use or not, and separate analyses were conducted to highlight the factors underlying different behavior change decisions. General conclusions regarding the effectiveness of the PTP are beyond the scope of the present study.     

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.

The persevering commuter – Duration of long-distance commuting

A growing number of people are long-distance commuters. For some long-distance commuting is a temporary solution, while as for others it can be a more long-term strategy to promote career and income. This study addresses duration of long-distance commuting – 30 km or more between home and work – in Sweden, and what characterizes individuals who commute for shorter or longer periods. The effects of long-distance commuting in terms of economic outcome for both partners in a commuter household are analysed. The study is based on register data for the years 1995–2005 covering all long-distance commuters in Sweden. One finding is that previous experience of long-distance commuting makes it more likely to have a long duration of long-distance commuting. In addition economic incentives, such as a higher income, are positively correlated for continuing to long-distance commuting more than a few years. Furthermore, the analysis shows that male commuters benefit more in terms of economic outcome of long-distance commuting. It is concluded that the trend with increasing long-distance commuting can sustain not only gender differences on the labour market but also within households. Finally, the paper indicates that long-distance commuting is a strategic mobility choice for households, rather than a short-term solution for a few years.