Towards measures of affective and eudaimonic subjective well-being in the travel domain

Studies of the connections between transportation and subjective well-being (SWB) require a clear understanding of the conceptual composition of travel-related SWB as well as psychometric instruments to measure these complex topics. Well-established psychological scales for measuring general SWB—including both hedonic (affective and cognitive) and eudaimonic aspects—are difficult to adapt or have yet to be tested in the travel domain. Existing measures of travel liking and travel satisfaction are somewhat inadequate for these purposes, especially for representing eudaimonia. Using a questionnaire survey of 680 commuters in the Portland, Oregon, region, exploratory and confirmatory factor analyses examined responses to a total of 42 items. Results suggested four-factor measurement models of both travel affect (Enjoyment, Attentiveness, Distress, and Fear) and travel eudaimonia (Health, Competence, Autonomy, and Security). Despite some limitations and opportunities for enhancements, these models show promise as ways of measuring affective and eudaimonic SWB in the travel domain for future studies and travel surveys.

     

Redefining activity types: Who participates in which leisure activity?

This paper analyzes the activity choices of individuals and the links between socio-demographics, daily schedules and activity attributes using a new activity choice framework. Activities are first clustered into groups based on their salient attributes, such as duration, frequency, flexibility, planning times, and number of involved persons, rather than their functional types (work, leisure and household obligations), using a K-means cluster technique. This led to the creation of several new activity groups such as “long, temporally fixed, personally flexible activities”, “short and flexible activities”. These activity groups form the choice set for the mixed logit activity choice modeling structure developed for the leisure activities in the second part of the paper. The model results reveal the significant relationships between socio-demographics, temporal characteristics, and characteristics of the schedules on leisure activity choice. The results demonstrate how changing demographics and other activities in individuals’ schedules may affect the nature of the leisure activities and present the substitution and complimentary effects that these new activity groups have on one another.     

Severity of injury resulting from pedestrian–vehicle crashes: What can we learn from examining the built environment?

This paper examines the impact of personal and environmental characteristics on severity of injuries sustained in pedestrian–vehicle crashes using a generalized ordered probit model. The data covers 2000–2004 of pedestrian–vehicle crashes taken from police incident reports for Baltimore City and supplemented with local land use, urban form and transportation information specific to the individual crash locations. The results on personal and behavioral variables confirm previous findings. Women pedestrians involved in crashes tend to be injured less frequently than their male counterparts; children have an increased likelihood of sustaining injuries and older persons are more likely to be fatally injured. Pedestrians who cross against the traffic signal, are not in a crosswalk and are involved in a crash after dark are associated with greater injury risk. Of the built environment policy variables of interest, transit access and greater pedestrian connectivity, such as central city areas, are significant and negatively associated with injury severity. These results suggest that the environmental conditions should be given more scrutiny and be an important consideration when evaluating and planning for pedestrian safety.     

Discretionary activity location choice: in-home or out-of-home?

This paper examines the location choice associated with discretionary activities (in-home vs. out-of-home). These substitution patterns are important in terms of travel demand as in-home activities do not necessitate travel while out-of-home activities incur travel. Mixed logit models are estimated using an activity dataset (2003 CHASE data) to analyze the factors associated with this choice at the individual activity-level. Results suggest that the attributes of an activity significantly contribute to understanding the likelihood of engaging in out-of-home activities. Activity type interaction terms reveal the varying influence that socio-demographics, activity attributes and travel have over four different activity types modeled. The results reveal that the location choice (in-home vs. out-of-home) is sensitive to travel characteristics. As the travel time and cost increases, an individual is less likely to engage in an activity out-of-home. Compared to passive and social activities, the location of active activities is more sensitive to changes in travel attributes.     

Modeling the effects of congestion on fuel economy for advanced power train vehicles

Fuel-speed curves (FSC) are used to account for the aggregate effects of congestion on fuel consumption in transportation scenario analysis. This paper presents plausible FSC for conventional internal combustion engine (ICE) vehicles and for advanced vehicles such as hybrid electric vehicles, fully electric vehicles (EVs), and fuel cell vehicles (FCVs) using a fuel consumption model with transient driving schedules and a set of 145 hypothetical vehicles. The FSC shapes show that advanced power train vehicles are expected to maintain fuel economy (FE) in congestion better than ICE vehicles, and FE can even improve for EV and FCV in freeway congestion. In order to implement these FSC for long-range scenario modeling, a bounded approach is presented which uses a single congestion sensitivity parameter. The results in this paper will assist analysis of the roles that vehicle technology and congestion mitigation can play in reducing fuel consumption and greenhouse gas emissions from motor vehicles.     

A Review of Pseudo Panel Data Approach in Estimating Short-run and Long-run Public Transport Demand Elasticities

Abstract

The distinctions between short-run and long-run public transport demand elasticities have been highlighted in the literature, but the identification of long-run travel demand has been constrained by existing research methodology and the unavailability of longitudinal travel survey data. The pseudo panel data approach using repeated cross-sectional data has been suggested as an alternative to conducting a longitudinal travel demand analysis when genuine panel data are not available. This paper comprehensively reviews the background and the current practices of pseudo panel data research, and introduces the challenges in applied research that need further investigation, particularly for public transport. A case study using the Sydney Household Travel Survey data is presented to demonstrate pseudo panel data construction and to identify the short-run and long-run public transport demand elasticities using a pseudo panel data approach. The research findings suggest that the public transport demand elasticity of price in Sydney is ?0.22 in the short run and ?0.29 in the long run.     

Local shopping as a strategy for reducing automobile travel

Transportation - Suburban development in the US is widely criticized for its contribution to automobile dependence and its consequences. Not surprisingly, then, a return to more urban-style...     

A simulation of the simple Mohring model to predict patronage and value of resources consumed for enhanced bus services

Choosing between the competing approaches to providing enhanced bus services requires the ability to predict the effects on patronage and overall benefits to society in terms of Value of Resources Consumed (VRC) of any proposed change. One common approach uses the Mohring model of 1972 and its successors to optimise one or more aspects of the supply of transit services given various level of demand. This paper simulates the operation of a simple circular bus route using the equations of a basic Mohring type model and a Decision Support System (DSS) to endogenise demand under the assumption that there is a fixed demand for travel but commuters can choose between the bus and private vehicle. The selected approach recognises that demand for a particular mode of travel is predicated on the speed of the vehicle in service which is itself determined by the demand for that vehicle (in this model or boarding and alighting time) rather than being determined exogenously. This creates an iterative feedback mechanism whereby changes to the attributes of the bus and car alter mode choice and the speed in service of the bus which, in turn, alters mode choice. The simulation is used to predict changes to patronage and VRC under scenarios regarding changes to the transport network (in terms of fares and running costs, frequency and spacing of bus stops) using parameters from both a Multinomial Logit (MNL) and a Mixed Multinomial Logit (MMNL). The results demonstrate that there are large differences in the size and, in some cases, direction of predicted changes to patronage, VRC and revenue, depending on whether the parameters of the MNL or MMNL model are used.