An analysis of bicycle route choice preferences in Texas,US

In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.

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.

Modeling the spatial and temporal dimensions of recreational activity participation with a focus on physical activities

This study presents a unified framework to understand the weekday recreational activity participation time-use of adults, with an emphasis on the time expended in physically active recreation pursuits by location and by time-of-day. Such an analysis is important for a better understanding of how individuals incorporate physical activity into their daily activities on a typical weekday, and can inform the development of effective policy interventions to facilitate physical activity. Furthermore, such a study of participation and time use in recreational activity episodes contributes to activity-based travel demand modeling, since recreational activity participation comprises a substantial share of individuals’ total non-work activity participation. The methodology employed here is the multiple discrete continuous extreme value (MDCEV) model, which provides a unified framework to explicitly and endogenously examine time use by type, location, and timing. The data for the empirical analysis is drawn from the 2000 Bay Area Travel Survey (BATS), supplemented with other secondary sources that provide information on physical environment variables. To our knowledge, this is the first study to jointly address the issues of ‘where’, ‘when’ and ‘how much’ individuals choose to participate in ‘what type of (recreational) activity’.     

Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously

Agent-based microsimulation models of transportation, land use or other socioeconomic processes require an initial synthetic population derived from census data, conventionally created using the iterative proportional fitting (IPF) procedure. This paper introduces a novel computational method that allows the synthesis of many more attributes and finer attribute categories than previous approaches, both of which are long-standing limitations discussed in the literature. Additionally, a new approach is used to fit household and person zonal attribute distributions simultaneously. This technique was first adopted to address limitations specific to Canadian census data, but could also be useful in U.S. and other applications. The results of each new method are evaluated empirically in terms of goodness-of-fit.     

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.     

Global Fish Trade,Prices, and Food Security in an African Coral Reef Fishery

This study evaluated the potential impact of global fish trade on local food prices by analyzing a 16-year locally collected time series of disaggregated coral reef fish products and prices that differed in their market chain linkages—ranging from local to international markets. We were primarily interested in evaluating how local and global markets interacted with the local prices of beef, fish, and maize. There was no cointegration between the prices of exported octopus and that of maize and beef over this study period. Further, the three types of fish and associated markets responded in different ways to various price changes. For internationally traded octopus, we found a positive association between price and catch rates but no evidence that the global trade in octopus markets created local inflation, particularly the prices of the fish eaten by the poor. In general, there was no evidence for price transmission from export to nonexport fish products even though fishers appeared to focus on octopus when prices were high. Consequently, fishers' behaviors and trade policies that promote adjusting fishing effort to internationally traded fish did not appear to promote poverty or food insecurity in this fishery.     

A review of guidelines for including wider economic impacts in transport appraisal

In this paper we examine how 23 industrialised countries treat wider economic impacts (WEIs) in transport appraisals. We identify 12 different types of impacts based on these countries’ appraisal guidelines. Agglomeration impacts and Production changes in imperfect markets are the most widely accepted, being recognised by 14 and 10 countries, respectively. However, about half of the impacts are mentioned by only one country, and few recommend including the impacts directly in cost–benefit analyses. Several countries provide provisos or criteria that must be met before WEIs can be assessed in the first place. We found method recommendations for quantifying WEIs in 10 countries. However, with the exception of the UK Department for Transport’s (DfT) methodological framework, there is very little international consensus on the choice of appropriate methods. Our findings thus supplement and reinforce the conclusions from the Norwegian Official Report of the Hagen Committee [NOU 2012:16. (2012). Samfunnsøkonomiske analyser. Oslo: Departementenes servicesenter] that there is currently no established consensus on the magnitude and relevance of WEIs, or on how and which of these impacts should be taken into account in transport appraisals. Recommendations for further research and appraisal practices are provided.     

A comprehensive analysis of household transportation expenditures relative to other goods and services: an application to United States consumer expenditure data

This paper proposes a multiple discrete continuous nested extreme value (MDCNEV) model to analyze household expenditures for transportation-related items in relation to a host of other consumption categories. The model system presented in this paper is capable of providing a comprehensive assessment of how household consumption patterns (including savings) would be impacted by increases in fuel prices or any other household expense. The MDCNEV model presented in this paper is estimated on disaggregate consumption data from the 2002 Consumer Expenditure Survey data of the United States. Model estimation results show that a host of household and personal socio-economic, demographic, and location variables affect the proportion of monetary resources that households allocate to various consumption categories. Sensitivity analysis conducted using the model demonstrates the applicability of the model for quantifying consumption adjustment patterns in response to rising fuel prices. It is found that households adjust their food consumption, vehicular purchases, and savings rates in the short run. In the long term, adjustments are also made to housing choices (expenses), calling for the need to ensure that fuel price effects are adequately reflected in integrated microsimulation models of land use and travel.     

Hoop stress approximation in offshore design codes

For a circular cylindrical vessel with an elastic material, Lame equations can accurately predict hoop stress variation within the vessel wall. However, because of the complexity involved in Lame formulations they are seldom used in design. In this paper, Lame equations are reproduced in terms of vessel outer and material cross-sectional areas and presented in a very simple format that enables hoop stress calculation without use of any approximation. Lame equations are also presented in terms of diameter-to-wall-thickness ratios in order to perform parametric studies. For a practical range of diameter-to-wall-thickness ratios the Lame hoop stress predictions are compared with approximate solutions of a selected design codes. For a range of loading conditions, comparison of results shows that a number of design codes overestimate the hoop stress. In contrast, a selection of offshore codes is shown to underestimate the hoop stress and, for a certain loading condition some codes ignore the hoop stress effect completely. The present paper also shows how the hoop stress approximation may lead to onerous results when the true wall axial stress is derived based on design code hoop stress formulations. It is concluded that what makes the present hoop stress formulations so important in design is their ability to interpret the mechanics of behavior that Lame equations strived to reveal.     

The economic impact of the ATA/Southwest Airlines code-share agreement

This paper examines the economic impact of the code-sharing agreement between ATA and Southwest Airlines, the first time Southwest has chosen to enter markets via a code-share alliance rather than direct entry. We examine the impact of complementary code-sharing on incumbent airline's fares and passenger volumes and also the overall impact on consumer welfare in Denver airline markets. Empirical results show that air fares decreased and passenger volumes increased for incumbent carriers operating on code-shared routes. Further, we find evidence that this code-sharing arrangement increased both consumer and producer surplus. Thus, the well-known “Southwest Effect” is observed not only when Southwest enters a route directly, but also when it enters a route via a code-share agreement with another airline.