Factors affecting public transportation,car, and motorcycle usage

This study established a hypothesis model based on the seemingly unrelated regression equations (SURE) model to investigate the relationship between public transportation, car, and motorcycle use in various townships in Taiwan and to analyse important factors that affect the usage of these modes. The SURE model was adopted because of the lack of a significant correlation between the dependent variables. The pairwise covariance analysis for any two of the three transportation modes revealed that the transportation modes could substitute for one another. Factors related to modal and demographic characteristics had different impacts on the usage of the three modes. The calculation of elasticity using different population densities and public transportation usage showed that when the ‘number of city bus routes’ was increased by 50% in areas with high population density and high public transportation usage, car usage decreased by 1.4%, which corresponds to 300,000 vehicles, and total CO₂ emissions reduced by 0.0204%. When the ‘total length of city bus routes’ was increased by 50%, the number of motorcycles used decreased by 83 million, and total CO₂ emissions reduced by 1.119%, which corresponds to 1.4 million tonnes of CO₂ emission. These findings suggest that these different factors had varying impacts on car and motorcycle usage in different areas. We therefore recommended that future transportation policies consider the varying transportation usage trends in different areas.     

Volatility of car ownership, commuting mode and time in the UK

This paper has two objectives: to examine the volatility of travel behaviour over time and consider the factors explaining this volatility; and to estimate the factors determining car ownership and commuting by car. The analysis is based on observations of individuals and households over a period of up to 11 years obtained from the British Household Panel Survey (BHPS). Changes in car ownership, commuting mode and commuting time over a period of years for the same individuals/households are examined to determine the extent to which these change from year-to-year. This volatility of individual behaviour is a measure of the ease of change or adaptation. If behaviour changes easily, policy measures are likely to have a stronger and more rapid effect than if there is more resistance to change. The changes are “explained” in terms of factors such as moving house, changing job and employment status. The factors determining car ownership and commuting by car are analysed using a dynamic panel-data models.     

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.     

Synopsis of bicycle demand in the City of Toronto: Investigating the effects of perception,consciousness and comfortability on the purpose of biking and bike ownership

This paper provides an empirical basis for the evaluation of policies and programs that can increase the usage of bikes for different purposes as well as bike ownership. It uses an integrated econometric model of latent variable connecting multiple discrete choices. Empirical models are estimated by using a bicycle demand survey conducted in the City of Toronto in 2009. Empirical investigations reveal that latent perceptions of ‘bikeability’ and ‘safety consciousness’ directly influence the choice of biking. It is also found that the choice of the level of bike ownership (number of bikes) is directly influenced by latent ‘comfortability of biking’. The number of bikes owned moreover has a strong influence on the choices of biking for different purposes. It is clear that bike users in the City of Toronto are highly safety conscious. Increasing on-street and separate bike lanes proved to have the maximum effects on attracting more people to biking by increasing the perception of bikeability in the city, comfortability of biking in the city and increasing bike users’ sense of safety. In terms of individuals’ characteristics, older males are found to be the most conformable and younger females are the least comfortable group of cyclists in Toronto.