Non-linear influences of the built environment on transportation emissions: focusing on densities

Compact development is often recommended to reduce auto-dependency thereby decreasing related energy consumptions and transportation emissions. However, there could be a non-linear relationship between density and transportation emissions because of a possible non-linear association between density and vehicle miles travelled (VMT); low travel speed due to congestion; and the relationship between neighborhood characteristics and vehicle characteristics (e.g., vehicle type and age). In addition, the self-selection issue can exist in the land use-transportation emissions analysis because transportation emissions are often estimated based on travel behavior. Using the 2006 Puget Sound Regional Council (PSRC) Household Activity survey, the follow-up stated preference survey, the Motor Vehicle Emission Simulator (MOVES) data, and the GIS network data, this study investigates the non-linear effects of densities on CO2 equivalent (CO2e) emissions with the consideration of self-selection. Specifically, quadratic forms of population and employment densities, different population density group indicators, and attitudinal factors are employed in the regression models. The results indicate that people living in denser neighborhoods tend to generate fewer CO2e emissions. However, this effect becomes insignificant as population density reaches a certain level

How does the seasonality influence utilitarian walking behaviour in different urbanization settings in Scotland?

The relationship between the built environment and walking has been analyzed for decades. However, the seasonality effects on the relationship between the built environment and walking have not been well examined even though weather is one of the key determinants of walking. Therefore, this study used 2007–8 Scottish Household Survey data collected over two years and estimated the interaction effects between the urbanization setting (i.e., residential locations: urban, town and rural areas) and seasons (i.e., spring, summer, autumn and winter) on walking. Scottish Urban-rural classification scheme is measured based on the population and access to large cities, and used as a key independent variable. The number of walking days for specific purposes such as work or shopping (utilitarian walking) during the past 7 days is used as a dependent variable. The results show that there are significant geographical variations of seasonality effect on utilitarian walking. That is, people living in rural areas are more sensitive to seasonality impacts than those living in urban areas. In addition, we found that the association between urbanization setting and utilitarian walking varies across seasons, indicating that their relationship can be miss-estimated if we ignore the seasonality effects. Therefore, policy makers and practitioners should consider the seasonality effects to evaluate the effectiveness of land use policy correctly. Finally, we still find the significant association between the urbanization setting and utilitarian walking behaviour with the consideration of seasonality effects, supporting the claim of New Urbanism

Does public transit improvement affect commuting behavior in Beijing, China? : A spatial multilevel approach

Developing countries like China have experienced substantial city transformations over the past decade. City transformations are characterized by transportation innovations that allow individuals to access to speedy commuting modes for work activities and offer potential influences on commuting behavior. This paper examines the potential effects of subway system expansion in Beijing on commuting behavior. Our methodological design controls for spatial effects by employing Bayesian multilevel binary logistic models with spatial random effects. Using cross-sectional individual surveys in Beijing, the results suggest that there is a significant rise in subway commuting trips while non-motorized and bus commuting trips are reduced with the new subway expansion. Model comparison results show evidence about the presence of spatial effects in influencing the role of built environment characteristics to play in the commuting behavior analysis

Examining the relationship between different urbanization settings, smartphone use to access the Internet and trip frequencies

No abstract available

How does Internet usage influence young travellers' choices?

New technologies have significant effects on travel behavior, attitudes, habits and potentially future travel demand. Effects may be more prominent for Millennials. Little empirical research has investigated these relationships, mainly due to data limitations. This study focuses on the potential influence of using the Internet while traveling on Millennials’ plans for car ownership. We examine two questions: Does using the Internet while traveling influence trip frequencies? and Does it affect Millennials’ intention to purchase a car? Results suggest that Internet use while traveling is positively associated with travel demand and the intention to purchase a car in the near future

Might Using the Internet While Travelling Affect Car Ownership Plans of Millennials?

New technologies have significant effects on travel behaviour, attitudes, habits and potentially future travel demand. Effects may be more prominent for Millennials. Little empirical research has investigated these relationships, mainly due to data limitations. This study focuses on the potential influence of using the Internet while travelling on Millennials’ plans for car ownership. We examine two questions: does using the Internet while travelling influence trip frequencies?; and does it affect Millennials’ intention to purchase a car? Results suggest that Internet use while travelling is positively associated with travel demand and the intention to purchase a car in the near future

Visualising where commuting cyclists travel using crowdsourced data

Encouraging more cycling is increasingly seen as an important way to create more sustainable cities and to improve public health. Understanding how cyclists travel and how to encourage cycling requires data; something which has traditionally been lacking. New sources of data are emerging which promise to reveal new insights. In this paper, we use data from the activity tracking app Strava to examine where people in Glasgow cycle and how new forms of data could be utilised to better understand cycling patterns. We propose a method for augmenting the data by comparing the observed link flows to the link flows which would have resulted if people took the shortest route. Comparing these flows gives some expected results, for example, that people like to cycle along the river, as well as some unexpected results, for example, that some routes with cycling infrastructure are avoided by cyclists. This study proposes a practical approach that planners can use for cycling plans with new/emerging cycling data

The Use of Crowd-sourced Cycling Data for Cycling Analyses (Strava)

This presentation includes several research outcomes from the UBDC transport work package. Specifically, we validate the usefulness of Strava data for cycling behaviour analysis and provide two examples (i.e., commuting cycling behaviour and evaluation of new cycling infra)

The Use of Crowd-sourced Cycling Data for Cycling Analyses (Strava)

This presentation includes several research outcomes from the UBDC transport work package. Specifically, we validate the usefulness of Strava data for cycling behaviour analysis and provide two examples (i.e., commuting cycling behaviour and evaluation of new cycling infra)

The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the significant net benefits, many local governments in Scotland have promoted cycling. Glasgow City Council constructed four significant pieces of cycling infrastructure between 2013 and 2015, partly in preparation for the 2014 Commonwealth Games and partly to encourage cycling more generally. This required substantial capital investment. However, the effectiveness of these big new infrastructure investments has not been well examined, mostly due to data limitations. In this study, we utilised data from the activity tracking app Strava for the years 2013–2016 and fixed effects panel data regression models to examine whether the new cycling infrastructure has increased cycling volumes on these routes. Our results show that three of the infrastructure projects have a positive effect on the monthly total volume of cycling trips made by users of the app, with flows up by around 12% to 18%. Although this result is promising, it needs to be interpreted with care due to the characteristics of the data