Background:
Traffic demand is growing worldwide and the increased carbon emission from transport and travel activities is contributing to greenhouse gas emission and climate change. As the oil and gas capital of Canada, the city of Calgary has a very high carbon footprint per population and the reduction of automobile use is an important policy goal for the city. Walking, a part of active transportation promotes sustainable transportation initiative by reducing greenhouse gas emission. To encourage walking, favorable walking environment should be ensured which largely depends on street pattern and connectivity. However, the effect of street pattern on walking at community level was not explored much in previous studies, particularly at rapidly expanding city such as Calgary’s context.
Aims and Objectives:
The study identifies the effects of different neighborhood design and planning factors associated with the share of walking in work trips while controlling for differences in social economic characteristics of the neighborhood.
Methods:
A linear regression model was developed using community-level data from the 2011 census and the road infrastructure data of Calgary.
Results:
Our study finds that different street patterns and types of land use, length of train tracks, number of train stations and number of schools have significant effect on walking.
Conclusion:
Thus, different neighbourhood street patterns and land uses should be considered in the development of new communities for promoting active and sustainable transportation.
Like other developed countries, Australia is experiencing considerable growth in air travel demand. This growth is putting tremendous pressure on airports to improve the capacity and level of service of access and egress modes. The main goal of this study was to develop a robust mode choice model for passengers’ access to airports, in the context of Brisbane, Australia. The novelty of this study includes (1) the use of recent techniques to combine passengers’ revealed and stated preferences for mode of access to airports and (2) the development of both traditional multinomial logit (MNL) as well as mixed logit (MXL) models using these data. The data were collected from 1,435 passengers through an online survey of both revealed and stated preferences, with the stated preferences pivoting on their most recent trip to Brisbane Airport. With these data, access mode choice models were estimated. MNL and MXL models were estimated that directly considered the effects of passenger mode choice inertia as well as novel passenger-specific characteristics. These factors had a large, statistically significant effect on the estimated models. The benefit of the MXL model was shown in the results, as the passengers’ value of time from the MXL model was A$105.15/h, which was strikingly lower than the MNL-estimated value of time. In addition, there were notably high direct elasticities for bus and shuttle with respect to travel time, and for car and taxi with respect to travel cost.
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