In recent years, cycling has attracted increasing attention as a sustainable alternative to private car use. In cities across the world, strategies are put in place to improve existing cycling infrastructure. This raises the question of how cyclists move through a city and what p
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In recent years, cycling has attracted increasing attention as a sustainable alternative to private car use. In cities across the world, strategies are put in place to improve existing cycling infrastructure. This raises the question of how cyclists move through a city and what part of the road network attracts the most cyclists. Prior studies on cycling behaviour have revealed that cyclists prefer routes that require less turns, over separated bicycle paths, smoother street surface materials like asphalt or concrete. Furthermore, dense and mixed-use neighbourhoods seem to attract more active travel. This research will analyse correlations between spatial characteristics and cycling route choices in Amsterdam.
Space Syntax is an analysis method that studies the urban morphology of a city. Until recently, the implementation of Space Syntax has mainly focused on the analysis of pedestrian flows, with a limited number of studies applying the methodology to cyclist behaviour. This master thesis presents exploratory research into the application of Space Syntax – in combination with other built environment characteristics – to study cyclist route choice. GPS data from the 2016 Bicycle Counting Week shows the cycling counts of every street segment.
A linear regression analysis found that “through-movement potential” represented by Normalised Angular Choice (NACH) explained more than 22% of variance in cycling activity. The results indicate that Space Syntax is an interesting indicator to locate which street segments could potentially see large numbers of cyclists. More research encompassing multiple cities in a variety of different contexts is recommended, as Amsterdam is a city with a rich cycling culture that spans multiple decades, making it difficult to generalize any conclusions.