Previous research has demonstrated the influence of street layout on travel behaviour; however, little research has been undertaken to explore these connections using detailed and robust street network analysis or cycling data. In this study, we harness state-of-the-art datasets to model cyclists¶ route choice based on a case study of the City of Glasgow, Scotland. First, the social fitness network Strava was used to obtain datasets containing the number of cycling trips on each street intersection for the years 2017 and 2018. Second, we employed a Python toolkit to acquire and analyse the street networks. OSMnx was subsequently employed to quantify several commonly used centrality indices (degree, eigenvector, betweenness and closeness) to measure street layout. Due to the presence of spatial dependence, a spatial error model was used to model route choices. Model results demonstrate that: (1) cyclists¶ movement models were consistent for the years 2017 and 2018; (2) the presence of a spillover effect suggests that cyclists tend to cycle in proximity to each other; and (3) cyclists avoid streets with high degree centrality values and prefer streets with high eigenvector centrality, betweenness centrality and closeness centrality. These findings reveal cyclists¶ desired street layouts and can be taken into consideration for future interventions.
|Journal||Transportation Research Interdisciplinary Perspectives|
|Publication status||Accepted/In press - 3 Jan 2021|
- Spatial modelling