Problem
Research questions and friction points this paper is trying to address.
cyclist route choice
visual factors
non-visual factors
trip purpose
built environment
Examining the Associations between Visual and Non-Visual Elements and Cyclists' Route Choices for Various Trip Purposes
🤖 AI Summary
This study addresses the insufficient understanding of how visual and non-visual factors jointly influence cyclists’ route choices across different trip purposes. Using Montreal as a case study, it pioneers a systematic integration of street view–derived visual perceptions—such as greenery and motorization levels—with socioeconomic characteristics and two-dimensional built environment variables. By combining spatiotemporal data mining with a comparative modeling framework that contrasts actual routes against shortest-path alternatives, the analysis is stratified by trip purpose to uncover distinct route choice mechanisms. The findings reveal that higher levels of greenery and lower motorization significantly encourage active cycling, offering empirical support and methodological innovation for the fine-grained planning of green transportation infrastructure.
📝 Abstract
Understanding cyclist preferences for the characteristics of the built environment is important in promoting sustainable urban transportation and active mobility. Despite previous studies on cyclists' route choices, the influence of visual and non-visual factors on these choices for different trip purposes remains unclear; thus, this paper fills this gap through a data-driven case study in Montreal, Canada. Non-visual factors include socioeconomic factors and two-dimensional environments, while visual factors involve visual perception during cycling and are computed using street view images. The study consists of two parts: one part analyzes spatiotemporal information to explore the non-visual factors between the start and end points of cycling trips, and the other part investigates the discrepancies in distributions of these factors between the shortest path and the actual one. The findings reveal the spatiotemporal characteristics that influence active riding choices, such as increased greenery and lower levels of motorization. These insights can inform the planning of street networks and the development of infrastructure to improve the use of active transportation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
street view imagery
visual perception
route choice
active mobility
built environment
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