# Examining the Associations between Visual and Non-Visual Elements and Cyclists' Route Choices for Various Trip Purposes

arXiv: https://arxiv.org/abs/2607.15808 · HTML: https://mlsanity.com/paper/2607.15808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 121 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [Estimating city-level travel patterns using street imagery: a case study of using Google Street View in Britain](https://mlsanity.com/paper/1802.02915/) (2018)
- [URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision](https://mlsanity.com/paper/1809.03609/) (2018)
- [Vector-based Pedestrian Navigation in Cities](https://mlsanity.com/paper/2103.07104/) (2021)
- [Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development](https://mlsanity.com/paper/1911.08007/) (2019)

## Abstract (arXiv)

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.