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

• URL: http://arxiv.org/abs/2607.15808v1
• Date: Fri, 17 Jul 2026 10:21:01 GMT
• Title: Examining the Associations between Visual and Non-Visual Elements and Cyclists' Route Choices for Various Trip Purposes
• Abstract summary: The study consists of two parts: one part analyzes the non-visual factors between the start and end points of cycling trips, and the other part investigates the discrepancies in these factors between the shortest path and the actual one.<n>The findings reveal thetemporal characteristics that influence active riding choices, such as increased greenery and lower levels of motorization.
• Score: 1.3108652488669734
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres [2.770226625653906]
  This study develops a perception-led, typology-based, and data-integrated framework.<n>It explicitly models street typologies and their sub-classifications to evaluate how visual and spatial configurations shape cycling experience.<n>The framework offers a transferable model for evaluating and improving cycling conditions in heritage cities.
  arXiv  Detail & Related papers  (2026-01-30T23:22:11Z)
• Exploring Sidewalk Sheds in New York City through Chatbot Surveys and Human Computer Interaction [47.311965900698084]
  We develop an AI-based survey that collects image-based annotations and route choices from pedestrians.<n>This paper conducts a grid-based analysis of entrance annotations and applies logistic mixed-effects modeling to assess sidewalk choice patterns.<n>By integrating generative AI into urban research, this study demonstrates a novel method for evaluating sidewalk shed designs.
  arXiv  Detail & Related papers  (2026-01-30T15:41:44Z)
• How built environment shapes cycling experience: A multi-scale review in historical urban contexts [2.770226625653906]
  We systematically reviewed 68 studies across urban planning, transportation, behavioural science, neuroscience, and public health.<n>We find a persistent reliance on objective proxies, limited integration of subjective accounts, and insufficient attention to the streetscape as a lived environment.
  arXiv  Detail & Related papers  (2025-09-19T06:30:32Z)
• Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons [2.3900828891729784]
  Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle.<n>In this study, we tackle the problem of capturing and understanding how individuals perceive cycling risk.<n>We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments.<n>We ask them to select the one they perceive as safer for cycling, if any.<n>Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals' responses, learns preferences directly from images,
  arXiv  Detail & Related papers  (2024-12-13T03:56:40Z)
• Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
  We present the first study to utilize a Graph Convolutional Network (GCN) architecture to model link-level bicycling volumes.<n>We benchmark it against traditional machine learning models, such as linear regression, support vector machines, and random forest.<n>Our results show that the GCN model outperforms these traditional models in predicting Annual Average Daily Bicycle (AADB) counts.
  arXiv  Detail & Related papers  (2024-10-11T04:53:18Z)
• Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning [2.678595263943329]
  We propose a novel framework aimed to quantify and interpret cyclists' complicated visual preferences. We adapt MEDIRL model for efficient estimation of cycling reward function. We find that cyclists focus on specific street visual elements when making route decisions.
  arXiv  Detail & Related papers  (2024-09-05T00:54:48Z)
• Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons [0.9299655616863538]
  This study presents a novel approach to identifying how the perception of cycling safety can be analyzed and understood. We repeatedly show respondents two road environments and ask them to select the one they perceive as safer for cycling. We compare several methods capable of rating cycling environments from pairwise comparisons and classify cycling environments perceived as safe or unsafe.
  arXiv  Detail & Related papers  (2023-07-25T10:31:45Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
  Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing. Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints. We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
  arXiv  Detail & Related papers  (2020-09-23T14:56:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.