A cyclist at an intersection looking around

Crowdsourcing Bike Safety

The goal of this project is to use mobile crowdsourcing to collect data on situations where the cyclists did not feel safe, but that didn’t lead to an accident. By logging and measuring head movement from smart earables, such as the Apple EarPod Pro, we want to automatically analyze where cyclists feel unsafe to provide urban planners with such information.

This is a project in collaboration with Andrii Matviienko (TU Darmstadt) and Bastian Pfleging (TU Eindhoven).

  • [DOI] A. Matviienko, F. Heller, and B. Pfleging, “Quantified Cycling Safety: Towards a Mobile Sensing Platform to Understand Perceived Safety of Cyclists,” in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, New York, NY, USA, 2021.
    [Bibtex]
    @inproceedings{10.1145/3411763.3451678,
    abstract = {Today's level of cyclists' road safety is primarily estimated using accident reports and self-reported measures.
    However, the former is focused on post-accident situations and the latter covers only subjective input.
    In our work, we aim to build a full picture of cyclists' safety assessment via a two-dimensional taxonomy, which covers data source (internal/external) and type of data source (objective/subjective).
    Based on this taxonomy, we present a mobile sensing concept for quantified cycling safety that fills the identified methodological gap by collecting data about body movements and physiological data.
    Finally, we outline a list of use cases and future research directions within the scope of the proposed taxonomy and sensing concept.},
    address = {New York, NY, USA},
    author = {Matviienko, Andrii and Heller, Florian and Pfleging, Bastian},
    booktitle = {Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems},
    date-added = {2021-02-22 20:20:42 +0100},
    date-modified = {2021-02-22 20:25:42 +0100},
    doi = {10.1145/3411763.3451678},
    isbn = {97814503809592105},
    keywords = {cyclist safety, crossing decision, head movements, perceived safety},
    location = {Yokohama, Japan},
    publisher = {Association for Computing Machinery},
    series = {CHI EA '21},
    title = {Quantified Cycling Safety: Towards a Mobile Sensing Platform to Understand Perceived Safety of Cyclists},
    url = {https://doi.org/10.1145/3411763.3451678},
    year = {2021}}