Evidence map›Paper›PMID 39796809›Full record

SynthesisSensors (Basel, Switzerland)2024

Understanding Cyclists' Visual Behavior Using Eye-Tracking Technology: A Systematic Review.

Fatima Kchour, Salvatore Cafiso, Giuseppina Pappalardo

Abstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Fatima KchourDepartment of Civil Engineering and Architecture, University of Catania, 64 Santa Sofia Street, 95123 Catania, Italy.ORCID 0009-0009-7884-3909
Salvatore CafisoDepartment of Civil Engineering and Architecture, University of Catania, 64 Santa Sofia Street, 95123 Catania, Italy.
Giuseppina PappalardoDepartment of Civil Engineering and Architecture, University of Catania, 64 Santa Sofia Street, 95123 Catania, Italy.ORCID 0000-0002-9793-1885

Funding

This paper was partially supported by the European Union (NextGeneration EU), through the MUR-PNRR project RESTART (CUP E63C22002070006) as well as by PNRR, National Centre for HPC, Big Data and Quantum Computing, Mission 4 Component 2, Investment 1.4, CU CUP E63C22002070006 and CUP E63C22001000006.
6 · The paper itself

Abstract

Eye-tracking technologies are emerging in research aiming to understand the visual behavior of cyclists to improve their safety. These technologies gather real-time information to reveal what the cyclists look at and how they respond at a specific location and time. This systematic review investigates the use of eye-tracking systems to improve cyclist safety. An extensive search of the SCOPUS and WoS databases, following the PRISMA 2020 guidelines, found 610 studies published between 2010 and 2024. After filtering these studies according to predefined inclusion and exclusion criteria, 25 were selected for final review. The included studies were conducted in real traffic or virtual environments aiming to assess visual attention, workload, or hazard perception. Studies focusing on other types of road users or participants not involved in active cycling were excluded. Results reveal the important impact of road elements' design, traffic density, and weather conditions on cyclists' gaze patterns. Significant visual workload is imposed mainly by intersections. Along with the valuable insights into cyclist safety, potential biases related to small sample sizes and technological limitations were identified. Recommendations for future research are discussed to address these challenges through more diverse samples, advanced technologies, and a greater focus on peripheral vision.

Indexed as

BicyclingEye-Tracking TechnologyAttentionEye MovementsHumansSafetyVisual Perceptioncyclist safetyeye-tracking systemsgaze behaviorhazard perceptionroad safetyvisual attentionvisual workload

Identifiers

PMID39796809
PMCPMC11723293

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.