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Ergonomics Volume 64, 2021 - Issue 6
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Articles

How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study

Joost de WinterFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The NetherlandsCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-1281-8200View further author information
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Pavlo BazilinskyyFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The NetherlandsORCID Iconhttps://orcid.org/0000-0001-9565-8240View further author information
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Dale WesdorpFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The NetherlandsORCID Iconhttps://orcid.org/0000-0002-3089-4127View further author information
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Valerie de VlamFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The NetherlandsView further author information
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Belle HopmansFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The NetherlandsORCID Iconhttps://orcid.org/0000-0002-5069-2696View further author information
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Just VisscherFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The NetherlandsORCID Iconhttps://orcid.org/0000-0002-9086-2274View further author information
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Dimitra DodouFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The NetherlandsORCID Iconhttps://orcid.org/0000-0002-9428-3261View further author information
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Pages 793-805 | Received 10 Aug 2020, Accepted 05 Dec 2020, Published online: 29 Jan 2021

Figures & data

Figure 1. The layout of the floor of the parking garage and the the path walked by the participants. There were 57 parking places, as indicated by the numbers.

Parking garage layout.
Figure 1. The layout of the floor of the parking garage and the the path walked by the participants. There were 57 parking places, as indicated by the numbers.

Table 1. Examples of fixations per area of interest.

Figure 2. Boxplot of the normalised number of fixations (NNF). Each circular marker represents one participant (n = 36). The green cross represents the participant mean. One marker for Ground (75.1%) is not shown as it falls outside the range of the y-axis.

Boxplot of normalized number of fixations.
Figure 2. Boxplot of the normalised number of fixations (NNF). Each circular marker represents one participant (n = 36). The green cross represents the participant mean. One marker for Ground (75.1%) is not shown as it falls outside the range of the y-axis.

Figure 3. The number of transitions (NT) between AOIs (participants switching attention from ‘Fixation from’ to ‘Fixation to’). The total number of transitions is 12,563, which equals the total number of fixations (12,599) minus the number of participants (36). The numbers on the diagonal represent re-fixations on the same AOI.

Number of transitions between AOIs.
Figure 3. The number of transitions (NT) between AOIs (participants switching attention from ‘Fixation from’ to ‘Fixation to’). The total number of transitions is 12,563, which equals the total number of fixations (12,599) minus the number of participants (36). The numbers on the diagonal represent re-fixations on the same AOI.

Figure 4. The normalised number of transitions (NNT) between AOIs. The normalisation was performed according to the expected number of transitions if totally random sampling were used (with diagonal removed). The data for the category ‘driver in other moving car’ was omitted because of the small number of fixations. The depicted numbers were multiplied with a constant C, so that they add up to 100%.

Normalized number of transitions between AOIs.
Figure 4. The normalised number of transitions (NNT) between AOIs. The normalisation was performed according to the expected number of transitions if totally random sampling were used (with diagonal removed). The data for the category ‘driver in other moving car’ was omitted because of the small number of fixations. The depicted numbers were multiplied with a constant C, so that they add up to 100%.

Figure 5. Left: Head yaw rate of a participant with a low head movement index (11.3 deg/s). Right: Head yaw rate of a participant with a high head movement index (27.4 deg/s). A positive value indicates a rotational velocity towards the left.

Illustration of head yaw rate.
Figure 5. Left: Head yaw rate of a participant with a low head movement index (11.3 deg/s). Right: Head yaw rate of a participant with a high head movement index (27.4 deg/s). A positive value indicates a rotational velocity towards the left.

Figure 6. Correlation between head movement index and normalised number of fixations (n = 36). The error bars represent 95% confidence intervals.

Correlations between head movement and normalized number of fixations.
Figure 6. Correlation between head movement index and normalised number of fixations (n = 36). The error bars represent 95% confidence intervals.

Figure 7. Example of a participant tracking a car behind parked cars.

Following a car behind parked cars.
Figure 7. Example of a participant tracking a car behind parked cars.
Supplemental material

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Data availability statement

Raw data and MATLAB code used for the analysis is available at https://doi.org/10.4121/13488384

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