Data Capture and Treatment

This section provides a description of the three parallel measurements used in the study, including details of their apparatus.

4.7.1 Eye-tracking apparatus

Gaze directions were captured using eye-tracking glasses (SMI ETG 2W analysis Pro) worn by the participant, see Figure 4.7. These were connected to a mobile recording device (Samsung Galaxy S4 GT-19506) and operated by iView ETG 2.1 software. This recording device was stored in a waist bag worn by the participant. Data were recorded using a sampling frequency of 60 Hz.

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The recorded data were then downloaded to a laptop running Begaze SMi experiment suite 360°. This software analyses the recording and generates a more comprehensive data file, which provides details about fixations, saccades, blinks, and the coordinates of the gaze position. It has a semantic mapping feature enabling fixations to be placed into different target categories, see Section 4.7.2 and Figure 4.8. The data can automatically be extracted to Excel for further analysis.

In this study, only eye fixations were used (saccades or blinks were not used). These instances where the participant’s gaze settles on a location in the scene for a specific time were identified using the event detection method of Begaze, which calculates a variety of eye metrics including fixations (SMI, 2016). This automated process has an advantage over the traditional method of calculating the dwell time or gaze coordinates when identifying fixations, as it is both time-efficient especially for long experiments and excludes human errors that are likely to occur when manually identifying eye fixations. This method has been implemented previously (Mantuano et al., 2017; Vansteenkiste et al., 2013; Viaene et al., 2016; Zeuwts et al., 2016).

The method is based on identifying two consecutive saccades, and the period between them is considered a fixation (Holmqvist et al., 2011; SMI, 2016). Thus, each fixation is bordered by two saccades. As per the Begaze software manual, fixations of under 50 milliseconds are removed within the event detection process. Using a fixation duration threshold of 50 milliseconds and above in eye-tracking studies is acceptable (Inhoff and Radach, 1998).

4.7.2 Semantic mapping: defining target categories

As explained earlier, eye fixations are the main eye-tracking data utilised to assess cyclists’ visual behaviour in current study. For these fixations to be meaningful, they need to be mapped with a reference describing what the participant is looking at; thus 8 categories were used in the semantic mapping stage.

These categories were derived from previous eye-tracking studies (Fotios et al., 2015b; Fotios et al., 2015c; Foulsham et al., 2011; Vansteenkiste et al., 2014a), in addition to a pilot analysis conducted on a sample of recordings to assess the frequency and nature of items which appear in the experiment environment and thus to determine the most suitable target categories’ titles.

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Table 4.3 provides a description and literature justification for the eight target categories. These categories were used to analyse cyclists’ visual behaviour, that is to identify where cyclists look and whether there are differences between critical fixations (either dual or SCR fixations) and all fixations’ pattern.

Fixations’ categorisation was performed by using the semantic mapping function of BeGaze, which enables classifying each fixation to a target category. The software will show two screens where the right screen is showing the scene observed by the participant with the gaze mark on an item, that is where the participant was looking at this moment, while the left screen shows the target categories. The researcher makes a judgment about which category best suits a fixation and clicks on a coded area on the left screen i.e. target category (the area of interest: AOI). This will map each fixation with a target category, see Figure 4.8.

Figure 4. 8 Screenshot showing semantic mapping function two widows of Begaze. The right part of

the screen shows the scene observed by the participant during the trial with the gaze mark (inside red circle). The left part shows the coded area of interests (target categories) where the researcher can link each fixation to a designated target category.

To assess the consistency of the experimenter’s allocation of fixations to different target categories, 17% of all trials were analysed independently by a second person (Dr James Uttley – research associate). When considering allocation to all eight categories, the two coders agreed on 65% of trials. This is below the level of agreement found in other studies (e.g. coding agreement > 90% as found by Foulsham et al., 2011).

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Table 4.3.Description and literature justification of the eight target categories. Target

category

Description Justification

Path Fixations on the road

surface ahead of the bicycle

Previous studies on cyclists have indicated the importance of fixating on the road path

(Mantuano et al., 2017; Vansteenkiste et al., 2014a; Vansteenkiste et al., 2017).

Goal A way finding fixations

above street level

This is essential for fixations related to navigating and planning ahead. This category

appeared in previous research (Fotios et al., 2015b; Vansteenkiste et al., 2013).

Obstacles Any object or irregularity

on the path which may cause an accident if not

detected including small posts

Used before in studies on pedestrians, it is proposed vital for cyclists’ safety on the roads. It is therefore anticipated to influence gaze behaviour (Fotios et al., 2015b).

Kerb Pavement/edge of

footpath

The expectation is that being aware of kerb distance is vital for cyclists to avoid accidents.

Cars Moving, crossing and

stationary cars which participants encounter

on the experimental route

Cars are a major source of cycling accidents on the roads; thus, they are visually important objects in the steering decisions made by cyclists. They are therefore

expected to have an effect on gaze behaviour (Werneke et al., 2015).

Cyclists & pedestrians ¹

Either on shared path in the distance or crossing

the road

Cyclists usually encounter other cyclists and pedestrians while riding; thus, fixations on this category will explain their visual criticality to cyclists (Dozza and Werneke, 2014; Mantuano et al., 2017; Werneke et al., 2015).

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Buildings Fixations on facades of

buildings

Separating buildings in an independent category could be of benefit in explaining how

cyclists observe the built environment (Forsyth and Krizek, 2011). In urban environment

buildings constitute a large portion of surfaces/objects in the participants surround.

Miscellaneous All other objects or

surfaces

All other fixations which do not fit the previous categories (Foulsham et al., 2011).

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General Fixations (miscellaneous) were a notable source of disagreement between the coders. However, when the categories were collapsed into the 3 categories utilised by Foulsham et al. (person, path and miscellaneous), the coder agreement was similar (87%) to that of other studies (Foulsham et al., 2011; Uttley, 2015). This suggests, perhaps unsurprisingly, that more categories lead to less agreement in fixation categorisations between coders.