The influence of cycle lanes on road users’ perception of road space

Figures & data

Table 1. Research methods used to address visual perception or urban elements.

Method	Description	Advantages	Disadvantages	Examples
Observational	These involve researchers observing and recording participants’ responses to various road elements and urban infrastructure.	real-world setting, direct observation, large sample size, cost-effectiveness	lack of control, ethical concerns, observer bias, limited information.	(Tilt et al., 2007); Straatemeier et al. (2010); (Chamseddine & Ait Boubkr, 2020)
Experimental	These involve manipulating the road elements and urban infrastructure to investigate their impact on visual perception and measuring participants’ responses.	experimental control, establishment of causal relationships, precise measurement of effects.	artificial setting, demand characteristics, potential ethical concerns, limited generalizability.	(Fossum & Ryeng, 2022; Hull & O’holleran, 2014)
Survey research	These involve administering questionnaires or interviews to participants to obtain their perceptions and opinions about road elements and urban infrastructure.	ease of administration, cost-effectiveness, large sample size, access to participant perceptions and opinions.	potential for response bias, limited insight into underlying mechanisms, reliance on self-report measures, lack of experimental control.	(Perovic & Folic, 2012; Rosso et al., 2016)
Simulation	These involve using computer simulations or virtual reality technology to create realistic driving scenarios and investigate the impact of road elements and urban infrastructure on visual perception.	control over road elements and urban infrastructure, ability to create realistic scenarios, precise measurement of effects.	potential for artificiality, lack of ecological validity, potential for demand characteristics, expense of equipment.	(C. J. Wilson & Soranzo, 2015; Jonescu et al., 2020; Rybarczyk, 2014)
Eye-tracking studies	These involve using specialized equipment to track participants’ eye movements and measure their visual attention to road elements and urban infrastructure.	precise measurement of visual attention, direct measurement of eye movements, high temporal resolution, access to unconscious processes	potential for artificiality, expense of equipment, lack of ecological validity, limited insight into underlying mechanisms.	(Gholami et al., 2021; Lisińska-Kuśnierz & Krupa, 2020)
Neuroimaging	These involve using brain imaging techniques, such as fMRI or EEG, to investigate the neural processes underlying visual perception of road elements and urban infrastructure.	access to underlying neural processes, ability to investigate complex relationships, high spatial resolution.	expense of equipment, artificiality of tasks, limited ecological validity, potential for demand characteristics.	(Bolouki, 2022; Tang et al., 2017)

Figure 1. Flow diagram of the methodology.

Table 2. Survey design outline.

SECTION 1 Visual perception	1) Visual perception of road space:
	- Set a) ‘Without taking out one lane (either parking or traffic lane). Is there space for a cycle lane?’ (Images: 1 fixed + 3 random of a batch of 6)
	- Set b) ‘Taking out one lane (either parking or traffic lane). Is there space for a cycle lane?’ (Images: 1 fixed + 3 random of a batch of 5)
	2) Visual perception of road elements:
	- Two images (road elements: footpath, cycle lane, lane, car)
SECTION 2 Socio-demographic characteristics	Country of residence
	Age
	Gender
	Travel mode of commute (or most usual trip)
	Education
	Occupation
	Income
	Access to car
	Driver license

Figure 2. Example of the question presented in the online survey. (Image source: Google Maps).

Figure 3. Age distribution by country.

Table 3. Sample description (source: Loyola et al. (2022)).

Characteristics	Categories	Country (per cent per country)			Total (n)	% of total
		Australia	The Netherlands	The UK
Gender	Female	56	56	63	943	59
	Male	42	41	36	615	39
	Non-binary	2	1	1	16	1
	Prefer not to say	1	2	1	17	1
Age	(continuous)				1591	100
Travel mode	Private vehicle	79	58	75	1138	72
	Bicycle	6	26	3	147	9
	Walk	5	5	11	130	8
	Bus	2	1	5	50	3
	Train	4	2	2	40	3
	Other	3	4	3	50	3
	Bus + train or Train + bus	3	2	1	26	2
	Ferry (+ bus, train, or other)	0	0	0	2	0
	Micromobility (e-scooter, e-bike, etc.)	0	1	0	5	0
	Taxi (Uber, OLA, Didi, etc)	0	0	0	3	0
Education	Degree	66	55	59	950	60
	High school	26	36	31	498	31
	Advanced degree	8	9	10	143	9
Occupation	Retired	41	21	47	622	39
	Work full-time	29	31	25	433	27
	Work part-time	16	15	14	234	15
	Other	4	21	7	156	10
	Self-employed	7	10	7	117	7
	Student	4	2	1	29	2
Income	(continuous)				1591	100
Access to car	Yes	97	87	92	1468	92
	No	3	13		123	8
Driver license	Yes	98	91	94	1496	94
	No	2	9	6	95	6
Lottery risk aversion	No risk	51	69	72	1051	66
	Risk-seeking	15	18	16	255	16
	Risk-neutral	28	8	7	196	12
	Risk-avoiding	7	5	5	89	6

Figure 4. Diagram flow for the analysis of the visual perception of road space (sub-sections in brackets).

Figure 5. Average recognition of having space for cycle lanes.

Table 4. Univariate logistic regression of visual perception of road space.

Parameter [p-value]		Estimate	SE	OR	OR 95% CI
Intercept		0.01	0.07	1.01	0.88	1.16
Country [0.001]	UK			1.00
	Australia	0.08	0.12	1.08	0.85	1.38
	Netherlands	0.56	0.13	1.75	1.37	2.25
Intercept		0.88	0.26	2.41	1.45	4.01
Age [0.005]	(continuous)	−0.01	0.00	0.99	0.98	1.00
(Intercept)		0.30	0.08	1.35	1.15	1.58
Gender [0.048]	Male			1.00
	Female	−0.21	0.10	0.81	0.66	1.00
Intercept		0.52	0.22	1.69	1.09	2.61
Travel mode [0.019]	Other			1.00
	Private car	−0.39	0.23	0.68	0.43	1.07
	Bus	−0.85	0.36	0.43	0.21	0.87
	Bicycle	0.05	0.28	1.05	0.61	1.83
	Train	−0.42	0.39	0.65	0.31	1.40
	Walk	−0.62	0.28	0.54	0.31	0.94
Intercept		0.24	0.17	1.27	0.91	1.77
education [0.007]	Advanced degree			1.00
	High school	−0.31	0.19	0.73	0.50	1.06
	Degree	0.04	0.18	1.04	0.73	1.48
Intercept		0.38	0.10	1.46	1.21	1.77
Occupation [0.114]	Work full-time			1.00
	Work part-time	−0.16	0.16	0.86	0.62	1.18
	Student	−0.17	0.39	0.84	0.40	1.80
	Self-employed	−0.22	0.21	0.80	0.53	1.21
	Retired	−0.34	0.13	0.71	0.56	0.91
	Other	−0.40	0.19	0.67	0.46	0.96
Intercept		−0.03	0.10	0.97	0.81	1.17
Income [0.011]	(continuous)	0.00	0.00	1.00	1.00	1.00
Intercept		0.17	0.05	1.19	1.07	1.32
Access to car [0.398]	Yes			1.00
	No	−0.16	0.19	0.85	0.59	1.23
Intercept		0.17	0.05	1.18	1.07	1.31
Driver license [0.774]	Yes			1.00
	No	−0.06	0.21	0.94	0.62	1.43
Intercept		0.16	0.06	1.18	1.04	1.33
Risk aversion [0.970]	No risk			1.00
	Risk seeking	0.02	0.14	1.02	0.77	1.34
	Risk neutral	0.02	0.16	1.02	0.75	1.39
	Risk avoiding	−0.09	0.22	0.91	0.59	1.40

Table 5. Results: binary logistic regression.

Parameter [p-value]		B	S.E.	Sig.	OR	OR 95% C.I.
Country [< 0.001]	Australia				1.00
	Netherlands	0.51	0.15	0.001	1.66	1.24	2.23
	UK	0.00	0.13	0.991	1.00	0.78	1.28
Age [0.018]	(continuous)	−0.01	0.00	0.018	0.99	0.98	1.00
Gender [0.025]	Female				1.00
	Male	0.24	0.11	0.025	1.27	1.03	1.57
Education [0.003]	High School				1.00
	Degree	0.39	0.11	0.001	1.48	1.18	1.85
	Advanced degree	0.32	0.20	0.106	1.37	0.93	2.02
	Constant	0.33	0.30	0.268	1.39

Table 6. Average measures (metres) of road elements from the presented images.

Road element	The United Kingdom		Australia		The Netherlands
	Average measure (metres)	Standard deviation	Average measure (metres)	Standard deviation	Average measure (metres)	Standard deviation
Lane (photo 1)	2.14	0.60	2.00	0.60	2.08	0.81
Car (photo 1)	1.72	0.56	1.56	0.40	1.74	0.68
Cycle lane (photo 1)	1.08	0.41	0.99	0.38	0.98	0.39
Footpath (photo 1)	1.37	0.62	1.30	0.55	1.15	0.66
Lane (photo 2)	1.87	0.35	1.80	0.45	1.78	0.45
Car (photo 2)	1.63	0.56	1.49	0.41	1.69	0.66
Footpath (photo 2)	1.34	0.46	1.28	0.49	1.32	0.56

Figure A1. Images of the streets presented to the respondents.

Figure A2. Images 1 and 2 (descending order) presented to respondents to estimate other road elements (section 4.3).

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