Effects of the roadside visual environment on driver wellbeing and behaviour – a systematic review

Figures & data

Figure 1. The view from the road: examples.

Figure 2. Scope of the review.

Table 1. Scope of the review.

Visual element		Examples	Included
Road	Geometry	Curves, slopes, junctions, roundabouts, tunnels, medians, lane width	No
	Design	Surface, markings, traffic signs/signals, lamp posts, bus shelters, walls, barriers, pedestrian/cycling infrastructure, tram/trolley infrastructure
	Condition	State of repair/maintenance, light (ambient and from posts), weather
	Users	Moving or parked vehicles, cyclists, pedestrians, glare from other vehicles' headlights
Roadside	Natural	Trees, other vegetation, water bodies, mountains	Yes
	Built	Buildings, industrial structures, advertisements, memorials, public art, utility poles and wires

Figure 3. Document selection process.

Table 2. Search terms.

Concept	Search terms
Subject	driv* AND
Object	(visual OR view OR vision OR visib* OR vista OR sight OR panoram* OR landscape OR landmark OR scenery OR beaut* OR aesthetic* OR roadside) AND
Location	(road* OR highway OR freeway OR expressway OR motorway) AND
Outcome	(wellbeing OR stress* OR anxi* OR anger OR agressi* OR rage OR frustrat* OR arous* OR distract* OR restorati* OR calm* OR relax* OR fatigue OR drowsiness OR sleep* OR bored* OR vigilan* OR alert* OR heart OR physiological OR psychological)

Table 3. Evidence on effects of roadside visual environment on driver wellbeing and behaviour.

Roadside visual environment	Effects
	Fatigue	Stress	Risky behaviour	Distraction
Monotonous views	+
Roadside vegetation		(−)	(−)
Advertisements			(+)	(+)
Other roadside			(+)	(+)

Note: +: positive, (+) mainly positive, depending on several variables, (−) mainly negative, depending on several variables

Appendix 1. Evidence of effects of roadside landscape type.

Authors	Year	Journal^1	Type of road^2	Type of area	Country	Visual element	Placement	Outcome	Data source	Measures	Analysis	Sample size	Sample balance	Findings
EFFECTS ON STRESS
Parsons et al.	1998	JEP	2 lanes	Rural and urban	USA	Forest, golf course, mixed, urban landscape	Next to road and beyond	Stress recovery	Physiological measurement (after video display)	Heart activity, blood pressure, skin conductance, facial activity, task performance	ANOVA	160 participants x 1 video each	All students, mean age 20	Non-urban landscapes: faster recovery from stress
Antonson et al.	2009	JEP	2 lanes, low traffic, 90 km/h	Rural	Sweden	Landscape type (open, forest, varied)	Next to road	Stress	Questionnaire, simulator,	Self-rated stress, speed, lateral position, steering activity	ANOVA	18 drivers x 2 days	Gender-balanced, age 25–65	Open landscape: less self-rated stress, higher speed, more deviation from road centre, more frequent steering wheel grasp
EFFECTS ON SPEED
Yu et al.	2019	AAP	2 lanes, 20–50 km/h	Rural (mountains)	China	Landscape type (trees/plants, mountains, houses/streets, tunnels, open fields)	Next to road and beyond	Performance	Naturalistic driving	Speed	Random forests classification, regression	42 drivers, total 50,000 km	75% male, mean age 33	Highest speed in open fields, then trees/plants, tunnels, mountains, houses. Landscape more important than road geometry
EFFECTS ON FATIGUE
Thiffault and Bergeron	2003	AAP	2 lanes, no curves, no traffic	Rural	Canada	Repetitive vs. scattered objects (trees, houses, farms)	Next to road	Fatigue	Simulator	Steering activity	ANOVA, trend analysis	56 drivers x 40 min. drive x 2	All male, all students, mean age 24	Repetitive objects: more frequent and variable large movements
Larue et al.	2011	AAP	2 lanes	Rural and urban	Australia	Scenery variability	Next to road and beyond	Fatigue, performance	Physiological measurement, simulator	Brain/heart/eye activity, skin conductance, speed, lateral position, steering activity, time to lane crossing	Regression model	25 drivers x 4 scenarios	72% female, age 18–49, all students/ staff	Low scenery variability: decreased vigilance
Wang et al.	2016	TRF	Motorway (4 lanes, low traffic)	Rural	China	Landscape colour	Next to road	Fatigue	Physiological measurement (in naturalistic driving)	Heart rate	Regression model	1 driver x 6 days	1 driver (male, age unknown)	Brighter colours increase heart rate
Ahlström et al.	2018	AAP	Rural: 2 lanes, low traffic, 80 km/h. Suburban: 2/4 lanes, high traffic, 60 km/h	Rural vs. suburban	Sweden	Rural vs. suburban landscapes	Next to road	Sleepiness	Physiological measurement, simulator, questionnaire	Eye activity, speed, pedal activity, steering activity, line crossings, self-rated sleepiness	ANOVA	30 drivers x 6 occasions x 2 scenarios	All male, age 18–25	Sleepiness and driver behaviour less affected by type of landscape than driving task demand

Notes: ¹ Journals: AAP: Accident Analysis and Prevention, JEP: Journal of Environmental Psychology, TRF: Transportation Research F (Traffic Psychology and Behaviour). ² Number of lanes: in both directions. High/medium/low traffic: denotes traffic volume or density. Speed: speed limit.

Appendix 2. Evidence of effects of roadside vegetation.

Authors	Year	Journal^1	Type of road^2	Type of area	Country	Visual element	Placement	Outcome	Data source	Measures	Analysis	Sample size	Sample balance	Findings
EFFECTS ON MENTAL STATES
Cackowski and Nasar	2003	EB	2 lane road vs. 6 lane motorways, low/medium traffic	Suburban/ rural	USA	Amount of vegetation	Next to road and beyond	Stress recovery, anger, frustration	Questionnaire (with video display)	Self-rated anger, frustration exercise	Before-after	106 participants x 1 video	All students, 63% male, mean age 25	More vegetation:more frustration tolerance, no effect on anger
Antonson et al.	2015	JEP	2 lanes	Rural	Sweden	Vegetation density	Next to road and beyond	Stress, performance	Physiological measurement, simulator, eye tracker, questionnaire	Heart rate, speed, brake response, lateral position, gaze, self-reported stress (yes/no)	ANOVA	25 drivers x 8 road stretches x 9 km	Gender-balanced, age 30–50	Denser vegetation: more self-rated stress, less speed reduction when facing hazard, no effect on heart rate, speed mean/variability, lateral position, gaze
Jiang et al.	2021a	EB	Motorway (6 lanes)	Urban	Hong Kong	Vegetation type and regularity	3.3–5 m from road	Mental states	Questionnaire	Self-rated boredom, anger, frustration, avoidance, anxiety, tension, fatigue	ANOVA	33 drivers x 6 scenarios x 90 min	64% men, age 18–60, mean 28, students/ staff	Mental states improve with greenness (tree vs. shrub vs. turf vs. barren) and regularity of vegetation
Stamatiadis et al.	2010	TRR	2 lanes	Rural	USA	Vegetation type and height	Next to road	Discomfort	Online questionnaire (with video display)	Self-rated discomfort	Non-linear modelling	170 participants x 50 scenarios	Unknown	Greenness (trees vs. tall plants vs. low plants vs. grass) plus barriers: more discomfort
EFFECTS ON PERFORMANCE
Van der Horst and De Ridder	2007	TRR	80 km/h	Rural	Netherlands	Trees (presence/ distance from road)	2 or 4.5 m from road	Performance	Simulator	Speed, lateral position	ANOVA	20 drivers x 24 scenarios	Gender-balanced, age 29–53, mean 46	Trees: shift to road centre (when road has barriers). Near trees: lower speed.
Fitzpatrick et al.	2014	JSR	2 lanes	Rural and urban	USA	Tree density and distance from road	1.5, 1.5–4.6, or 4.6+ m from road	Performance	Observation, questionnaire	Speed, lateral position, preferred speed	Statistical tests	100 participants x 13 videos	Unknown	Near or dense trees: lower speeds. Dense trees: shift to road centre
Calvi	2015	TRR	2 lanes	Rural	Italy	Tree spacing and distance from road	1.5 or 4 m from road	Performance	Simulator	Speed, lateral position	ANOVA	38 drivers x 2 scenarios	All students/ staff, 68% men	Less spacing: shift to centre, same speed. Near trees: lower speed, shift to road centre.
Fitzpatrick et al.	2016	TRF	2 lanes, low traffic	Rural	USA	Tree density and distance from road	0, 2, or 6 m from road	Performance	Simulator, eye tracker	Speed, lateral position, gaze (at speedometer)	Paired sample comparison	24 drivers x 6 scenarios	Gender-balanced., age: 21–38, mean 26	Near trees: lower speed, shift to centre. Density: no impact on speed or lateral position
Zheng et al.	2018	TIP	2 lanes, 80 km/h	Rural	China	Enclosure by trees	Next to road	Performance	Simulator	Speed, reaction time	Regression models	44 drivers x 24 or 8 scenarios	61% men, age 20–54, mean 34	Enclosure: shorter reaction time. On straight roads: less speed
Jiang et al.	2021b	UFUG	Motorway (6 lanes)	Urban	Hong Kong	Vegetation type and regularity	3.3–5 m from road	Performance	Simulator, questionnaire	Speed, lateral acceleration, steering, self-rated performance	ANOVA	33 drivers x 6 scenarios x 90 min	64% men, 18–60, mean 28, students/ staff	Better driving performance in shrub landscapes than tree/turf/barren ones

Notes: ¹ Journals: EB: Environment and Behaviour, JEP: Journal of Environmental Psychology, JSR: Journal of Safety Research, TIP: Traffic Injury Prevention., TRF: Transportation Research F (Traffic Psychology and Behaviour), TRR: Transportation Research Record, UFUG: Urban Forestry & Urban Greening ² Number of lanes: in both directions. High/medium/low traffic: denotes traffic volume or density. Speed: speed limit.

Appendix 3. Evidence of effects of roadside advertisements

References	Year	Journal^1	Type of road^2	Type of area	Country	Visual element	Placement	Outcome	Data source	Measures	Analysis	Sample size	Sample balance	Findings
UNSPECIFIED TYPE
Horberry et al.	2006	AAP	2 lanes, 60–80 km/h	Unknown	Australia	Advertisements (unknown type)	Unknown	Distraction, performance	Simulator, questionnaire	Speed, performance in in-vehicle distraction task, self-rated workload	t-test, ANOVA	31 drivers x 2 scenarios x 6 km	Gender unknown, age <75	More ads: lower speed (older drivers). No effect on distraction or self-rated workload
Young et al.	2009	TRF	Motorway (112 km/h) vs. urban road (80 km/h) vs. rural (48 km/h)	Urban vs. rural	UK	Billboards (unknown type)	Unknown	Distraction, performance	Simulator, eye tracker, questionnaire	Time out of lane, lane drifting, time to contact, gaze, crashes, recall exercise	ANOVA	48 drivers x 6 scenarios	60% male, mean age 33	Bilboards: more time out of lane, lane drifting, glances. No interactions with road type/area.
Bendak and Al-Saleh	2010	IJIE	3 lanes, medium traffic	Unknown	Saudi Arabia	Advertisements (unknown type)	Unknown	Performance	Simulator	Tailgating, speeding, drifting from lane, not signalling, junction crossing	t-test	12 drivers x 2 scenarios	All male, age 23–28, university degree	Ads: lane drifting, risky junction crossing. Other risky behaviours insignificant.
SPECIFIC TYPE
Young et al.	2017	AE	Motorway, busy urban retail road, busy arterial; 40–100 km/h	Urban	Australia	Static billboards	Unknown	Situation awareness	Naturalistic driving	Verbal protocols	Network analysis	19 drivers x 38 km	63% male, age 22–47, mean 31	Glances to billboards when low traffic volume/speed or stopped, not when manoeuvres
Mollu et al.	2018	TRF	2 lanes, with cycle track, 50–70 km/h	Rural, urban	Belgium	Electronic billboards with movable display	Elevated, various distances from road	Distraction, performance	Simulator, eye tracker, questionnaire	Gaze, speed, reaction time, lateral position, self-rated workload	ANOVA	35 drivers x 7 scenarios (3.8–5.2 km each)	Gender-balanced, age 22–66, mean = 39	Billboards: longer reaction time. Short display, retail areas: more glances. No distance effect.
Belyusar et al.	2016	AAP	Motorway, 8 lanes, 105 km/h	Suburban	USA	Electronic billboards with movable display	Road verge, elevated	Distraction	Naturalistic driving, eye tracker	Gaze, speed braking, steering, accelerating activity	ANOVA	123 drivers x 2 h (2 billboards)	Gender-balanced, age 20–69.	Billboards: more and longer glances, especially older drivers. Performance impact depends on road and traffic
Shaw et al.	2019	TRF	2 lanes	Rural	USA	Electronic billboards, movable, vs. other distractors	Near but not next to road	Distraction, Performance	Simulator	Speed, lateral position, driving errors, reaction time	ANOVA	46 drivers x 2 scenarios x 15 min	76% women, 94% 18–24 students	Longer reaction time for billboards than police cars, work zones
Harms et al.	2019	TRF	Motorway, 4 lanes	Unknown	Netherlands	Advertisements on traffic management variable signs	Elevated	Performance	Simulator	Follow the displayed safety instructions, speed reduction		32 drivers x 10 scenarios (total 90 min)	Gender-balanced, age 23–62, mean 32	Ads on variable signs: no effect on following safety instructions, smoother speed reduction
COMPARED TYPES
Beijer et al.	2004	TRR	Raised 6-lane motorway, high traffic, 80 km/h	Urban	Canada	Static vs. movable display billboards	Offset from road, raised	Distraction	Naturalistic driving, eye tracker	Gaze	ANOVA	25 drivers x 6 km (37 billboards)	Gender unknown, age 25–50	More/longer glances at billboards with movable displays
Smiley et al.	2004	TRR	Road intersections in central areas, motorway	Urban	Canada	Static vs. movable display electronic billboards	On footway or 130 m from road, raised	Distraction	Naturalistic driving, eye tracker	Gaze	Descriptives, statistical tests	16 drivers x 30–50 min (4 billboards)	Gender unknown, age 25–50, mean 41	More glances made at short headways to static billboards
Edquist et al.	2011	AE	6 lanes, with median, 70 km/h	Commercial and industrial	Australia	Static vs. movable display billboards	Unknown	Distraction, performance	Simulator, head/eye tracker	Reaction time to traffic signs, errors, head/eye movement	ANOVA	48 drivers x 2 scenarios x 9 km	63% male, 3 age groups	Billboards: distractions. Errors and delayed reaction (especially older drivers). Movable vs. static: no difference
Dukic et al.	2013	TIP	Motorway, 6 lanes, high traffic, > = 50 km/h	Urban	Sweden	Static vs. movable display electronic billboards	Road verge, elevated	Distraction, performance	Naturalistic driving, eye tracker	Gaze, speed, lateral position, headway	ANOVA	41 drivers x 40 km	Gender unknown, age 35–55, mean 42	Billboards: no effect on performance. More/longer glances to electronic billboards.
Stavrinos et al.	2016	TRF	4 lanes, with median, 65mph	Unknown	USA	Static vs. movable display electronic billboards	Offset from road, elevated	Distraction	Simulator, eye tracker	Gaze	ANCOVA	54 drivers x 26 km (16 billboards)	Gender-balanced, 3 age groups	Teenagers: more/ longer glances. Longer glances when long transition times (teenagers) or static (older drivers)
Costa et al.	2019	AE	2 lanes, 198 vehicles/day, 50 km/h (urban), 90 km/h (rural)	Rural (47%) vs. urban (53%)	Italy	Types of advertisements	Various heights/ distances from road	Distraction	Naturalistic driving, eye tracker	Gaze	ANOVA, regression	15 drivers x 30 km (154 advertisements)	2/3 male, mean age 26	More glances at static billboards, then gas price LED signs, directions, vendor signs, display boards. Longer glances in rural areas and to lower advertisements
Brome et al.	2021	TRF	4 lanes	Urban	Lebanon	Static vs. transitioning vs. animated electronic billboards	On footway, raised, every 400m	Distraction, performance	Simulator, eye tracker, physiological measurement	Speed, acceleration, lateral position, reaction time, gaze, brain activity	ANOVA	100 drivers x 15–20 min (27 billboards)	Gender-balanced, mean age 23, all students	Static billboards: fewer/shorter glances, less deviation from centre, shorter reaction time. No effect on speed or acceleration
COMPARED CHARACTERISTICS
Crundall et al.	2006	AAP	Street	Urban	UK	Placement of advertisement	On footway, raised 3 m vs. street level (bus shelter)	Distraction	Eye tracker (during video display)	Gaze	ANOVA	32 participants x24 videos	Gender-balanced, mean age 29	Street level ads: more glances when facing possible hazards, less when looking for ads
Megías et al.	2011	SS	Streets	Urban	Spain	Emotional content (static billboards)	Centre of road, raised	Distraction, safety behaviour	Simulator (motorcycle), eye tracker	Gaze, reaction time	ANOVA	22 drivers (motorcycle) x12 scenarios	Gender-balanced, age 18–25, all students	Emotional content: more glances. Negative emotional: shorter reaction time
Chan and Singhal	2013	AAP	2 lanes	Rural	Canada	Emotional content (static billboards)	Next to road, every 200m	Distraction, performance	Simulator	Speed, lateral position, steering activity, response times, memory recall	ANOVA	30 drivers x 4 scenarios	Gender unknown, mean age 21, all students	Emotional content (especially positive): lower speed. Negative content: better memory recall.
Zalesinska	2018	AAP	2 lanes	Urban	Poland	Angle, size, luminance (electronic billboards, movable)	Real billboard in laboratory	Distraction	Simulator	Reaction time to hazard	ANOVA	175 drivers x 3 scenarios	Gender unknown, mean age 23	Reaction time decreases with angle to the road and increases with size and luminance
Walker and Trick	2019	SS	2 lanes with median	Unknown	Canada	Emotional content (billboards)	33 m from road, every 1.2 km	Distraction, Performance	Simulator	Reaction time, steering activity, speed	ANOVA	23 drivers x 4 scenarios x 15 min	Gender-balanced, all students	Emotional content: no impact on speed. Negative content: longer response time
Maliszewski et al.	2019	PLOS ONE	3 lanes, 60 km/h	Rural	Poland	Sexual appeal (billboards)	40 m from road	Distraction, Performance	Simulator	Reaction time, speed	t-test	55 drivers x 8 min (8 billboards)	Gender balanced, age 18–64, mean 39	Sexual appeal: Lower and more variable speed, longer reaction time. No age or gender effect.
Marciano	2020	AAP	4 lanes	Suburban	Israel	Design (text and graphics) (static billboards)	Median strip	Performance	Simulator	Collisions, reaction distance	ANOVA	18 drivers x 6 scenarios x 28 km	Gender-balanced, age 22–40, mean 27, all students	Billboards with graphic elements and little text are the safest
Meuleners et al.	2020	AAP	Motorway, 6 lanes, 100 km/h	Unknown	Australia	Placement, complexity, display time (electronic billboards)	Side of road or bridge (overhead)	Distraction, performance	Simulator, eye tracker	Speed, lateral position, headway, gaze	ANOVA	96 drivers x 10–15 min (3 billboards)	Gender unknown, age 18–76	Billboards: lower speed, more variable speed/lateral position, risky headways, glances. Depends on billboard characteristics.
Sheykhfard and Haghighi	2020	JSF	Major roads, links vs. junctions	Urban	Iran	Location and sight distance (electronic billboards)	Unknown	Distraction	Naturalistic driving	Distraction (yes/no)	Structural equation model	78 drivers x 27 km	Gender-balanced, age 18–65	Probability of distraction higher at junctions and long sight distances
Harasimczuk et al.	2021	CP	Unknown	Unknown	Poland	Content	2.5 m from driver's view	Performance	Simulator	Distance and headway time to vehicle in front	t-tests	53 drivers x unknown length/scenarios	Gender balanced, 3 age groups, mean 39	Longer slogans: more variable distance and time headway to vehicle in front.

Notes: ¹ Journals: AAP: Accident Analysis and Prevention, AE: Applied Ergonomics, CP: Current Psychology, IJIE: International Journal of Industrial Ergonomics, JSF: Journal of Safety Research, SS: Safety Science, TIP: Traffic Injury Prevention, TRF: Transportation Research F (Traffic Psychology and Behaviour), TRR: Transportation Research Record. ² Number of lanes: in both directions. High/medium/low traffic: denotes traffic volume or density. Speed: speed limit

Appendix 4. Evidence of effects of roadside objects

References	Year	Journal^1	Type of road^2	Type of area	Country	Visual element	Placement	Outcome	Data source	Measures	Analysis	Sample size	Sample balance	Findings
COMPARED OBJECTS
Antonson et al.	2014	AAP	2 lanes, low traffic, 90 km/h	Rural	Sweden	Various objects, old and modern	Various distances from road	Stress, distraction, performance	Physiological measurement, questionnaire, eye tracker, simulator	Heart rates, self-rated stress, gaze, speed, lateral position	ANOVA (type of object x distance)	18 drivers x 3 road stretches	Gender-balanced, age: 30–45	Objects: no stress, reduced speed. Far objects: longer gaze. Close objects: speed variability, shift to road centre. No differences old vs. modern
ROADSIDE MEMORIALS
Tay	2009	AAP	Junctions, 4-lanes	Urban	Canada	Roadside memorials	On lamp posts next to road	Safety behaviour	Observation	Red light violations	Experiment, before-after comparison	∼43,000 vehicles (treatment), ∼23,000 (comparison)	Not applicable	Fewer red light violations
Tay et al.	2011	AAP	Motorway (4 lanes 110 km/h)	Rural vs. urban	Canada	Roadside memorials	16 m from road	Safety behaviour	Observation	Speed, vehicle headways	Experiment, before-after comparison	∼39,000 speed and headway observations	Not applicable	No effect
Tay	2018	TRF	4-lane	Unknown	Australia	Roadside memorials	On lamp posts next to road	Safety behaviour	Questionnaire	Red light violation intentions	Regression model	949 participants x 2 photos each	Gender and age balanced	Fewer red light violation intention by older people, middle-aged women, and those who feel sad/sympathetic or worried/concerned
Beanland and Wynne	2019	CR	60–110 km/h	Unknown	Australia	Roadside memorials	Unknown	Distraction safety behaviour	Eye tracker, questionnaire (with video display)	Gaze, preferred speed	Regression model	40 participants x 40 short videos	73% female, age 20–44	Only brief distractions, no effect on preferred speed
DRONES
Barlow et al.	2019	TRC	56 km/h. Rural: 2 lanes, Urban: 4 lanes and footway	Rural vs. urban	USA	Drones	Various distances from road (0–15.2 m)	Distraction	Eye tracker (in simulator)	Gaze	ANOVA, regression model	30 drivers x 18 scenarios each	Gender-balanced, age 18–75, mean 29	Glances, especially when adjacent to road and in rural area.
Ryan et al.	2020	TRF	2 lanes, 56 km/h	Rural	USA	Drones	Next to road, 20, 40, or 60 ft high	Distraction	Simulator, eye tracker	Speed, lateral movement, gaze	Statistical tests	29 drivers x 8 scenarios x 2 min	Gender-balanced, age 18–75, 64% 18–24	Glances. Little impact on speed or lateral position. Age, gender, drone height insignificant.
WIND TURBINES
De Ceunynck et al.	2017	EJTIR	Motorway (4 lanes)	Suburban	Netherlands	Wind turbines	26 m away from road	Distraction	Observation	Speed, lateral position, traffic conflicts	Before-after comparison	797,341 (speed), 3649 (video)	Not applicable	Lower speed. Shift to road centre. Slightly more variable speed and lateral position. No traffic conflicts
UTILITY POLES
Fitzpatrick et al.	2014	JSR	2 lanes	Rural	USA	Utility poles	1.2 m from road	Safety behaviour	Questionnaire	Preferred speed	Paired t-test	100 drivers x 2 videos	Unknown	Reduced speed

Notes: ¹ Journals: AAP: Accident Analysis and Prevention, CR: Cognitive Research: Principles and Implications, EJTIR: European Journal of Transport and Infrastructure Research, JSR: Journal of Safety Research, TRC: Transportation Research Part C (Emerging Technologies), TRF: Transportation Research F (Traffic Psychology and Behaviour) ² Number of lanes: in both directions. High/medium/low traffic: denotes traffic volume or density. Speed: speed limit