Understanding the value of autonomous vehicles – an empirical meta-synthesis

Figures & data

Figure 1. Summary of the comprehensive search procedure (source: authors analysis).

Figure 2. Box plot distribution of the estimated VOT for different types of vehicle.

Table 1. Difference in VOT (in %) among vehicle categories (row minus column) .

Note: Colour code indicates the significance criterion,  = significant,  = not significant.

Table 2. Variation of mean VOT (AU$/hr) between vehicle types and different variables.

Variable	Category	Private AV (PAV)			Shared AV (SAV)			CAR
		N	Mean	Std. Dev.	N	Mean	Std. Dev.	N	Mean	Std. Dev.
Country	Australia	5	9.0	8.9	–	–	–	4	15.6	8.5
	USA	7	18.8	16.5	10	28.6	14.2	9	27.1	17.9
	Germany	42	12.2	5.8	35	10.8	2.6	16	16.6	9.9
	Netherlands	25	11.0	3.0	–	–	–	25	11.8	1.0
	Europe	–	–	–	8	45.4	28.6	2	51.9	40.6
	Asia	2	12.1	0.8	4	18.6	7.3	–	–	–
Geographical location	Rural	26	11.0	3.0	–	–	–	26	11.9	1.0
	Urban	10	14.0	15.8	7	25.5	16.9	9	23.6	19.4
AV demonstration	AV written	25	14.1	9.5	17	34.7	24.4	–	–	–
	Chauffeur written	13	8.9	2.5	2	23.1	7.4	–	–	–
	Video	43	11.9	5.9	39	12.6	6.2	–	–	–
Sample size	Category 1	30	12.1	6.4	25	12.0	8.2	3	22.1	19.8
	Category 2	34	10.5	3.1	7	16.9	13.2	34	13.0	3.6
	Category 3	8	10.4	6.0	11	23.6	18.9	5	13.1	8.9
	Category 4	8	21.1	14.3	14	31.0	23.4	12	30.4	22.3
Sample characteristics	Trader	80	12.2	7.0	45	13.6	9.5	52	15.7	10.4
	Non-trader	–	–	–	13	39.7	22.4	4	38.3	28.9
Trip type	Commute	67	11.6	6.5	42	15.0	10.4	40	15.0	10.6
	Non-commute	14	14.5	8.9	16	31.0	25.2	16	23.1	17.9
Time of day	AM peak	70	11.3	5.0	38	12.1	5.5	42	13.6	6.6
	Others	9	19.3	14.4	18	35.6	22.9	14	28.7	21.0
Trip distance	<20 km	47	11.9	5.8	39	12.0	4.6	19	17.3	9.2
	>20 km	5	9.4	4.9	4	15.9	5.1	7	15.8	6.7
Trip duration	<30 min	31	10.8	3.2	8	18.5	11.4	33	13.3	4.7
	>30 min	11	14.7	14.9	7	23.4	17.7	9	23.8	19.2
Gender	Male	12	13.6	2.8	12	9.8	1.7	–	–	–
	Female	12	11.3	2.7	12	11.0	1.8	–	–	–
Age	<18 years	–	–	–	8	48.1	24.8	–	–	–
	Young adult	8	10.9	2.7	8	9.0	1.5	–	–	–
	Middle aged	8	13.2	2.9	8	11.1	1.7	–	–	–
	Seniors	8	13.3	2.9	8	11.2	1.7	–	–	–
Education	<uni deg	12	10.3	1.9	12	9.1	1.2	–	–	–
	=>uni deg	12	14.6	2.1	12	11.7	1.4	–	–	–
Income	Low	7	7.0	4.4	4	11.3	5.7	6	10.5	5.2
	Medium	7	10.0	6.7	3	11.3	1.5	6	16.4	8.1
	High	5	17.4	12.1	4	19.5	5.2	5	23.0	12.9
License	No	12	14.0	14.4	18	34.4	23.7	13	26.3	23.0
	Yes	69	11.7	4.9	40	12.7	6.2	43	14.6	7.2

Table 3. Statistical t-test results of the mean VOT difference between different variables and their individual categories.

Variable	Category	Pair 1 vs Pair 2	Mean difference [pair 2 – pair 1]		t-value	Sig. (p)
			AU$/hr	%
Country	PAV	USA vs Germany	−6.6	−35%	2.024	0.049
		USA vs Netherlands	−7.7	−41%	2.298	0.029
	SAV	USA vs Germany	−17.9	−63%	7.204	<0.001
	Car	USA vs Netherlands	−15.2	−56%	4.358	<0.001
	Germany	Car vs PAV	−4.4	−27%	−2.099	0.040
		Car vs SAV	−5.8	−35%	−3.262	0.002
Geographical location	Car	Urban vs Rural	−11.7	−50%	−3.152	0.003
	PAV	Urban vs Rural	−3.0	−21%	−0.942	0.353
	Urban	Car vs PAV	−9.6	−41%	−1.171	0.130
		SAV vs PAV	−11.5	−45%	−1.414	0.091
AV demonstration	AV written	SAV vs PAV	−20.7	−60%	−3.848	<0.001
	Video	SAV vs PAV	−0.7	−6%	−0.507	0.614
	SAV	AV written vs Video	−22.1	−64%	5.348	<0.001
	PAV	AV written vs Video	−2.2	−16%	1.030	0.310
		AV written vs Chauffeur written	−5.1	−36%	2.519	0.017
		Video vs Chauffeur written	−2.9	−24%	2.556	0.014
Sample size	PAV	Category 4 vs Category 1	−9.0	−43%	−2.669	0.011
		Category 4 vs Category 2	−10.6	−50%	−4.114	<0.001
		Category 4 vs Category 3	−10.7	−51%	−1.954	0.081
	SAV	Category 4 vs Category 1	−19.0	−61%	−3.709	<0.001
		Category 4 vs Category 2	−14.1	−45%	−1.763	0.094
		Category 4 vs Category 3	−7.5	−24%	−0.885	0.385
	Car	Category 4 vs Category 2	−17.4	−57%	−4.465	<0.001
Sample characteristics	SAV	Non-trader vs Trader	−26.1	−66%	−6.185	<0.001
Trip type	Car	Non-commute vs Commute	−8.1	−35%	−1.682	0.109
	SAV	Non-commute vs Commute	−15.9	−51%	−3.437	0.001
	PAV	Non-commute vs Commute	−2.9	−20%	−1.137	0.272
	Commute	SAV vs PAV	−3.4	−23%	−2.132	0.035
		Car vs PAV	−3.4	−23%	−1.860	0.068
	Non-commute	SAV vs PAV	−16.5	−53%	−2.319	0.028
		Car vs PAV	−8.6	−37%	−1.697	0.103
		SAV vs Car	−7.9	−25%	1.018	0.318
Time of day	Car	Others vs AM peak	−15.1	−53%	−4.153	<0.001
	SAV	Others vs AM peak	−23.5	−66%	−6.022	<0.001
	PAV	Others vs AM peak	−8.0	−41%	−3.417	0.001
	Others	SAV vs PAV	−16.3	−46%	−2.250	0.034
		Car vs PAV	−9.4	−33%	−1.267	0.219
		SAV vs Car	−6.9	−19%	0.885	0.384
Trip distance	<20 km	Car vs PAV	−5.3	−31%	−2.824	0.006
		Car vs SAV	−5.3	−31%	−2.929	0.005
	>20 km	Car vs PAV	−6.4	−41%	−1.902	0.043
		SAV vs PAV	−6.5	−41%	−1.922	0.025
	PAV	<20 km vs >20 km	−2.5	−21%	1.072	0.165
	SAV	>20 km vs <20 km	−3.9	−25%	−1.469	0.112
Trip duration	<30 min	SAV vs PAV	−7.7	−42%	−3.425	0.002
		Car vs PAV	−2.5	−19%	−2.490	0.016
		SAV vs Car	−5.3	−29%	2.078	0.044
	>30 min	SAV vs PAV	−8.7	−37%	−1.079	0.303
		Car vs PAV	−9.1	−38%	−1.159	0.265
	PAV	>30 min vs <30 min	−3.9	−27%	−1.411	0.083
	SAV	>30 min vs <30 min	−4.9	−21%	−0.623	0.274
	Car	>30 min vs <30 min	−10.5	−44%	−2.917	0.006
Gender	Male	PAV vs SAV	−3.8	−28%	4.053	<.001
Age	Young adult	PAV vs SAV	−1.9	−17%	1.707	.116
	Middle aged	PAV vs SAV	−2.1	−16%	1.747	.108
	Seniors	PAV vs SAV	−2.1	−16%	1.780	.102
	PAV	Seniors vs Young adult	−2.4	−18%	−1.738	.104
	SAV	Seniors vs Young adult	−2.2	−20%	−2.732	.016
Education	<uni deg	PAV vs SAV	−1.2	−12%	1.834	.083
	=>uni deg	PAV vs SAV	−2.8	−19%	3.834	.001
	PAV	(=>uni deg) vs (<uni deg)	−4.2	−29%	−5.077	<.001
	SAV	(=>uni deg) vs (<uni deg)	−2.6	−22%	−4.931	<.001
Income	PAV	High vs Low	−10.4	−60%	−1.831	.065
	SAV	High vs Low	−8.1	−42%	−2.115	.040
	Car	High vs Low	−12.5	−54%	−2.036	.048
License	Car	No vs Yes	−11.7	−44%	2.940	.005
	SAV	No vs Yes	−21.7	−63%	5.453	<.001
	PAV	No vs Yes	−2.3	−16%	1.035	.304
	No	Car vs PAV	−12.3	−47%	−1.616	.061
		SAV vs Car	−8.1	−24%	0.952	.350
		SAV vs PAV	−20.4	−59%	−2.932	.007

Figure 3. VOT distribution classed by AV demonstration approach.

Table 4. Factors influencing the VOT estimates for AV travel.

Variables	Comments
Vehicle type	VOT will be lowest for private AV.
Country	VOT is found to be lower in Germany and the Netherlands then Australia and the US.
Geographical location (rural vs. urban)	AVs will be popular to urban residents.
AV demonstration approach	VOT will be lower when showing animation video.
Sample size	VOT will be higher for sample size of more than 500.
Sample characteristics	VOT will be lower if the responses from the “non-traders” are excluded.
Trip type	Commuters will experience lower VOT.
Time of the day	VOT will be lower in the morning peak.
Trip distance	In short trips both PAV and SAV will exhibit lower VOT compared to CAR but in long trips, PAV will yield lower VOT.
Age	VOT will be lower for young adults (18–30 years).
Education	VOT will be higher for highly educated people.
Income	VOT will be higher for high-income group.
Driving license	VOT will be lower for people having current driving experience.

Table A1. Studies along with their major characteristics included in the meta-analysis (ordered by year of publication).

Study	Country	Survey year	Survey type	Sample size	AV mode	AV explanation
Krueger et al. (2016)	Australia	2015	Online	435	Shared	Written
Yap et al. (2016)	Netherlands	2014	Online	761	Shared	Written
Becker and Axhausen (2018)	Switzerland	2018	Paper	N/A	Shared, Pooled	Written
Kolarova et al. (2018)	Germany	2017	Online	485	Private, Shared	Animation
Steck et al. (2018)	Germany	2017	Online	172	Private, Shared	Animation
Wong et al. (2018)	Australia	2017	Online	108	Private	Animation
Correia et al. (2019)	Netherlands	2017	Online	324	Private	Written
Gao et al. (2019)	USA	2018	Online	502	Shared	Written
Hicks et al. (2019)	USA	2019	Online	614	Shared	Written
Kolarova et al. (2019)	Germany	2017	Online	485	Private, Shared	Animation
Kolarova and Steck (2019)	Germany	2017	Online	441	Private, Shared	Animation
Krueger et al. (2019)	Australia	2017	Online	512	Private	Written
Lavieri and Bhat (2019)	USA	2017	Online	1607	Shared, Pooled	Written
Torres-Montoya and Innao (2019)	USA	2018	Online	1717	Private	Written
Etzioni et al. (2020)	Cyprus, UK, Slovenia, Montenegro, Hungary, Iceland	2020	Online	1669^a	Shared	Written
Sun et al. (2020)	Singapore	2019	Interview	204	Transit	Written
Zhong et al. (2020)	USA	2017	Online	1881	Private, Shared	Written
Szimba and Hartmann (2020)	Germany	2019	Simulation	–	Private	–
Etzioni et al. (2021)	Israel	2019	Online	713	Shared, Pooled, Transit	Animation
Kang et al. (2021)	USA	2019	Online	953	Private, Pooled	Written
Kolarova and Cherchi (2021)	Germany	2017	Online	484	Private, Shared	Animation
Choi et al. (2022)	South Korea	2019	Online	500	Private, Shared	Written
Hamadneh and Esztergár-Kiss (2022)	Hungary	2020	Online	525	Private, Shared	Written
Harb et al. (2022)	USA	2019–20	Interview	71	Private	Chauffeur driven^b

^a Cyprus (158), UK (79), Slovenia (274), Montenegro (321), Hungary (285), Iceland (552).

^b Chauffeur driven personal cars were used as an analogy of personally owned AV.

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