expectation, adoption, and appreciation of an automated

03.10.2020 1

Expectation, Adoption, and Appreciation of an automated bus service: a longitudinal analysis

Yusak Susilo, Univ. Prof. Dr.

BMVIT Endowed Professor in Digitalisation and Automation in Transport and Mobility SystemsUniversity of Natural Resoures and Life Sciences (BOKU), Vienna, Austria

Digitalisation and Automation in Transport

and Mobility Systems

DAVeMoS

Acknowledgement

• The case studies discussed in this presentation is a part of MMiB (Modern Mobilitet i Barkarbystaden) project, which is a part of Drive Sweden project, funded by Vinnova, a collaboration led by Nobina AB, with SLL, Järfälla municipality, and KTH ITRL.

• The results presented today will focus on the users’ behaviours, which have been analysed together with: Jia Guo (TUM), Esther Chee Pei Nen (NTU Singapore), Mia Xiaoyun Zhao (KTH), Anna Pernestal (KTH), and colleagues

03.10.2020 2Digitalisation and Automation in Transport


DAVeMoS

Outline

• Background: The needs to match expectations with realities

• Case study and survey design

• Usage patterns, appreciation and WTPs

• What made people adopt the service (new transport technology) and vice versa – and how this different compared to traditional buses

• Reflections

3

Automated vehicles

• It is coming, and always looks good in the internet, amazing in the movies - but what does it mean? How this would impact our city and our life?

03.10.2020 4Picture sources: various newsletter and getty

What techniques we have been working at in the last decade(s)?• Optimised

• Smooth

• Safe

• On-demand

• Responsive

• Robust

• Better use of resources

• Multimodal

• ….

03.10.2020 5Source: Disney/Pixar, 2008

Why this makes some people so excited?

• It will be our savior ! (we can have our cake and eat it)

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Source: BCG, 2017

And innovations and living labs:

Leads to explosions of interests and research

03.10.2020 7Deploymant Map Sources: EasyMile and Navyia websites, 12 January 2020, Pictures: Barkarby (Stockholm), Seestadt (Vienna)

Assuming that this is a good thing and it will only bring goodness and prosperity to ALL of us ….(there are pros and cons evidences that need to be further discussed)

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Still need to match expectations with realities … to make the whole thing implementable

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• Many of previous studies based on hypothetical conditions/under SP framework. Difficult to use the numbers for a real service design (by real PT operators).

• For our local authorities:• Fast growing citi(es), and have first and last mile problems.

• Important for innovation: move beyond sandbox concept – and enable the authorities and PT operators to build their capacity (regulation and operation) beyond test-track deployment(s)

• Led to a deployment of Automated Bus Service as a regular PT operation.

• For our operators: • What it takes to operate such technology

• What would be the business case

• How they can operate things differently

• To have a fully operational and integrated automated public transport.

• Focus on first and last mile service, not to compete with but to complement the existing public transport service

• Started with Kista project (full scheduled service, on public roads, but free)

• two EZ10, 6 months deployment (January 2018-June 2018)• The focus of the deployment is the feasibility to use such

service as a last mile transport option. • Used by more than 10k passengers and logged 2000km• Learned a lot from operational side• Big gain: work with existing paper works and laws

• To understand the demand: need a serious deployment

Case study: Barkarby, Stockholm, Sweden

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Source: Pernestål-Brenden et al., 2018; Chee et al., 2020a

Brief overview of select results to date

• 1. Who is willing to use this service:

Source: Chee et al., 2018


Source: Chee et al., 2020b

• To have a fully operational and integrated automated public transport.

• Focus on first and last mile service, not to compete with but to complement the existing public transport service

• Started with Kista project (full scheduled service, on public roads, but free)

• Make routes that make sense to cater people travel needs – optimize between users’ costs and operators costs and push the limit of the vehicles

• 4-6 EZ10s, fully paid service, mixed road, with headway 10-15 minutes, 15 km/hours (soon, to increase to 15-25 km/h), 7 days a week, last/first mile to major bus hub, serving a constantly expanding neighbourhood.

• By end of October 2019, supported by integrated Travel information (Travis)


03.10.2020 13Source: Guo et al., 2020a

• In term of analyses, uniqueness of the case study:• long term studies – panel,

• keep growing (built) environment,

• mix of new and old users – there always be new users,

• real interactions with other road users.

• Three waves of longitudinal survey: (500 inhab.+ 100 fresh)• socio-economic variables,

• transport habits (e.g. mode usage, number of trips, etc.),

• psychological (attitudes, preferences and perceptions) questions,

• the feasibility of business model and WTP.


03.10.2020 14Source: Guo et al., 2020a

Empirical analysisCharacteri sti cs Wave 1

(n = 519)

Wave 2

(n = 573)

Wave 3

(n = 584)

All waves

(n = 393)

Gender

Male 44.7 44.5 43.2 44.8

Age (years)

0–14 0 0 0.3 0

15–24 6.6 6.3 7.0 4.1

25–34 32.4 34.0 34.4 30.5

35–44 27.0 27.0 27.1 28.8

45–54 13.5 13.5 13.4 14.5

55–64 8.7 8.2 8.2 9.2

Above 65 11.9 10.9 9.6 13.0

Employment status

Full -time employed 72.3 74.7 71.4 71.0

Self-employed 3.7 2.8 3.1 3.3

Student 6.9 7.0 8.2 7.1

Other (pension, parental-l eave) 17.1 15.4 16.8 18.6

Education status

Primary school 2.9 3.0 2.7 2.5

Upper secondary school 27.7 28.5 28.3 25.4

Bachelor degree 16.8 15.4 17.8 16.5

Master degree 51.3 51.5 49.1 53.7

Doctoral degree 1.3 1.6 1.5 1.8

Household gross yearly income in Swedish Kronor (SEK) (before tax)

Below 100,000 SEK 1.7 1.6 1.7 1.5

100,000–299,000 SEK 10.2 10.3 9.1 9.9

300,000–499,000 SEK 24.5 26.8 27.1 21.4

500,000–699,000 SEK 20.8 19.1 18.7 21.1

700,000–899,000 SEK 12.7 12.3 11.5 14.2

Above 900,000 SEK 9.1 7.9 7.7 10.2

Do not want to specify 21.0 22.1 24.0 21.6

Live or work in the neighborhood

Yes, live in the neighborhood 91.7 91.3 89.9 93.9

Yes, work in the neighborhood 9.1 9.4 9.2 7.6

Nei ther 3.7 3.5 4.6 3.3

Car ownership

Yes 75.0 68.1 64 75.9

No 25.0 31.9 35.3 24.1

Do you consider yourself a tech-savvy person?

Yes 87.2 87.1 72.8 89.0

No 12.8 12.9 9.8 11.0

How do you usually commute?

Walk 17.2 16.6 16.8 18.2

Cycling 15.1 14.4 14.2 16.9

By bus 49.1 51.5 53.3 46.8

By Metro 32.1 31.5 32.5 32.7

By Train 48.7 48.9 50.2 52.9

By Car 38.3 34.3 31.7 35.8

87,70%

89,80%

93,40%

11,50%

9,50%

6,30%

0,80%

0,70%

0,30%

80,00% 82,00% 84,00% 86,00% 88,00% 90,00% 92,00% 94,00% 96,00% 98,00% 100,00% 102,00%

Wave 1

Wave 2

Wave 3

Awareness of the ABs in Barkarby

Yes and seen Yes but not seen No

20,70%

24,70%

28,10%

1,70%

2,70%

2,90%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Wave 1

Wave 2

Wave 3

Took ABs before

1-5 times 6-10 times 11-15 times >15 times NeverSource: Zhao et al., 2020

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

35,00%

40,00%

N/A Stronglydisagree

Disagree Neutral Agree Strongly agree

ABs should play an important role in our transportation systems in the future?

Wave2 Wave3

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

35,00%

N/A Strongly disagree Disagree Neutral Agree Strongly agree

ABs would serve your travel needs?

Wave2 Wave3

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

35,00%

40,00%

N/A Strongly disagree Disagree Neutral Agree Strongly agree

Is ABs suitable for everyday use?

Wave2 Wave3

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

35,00%

40,00%

45,00%

50,00%

N/A Definitely not Probably not Neutral Probably yes Definitely yes

Use ABs more often in the future?

Wave2 Wave3

Source: Zhao et al., 2020


• 2. What are the important QoSAs for them?

Basic Factor:Travel Fare of the Automated Bus Service,

compared to other mode alternative

Performance Factor:Frequency of the Automated Bus Service

Exciting Factor:Ride Comfort due to Driving Speed and

Driving Patter of Automated Bus

Basic Factor

Performance Factor:Travel fare compared to other alternativeTravel time compared to other alternative

Exciting Factor:Ride Comfort due to Driving Speed and

Driving Patter of Automated Bus

Basic Factor

Performance Factor:Frequency of the Automated Bus Service

Exciting Factor:Travel time compared to other alternative

Experienced Users (N=269) Non-Experienced Users (N=269) All Users (N=574)

Source: Susilo et al., 2020; Chee et al., 2020b


• 3. On innovation delivery and operation design:

Adopters vs Prospective Adopters vs Persistent Non-Adopters

On 4 aspects:1. Reliability2. Safety3. Comfort 4. Information provision

Source: Jia et al., 2020a

(1) Safety perception, (2) car ownership, (3) management of expectation and disappointment are important elements that have great influence on the adoption of the technology


• 4. Revisit guidelines and practices on infrastructure design and investments:

Contribute to the harmonisation and discussion of the framework for the use of automated vehicles and the investment and planning of digital infrastructure

Also revisit the plausible impacts on the user groups on public roads

Source: Chee et al., 2020c

MNL: What factors that differentiate adopters vs potential adopters vs non-adopters?

Adopters Potential-adoptersVariable Value Standard

ErrorSig. Value Standard

ErrorSig.

Attitudinal VariableReliability

Reliability .04 .22 .84 .09 .20 .65Speed .44 .33 .18 .18 .30 .54Frequency .10 .23 .64 .17 .19 .38Travel time than bus service .13 .30 .65 .34 .26 .19Travel time than car -.01 .29 .98 .02 .26 .94

SafetySafety with No operator .39 .23 .09 .30 .21 .15Safety with operator .46 .21 .03 .33 .19 .08Hacked -.15 .21 .47 -.30 .19 .11

Informative .41 .28 .15 -.16 25 .53Ride comfort

Pleasant ride .23 .30 .44 .32 .27 .24Expectation of comfortability -.68 .29 .02 -.25 .26 .33

Social demographicGender Male 1; Female -1 .18 .42 .67 .30 .37 .43Age Young 1; Old -1 -.21 .43 .62 .27 .38 .48Income Low 1; High-1 .26 .42 .54 31 .37 .41Car ownership Own cars 1; Have no car -1 -.93 .57 .10 -.84 .53 .10Constant -2.88 1.64 .08 -.86 1.48 .56Pseudo R-Square .16

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Safety perception - Non-adopter are more sceptics.Most social-demographic characteristics such as age, gender, and annual income are found have no significant impact on the bus adaptation. People currently have no car are more likely to take the autonomous bus ride. Beside car ownership, “disappointment” is another factor that has large negative magnitude to adopt the technology.

None of respondents’ expectation and perception is found significantly influencing the duration of the adopting processes of the new transportation mode.

Source: Guo et al., 2020a

MxL: automated vs regular busMNL ML

Variable Description Auto Bus Bus Auto Bus BusSPEED High 1, Low -1 -.27999*** -.28535*** -.37148*** -.37073*** SEAT Enough seat 1, Crowed -1 .22569*** .21585*** .29858*** .27107*** LANE Have bus lane 1, Share -1 .09457*** .26740*** .12093*** .34738*** FREQ1 Every 5 mins .59127*** .45066*** .76195*** .58244*** FREQ2 Every 10 mins .11744** .14212** .14565** .17934*** FREQ3 Every 15 mins -.28711*** -.15974*** -.37188*** -.19419*** WALK1 5 mins .77268*** .75992*** .98936*** .95839*** WALK2 10 mins .38142*** .20439*** .49076*** .26281*** WALK3 15 mins -.45560*** -.32762*** -.57807*** -.41726*** Context VariablePURPOSE Work 1, Recreation -1 -.09659*** -.13090*** DISTANCE Short 1, Long -1 .09564*** .11846*** WEATHER Sunny 1, Raining or snowy -1 .04650 .06767* TIMEOFDAY Peak time 1, Off peak time -1 -.02034 -.01801 COMPANION With friends family 1, Alone -1 .16531*** .20661*** AGE .02638 .03146 C_ Auto Bus .01878 -.00582 NStd.Devs 1.29614*** Log likelihood -2582.14813 -2407.68649LL 0 -3149.6608 -3149.6608 R2 .18 .2312

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Automated bus passengers are more sensitive to the drop of LOS, in particular towards frequency and walking distanceNo differences in travel speed appreciationAutomated bus users less concerns of bus lane, but expect more of seat availability

Automated bus is less appealing for work trips, longer trips, and if they travel alone. Weather and time of day were found insignificant

Source: Guo et al., 2020b

Take-home message(s):

• We presented and highlighted evidences from full scale deployment of automated bus service on public road as last and first mile PT service.

• Values and proportions changes overtime – importance of longitudinal studies

• Low(er) expectation is the unique characteristic of adopters vs non-adopter –important to set the right image and expectations.

• Tipping points: safety perception, car ownership, and ability to coup with (or avoiding) disappointment (towards own expectations of what technology could do)

• Inability of traditional explanatory variables to estimate the adoption time highlight the challenges to forecast the demand of this (new!) travel mode.

• Automated bus users are more elastics towards the changes of LOS

03.10.2020 22

Thank you!

Yusak Susilo, [email protected]

www.davemos.online

23Digitalisation and Automation in Transport


DAVeMoS

Ongoing: rural contextSource: Digibus Austria/EasyMile, 2020

Relevant publications:

• Chee, P.N.E., Susilo, Y.O., Pernestål Breden, A., and Wong, Y.D. (2018) Investigating the willingness of use autonomous bus as a last-mile travel mode: First evidence from public trial in Kista, Stockholm. hEART 2018 – 7th Symposium of the European Association for Research in Transportation, Athens, Greece.

• Chee, E.P.N, Susilo, Y.O., Pernestål-Brenden, A. and Wong, Y.D. (2020a) Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden. European Transport Research Review, 12, 20 (2020), doi: 10.1186/s12544-020-00404-y

• Chee, E.P.N, Susilo, Y.O., and Wong, Y.D. (2020b) Determinants of Intention to Use First-/Last-mile Shared Autonomous Bus Service. Transportation Research part A, 139, pp. 350 – 375, doi: 10.1016/j.tra.2020.06.001.

• Chee, E.P.N, Susilo, Y.O., Pernestål-Brenden, A. and Wong, Y.D. (2020c) Evaluation of pedestrian midblock road-crossing risk when interacting with automated bus. Submitted to Transportation Research part C.

• Guo, J., Susilo, Y.O., Antoniou, C. and Pernestål, A. (2020a) Influence of Individual Perceptions on the Decision to Adopt Automated Bus Services. Sustainability, 2020, 12(16), 6484, doi: 10.3390/su12166484

• Guo, J., Susilo, Y.O., Antoniou, C. and Pernestål, A. (2020b) Customers’ Optimism Bias and the Word of Mouth in an Automated Bus Service Adoption. Submitted for publication at Cities.

• Guo, J., Susilo, Y.O., and Pernestål, A. (2020c) How Expectation and Perception Affects Public’s Use Intention of Automated Buses for Different Journey Purposes. Under review for publication at Travel Behaviour and Society.

• Pernestål-Brenden, A., Darwis, R., Susilo, Chee, P.N., Jenelius, E., Hatzenbühler, J., Hafmar, P. (2018) Shared Automated Vehicles - Research & Assessment in a 1st pilot, SARA1 Results report. Vinnova.

• Zhao, X., Susilo, Y.O. and Pernestål, A. (2020) The long term acceptance pattern of automated public transport service: Evidence from Stockholm. The 3rd Symposium on Management of Future Motorway and Urban Traffic Systems (on-line), Luxembourg, July 2020.

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expectation, adoption, and appreciation of an automated

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