Preferred citation style

Axhausen, K.W. (2017) How to organise a 100% autonomous transport system ?, presentation at the University of Newcastle, Newcastle upon Tyne, May 2017

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How to organise a 100% autonomous transport system ?

KW Axhausen

IVTETHZürich

May 2017

Acknowledgements

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• Weis: Induced demand

• Meyer, Becker, Bösch: Accessibility and AV

• Bösch, Becker and Becker: Cost calculations

• Loder, Ambühl, Menendez: MFD

When will they arrive?

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On-going trials known to Accenture, February 2017

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Known hurdles

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• Regulatory approval• Behaviour in dilemma situations• Restrictions to protect incumbents

• Car manufacturers and service industries• Public transport industry• Taxi industry

• User acceptance• Reliance on taxi services• Acceptance of pooled taxi services• Replacement of the pride of ownership• Foregoing the mastery of the car

Known hurdles

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• Non-user behaviour• Social norms for playing with AVs• Encoding social norms into the AV logic

• User behaviour• Number and extent of empty rides • Use for butler services (delivery, early positioning, etc.)

Basic trade-offs

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Updated full cost/pkm estimate (current occupancy levels)

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Updated full cost/pkm estimate (current occupancy levels)

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Updated full cost/pkm estimate (current occupancy levels)

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Basic trade-offs between supply and demand

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• Costs for generalised cost (service) levels • Fixed costs

• Ownership, taxes, insurance, repair• Management

• Variable costs• Fuel, toll, parking, maintenance, cleaning• Promotion

• Generalised costs• Access/egress walk and waiting time• Speed (urban, longer-distance trips)• Quality of the ride (design, cleanliness, in-vehicle services)• Fares (pricing models)

Some scenarios for a 2030 Level 5 vehicle future

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Facets

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• Market structure (monopoly, oligopoly, dispersed)

• Role and extent of transit

• System target (system optimum, user equilibrium)

• Type of traffic system manager

• Road space allocation

• Share of autonomous vehicles

• Share of electric vehicles

Scenario 1 – As before

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• Dispersed: Current owners replace their vehicles

• Transit scaled down to the high capacity modes

• User equilibrium as system target

• Municipalities remain traffic system manager

• Road space allocation trends towards the AV, maybe even growth

• 100% share of small autonomous vehicles for safety reasons

• 100% share of electric vehicles for climate reasons

Scenario 2: Uber et al. take over

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• Oligopoly of fleet owners

• Transit scaled down to the high capacity modes

• System optimum via tolls and parking charges

• Operators negotiate slots with each other

• Road space allocation tends towards the slow modes

• 100% share of mixed size autonomous vehicles for cost reasons

• 100% share of electric vehicles for climate reas0ns

Scenario 3: local transit new

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• Monopoly, the MTR expands into small vehicles

• Larger vehicles and hub-operations are encouraged

• System optimum routes are allocated over the days

• MTR is the traffic system manager

• Road space allocation unchanged

• 100% share of mixed size autonomous vehicles for cost reasons

• 100% share of electric vehicles for climate reasons

How to enable the mobility of low income travellers?

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• Today• Public covers the fixed costs, especially for railways, but also

busses• Across-the-board operational subsidies

• Lack of means-testing• Low price season tickets/fares

• Operational support via priority at signals and road space allocation

• Future, where each kilometre is tracked and chargeable• Income-adjusted rebates ?• Income and work-distance adjusted rebates ?• Fixed free kilometre budget ?

How to allocate the allocate the income from scarcity?

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• Congestion costs of (missing) road capacity• Public congestion charge redistributed as

• New capacity?• Reduced local sales taxes/income taxes?

• Private tariffs • Cross-subsides to ensure capacity in too low demand

periods• Higher income to the operators with reaching the

optimal toll• Waiting costs of a too low vehicle capacity

• Surge pricing redistributed• Too increase capacity made available• Higher operator incomes

Detour: MFDs

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MFD: Capacity differences: London (3 days)

0

100

200

300

400

0.0 0.1 0.2 0.3 0.4 0.5Occupancy

Flow

[veh

/h−l

ane]

Days: 3, N=184

0

100

200

300

400

0.0 0.1 0.2 0.3 0.4 0.5Occupancy

Flow

[veh

/h−l

ane]

Days: 3, N=184

0

3000

6000

9000

0 5000 10000 15000 20000East direction [m]

North

dire

ctio

n [m

]

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MFD: Capacity differences: Madrid (1 day)

0

200

400

600

0.0 0.1 0.2 0.3 0.4Occupancy

Flow

[veh

/h−l

ane]

Days: 1, N=294

0

2500

5000

7500

0 2000 4000East direction [m]

North

dire

ctio

n [m

]

0

200

400

600

0.0 0.1 0.2 0.3 0.4Occupancy

Flow

[veh

/h−l

ane]

Days: 1, N=294

0

2500

5000

7500

0 2000 4000East direction [m]

North

dire

ctio

n [m

]

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MFD: Stability - Luzern

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Detour continued: Multimodal MFDs

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Study area: Zürich

• 2.6 km2

• Innenstadt ~ 50% Strassenkilometer abgedeckt• Wiedikon ~ 30% Strassenkilometer abgedeckt

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Zürich: The car MFDsM

IV V

erke

hrsf

luss

[Fz/

Spur

-h]

MIV Verkehrdichte [Fz/Spur-km]

Innenstadt Wiedikon

Ambühl, L., Loder, A., Menendez, M. and Axhausen, K. W. (2017) ‘Empirical Macroscopic Fundamental Diagrams: New Insights from Loop Detector and Floating Car Data’, Presented at 96th Annual Meeting of the Transportation Research Board.

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Zürich: 3d MFD (FCD & loops) City centre

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Lode

r et a

l., 20

16

Zürich: 3d MFD (Loops) City centre

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Lode

r et a

l., 20

17

05

1015

2025

Wei

ghte

d av

erag

e sp

eed

[km

/h]

0 .2 .4 .6 .8 1Share of public transport users (paxpt ⁄ paxtot)

1000 pax3000 pax5000 paxobserved

Accessibility and mobility tools: Swiss case

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AcccarAccbusAccrail

ncar

nGA

Switzerland: general accessibility

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Accessibility and car ownership in Switzerland

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2010 Mobility tools in Switzerland

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Induced demand by AV

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Induced demand elasticities from a pseudo-panelSo

urce

: Wei

s und

Axh

ause

n (2

013)

Accessibility Share of mobiles 0.61Number of trips 0.44Trips per hour 0.24Out-of-home time 0.10Total distance travelled 1.14

Transport price index Share of mobiles -0.06Number of trips -0.19Trips per hour -1.66Out-of-home time -1.95Total distance travelled -0.84

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Accessibility change for scenario 3/optimistic

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Accessibility change for scenario 3/o with induced demand

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What should we do ?

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Next steps

• More work on acceptance of AV • By age and education• By location of residence

• More work on future cost/prices by type of operator

• More work on the efficiency of the fleets (empty kilometres, parking, drop off/pick up)

• More work on how to achieve system optimum with fleet operators

• More work on future ‘transit’ ?

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Questions ?

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