Preferred citation style

Transcription

1 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

2 How to organise a 100% autonomous transport system? KW Axhausen IVT ETH Zürich May 2017

3 Acknowledgements Weis: Induced demand Meyer, Becker, Bösch: Accessibility and AV Bösch, Becker and Becker: Cost calculations Loder, Ambühl, Menendez: MFD

4 When will they arrive?

5 On-going trials known to Accenture, February 2017

6 Known hurdles 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

7 Known hurdles 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.)

8 Basic trade-offs

9 Updated full cost/pkm estimate (current occupancy levels)

10 Updated full cost/pkm estimate (current occupancy levels)

11 Updated full cost/pkm estimate (current occupancy levels)

12 Basic trade-offs between supply and demand 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)

13 Some scenarios for a 2030 Level 5 vehicle future

14 Facets 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

15 Scenario 1 As before 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

16 Scenario 2: Uber et al. takeover 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

17 Scenario 3: local transit new 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

18 How to enable the mobility of low income travellers? 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?

19 How to allocate the allocate the income from scarcity? 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

20 Detour: MFDs

21 MFD: Capacity differences: London (3 days) 400 Days: 3, N=184 Flow [veh/h lane] Flow [veh/h lane] Occupancy North direction [m] East direction [m] Occupancy

22 MFD: Capacity differences: Madrid (1 day) Days: 1, N= Flow [veh/h lane] Flow [veh/h lane] Occupancy North direction [m] North direction [m] East direction [m] Occupancy East directio

23 MFD: Stability - Luzern

24 Detour continued: Multimodal MFDs

25 Study area: Zürich 2.6 km 2 Innenstadt ~ 50% Strassenkilometer abgedeckt Wiedikon ~ 30% Strassenkilometer abgedeckt

26 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. Zürich: The car MFDs Innenstadt Wiedikon MIV Verkehrsfluss [Fz/Spur-h] MIV Verkehrdichte [Fz/Spur-km]

27 Zürich: 3d MFD (FCD & loops) City centre Loder et al., 2016

28 Zürich: 3d MFD (Loops) City centre Loder et al., 2017 Weighted average speed [km/h] Share of public transport users (pax pt pax tot ) 1000 pax 3000 pax 5000 pax observed

29 Accessibility and mobility tools: Swiss case n car n GA Acc car Acc bus Acc rail

30 Switzerland: general accessibility

31 Accessibility and car ownership in Switzerland

32 2010 Mobility tools in Switzerland

33 Induced demand by AV

34 Induced demand elasticities from a pseudo-panel Accessibility Share of mobiles 0.61 Number of trips 0.44 Trips per hour 0.24 Out-of-home time 0.10 Total distance travelled 1.14 Source: Weis und Axhausen (2013) Transport price index Share of mobiles Number of trips Trips per hour Out-of-home time Total distance travelled

35 Accessibility change for scenario 3/optimistic

36 Accessibility change for scenario 3/o with induced demand

37 What should we do?

38 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?

39 Questions?