chances and impacts of autonomous vehicles - eth z · preferredcitationstyle axhausen, k.w. (2017)...
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Preferred citation style
Axhausen, K.W. (2017) Chances and impacts of autonomous vehicles, Seminar CASA, UCL, London, September 2017.
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Chances and impacts of autonomous vehicles
KW Axhausen
IVTETHZürich
September 2017
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Acknowledgments
S Hörl for the work on AV simulation
P Bösch, F Becker and H Becker for the cost estimates
Meyer, H Becker and P Bösch for the induced demand work
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Basic assumption 1
Accessibility ∼Opportunities, Speeds
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Basic assumptions2
Traffic is a system of moving, self-organising
Queues
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Basic assumption 3
The queues are the crucial short-term interaction between capacity, i.e. the
number of slots
for the desired speed and the
current demand
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Basic assumption 4
Travel demand (pkm) is a
normal good
i.e. it grows with
sinking “generalised costs”
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Basic assumption 5
The travellers chose their
average generalised costs
with their package of
locations (residence, work) andmobility tools
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Basic assumption 6
A person‘s travel demand is the
result of its activity participation
constrained by the currently
available time and money resources
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When will they arrive?
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On-going trials known to Accenture, February 2017
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And maybe why not
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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
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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.)
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What are the current expectations?
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What are the current expectations?
• AV will reduce the generalised costs (time perception, monetary costs)
• AV will reduce them further through (pooled) taxis• AV will increase the number of slots
• AV will redistribute time by reducing shopping and pick-up/drop-off trips
• AV (vehicles/drones) will undermine the existing retail services
• AV will make most of current public transport superfluous
• AV will enable a new wave of urban sprawl
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Basic trade-offs
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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)
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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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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
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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
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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
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Scenario 3: Local transit new
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• Monopoly, the MVV expands into small vehicles
• Larger vehicles and hub-operations are encouraged
• System optimum routes are allocated over the days
• MVV 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
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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 ?
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MATSim: An open-source agent based simulation
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Simulation Framework: DVRP extension
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Mac
ieje
wsk
iet a
l. (2
017)
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Simulation Framework: DVRP further extensions
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• Single & multi passenger trips
• Demand-responsive simulation
• Multiple operators
• Full integration as ‘public transport’
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Excursus: Homophily in shared rides
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Zhao, J. (2017) Urban Agenda for AV Deployment shared mobility, human interaction & urban creativity, presentation at Future Urban Mobility Symposium 2017, Singapore, July 2017.
.
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Homophily in shared rides
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What would be the generalised costs of matching riders according to their preferred social criteria?
Matching to • Minimize the travel time of the shared AVs travelling• Minimize the miles travelled of the shared AVs• .Maximise the degree of the social match
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Number of matches by extra waiting time and criteria
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Travel time by matching criterion
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0 2.5 5 7.5 10 12.5 15
Nosharing
MinimizetraveltimeofAVs
MinimizemilestravelledofAVs
Maximisethesocialmatch
Passengertraveltime/trip[min]
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Induced demand by AVs
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Induced demand elasticities from a pseudo-panelSo
urce
: Wei
s un
d Ax
haus
en (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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2010 Switzerland general accessibility
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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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Accessibility change for scenario 3/c with induced demand
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What should we do next?
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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, rebalancing, dispatch)
• 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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References
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Hörl, S. (2016) Implementation of an autonomous taxi service in a multi-modal traffic simulation using MATSim. Master Thesis, Chalmers University of Technology, Göteborg.
Maciejewski, M., J. Bischoff, S. Hörl and K. Nagel (2017) Towards a testbed for dynamic vehicle routing algorithms, Accepted for presentation at the 15th International Conference on Practical Applications of Agents and Multi-Agent Systems, Porto.
Bischoff, J., M. Maciejewski (2017) Simulation of City-wide Replacement of Private Cars with Autonomous Taxis in Berlin. Procedia Computer Science, 88, 237-244.