Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
DRIVING ALONE VERSUS RIDING
TOGETHER - HOW SHARED
AUTONOMOUS VEHICLES CAN
CHANGE THE WAY WE DRIVE
Tesla Model S
Key topics to cover
 How quickly will they be adopted?
 How can we model AV?
 How will they change our transport
networ...
HOW QUICKLY WILL THEY BE
ADOPTED?
AV % of new sales – Aggressive
Projected growth in AV fleet
Adoption rate of other technologies
Others
Airbags: 0-100% in 25 years (1973-1998)
Automatic transmission: 0-80% in 70 yrs...
MODELLING APPROACH
4S
Structure
Stochastic:
● Monte Carlo methods to draw
values from probability
distributions
● Random variable parameters
...
Key features of 4S model
 No matrices, no skims, no zones, no centroid
connectors
 All travel is from node to node
 Mod...
South East Queensland Network
Population: 3.4m
Growth rate: 2.4%
Network detail – Brisbane CBD
Stages of AV Modelling
 Stage 1: Driver must be present but
inattentive
 Stage 2: No driver required, can
sleep etc
 Sh...
Mobility-as-a-Service
 ‘Mobility-as-a-Utility’ - have a right to this service
 Complete re-think of how we think of trav...
ASSUMPTIONS
Assumptions
Assumptions: Value of time
 Stage 1: Driver present but
inattentive
 VOT multiplier: 75%-100% c.f.
standard
 Stage 2: N...
Assumptions: Trip rates
 Multiple reasons for more travel
 Reduced cost (perceived and actual)
 Easier sharing of car w...
Assumptions: Veh. operating costs
 AV are likely to be plug in electric
 Significantly lower energy cost and
maintenance...
Assumptions: Capacity
 Stage 1: Mixed AV and Manual
 5% capacity increase
 reduced crash rates and improved
operations ...
HOW WILL THEY CHANGE OUR
TRANSPORT NETWORKS?
Mode Share Impacts
CHANGES IN DISTANCE
TRAVELLED
Changes in average driving
speed
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%11...
Speeds in 2046
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120%
Cumulativ...
CHANGES IN NET UTILITY
WHAT ARE THE EFFECTS OF
SHARED AUTONOMOUS VEHICLES
Behavioural Response to
Shared Autonomous Taxis
 Change from an up front model (buy a car,
annual registration and insura...
Effects of shared Autonomous
Taxis on mode share
Other effects of shared
Autonomous Taxis
 25% drop in time spent travelling: 8.4 to 6.3 m
h/d or 76 to 56 min/person/day
...
Effects of Multi-occupant
Shared AVs
 Reduced cost leads to increased car
demand, but higher vehicle occupancy
 Reduced ...
HOW WILL AV CHANGE OUR
CITIES?
Differential effect of improvements
WHAT ARE THE IMPLICATIONS FOR
WHAT WE DO NOW?
Overall consequences
Operate AV as
improved
private cars
Big problems!
100% AV
Capacity +
speed
improves
Mitigate extra
de...
Overall Consequences
 Best with shared vehicles and mobility-
as-a-service
 Reduce car footprint, share released road
 ...
Conclusions on Infrastructure
 Will need to justify infrastructure spending based
on much shorter projected benefit strea...
Upcoming SlideShare
Loading in …5
×

Driving alone versus riding together - How shared autonomous vehicles can change the way we drive

282 views

Published on

Peter Davidson & Anabelle Spinoulas

Published in: Education
  • Be the first to comment

  • Be the first to like this

Driving alone versus riding together - How shared autonomous vehicles can change the way we drive

  1. 1. DRIVING ALONE VERSUS RIDING TOGETHER - HOW SHARED AUTONOMOUS VEHICLES CAN CHANGE THE WAY WE DRIVE Tesla Model S
  2. 2. Key topics to cover  How quickly will they be adopted?  How can we model AV?  How will they change our transport networks?  What are the effects of shared AV?  How will they change our cities?  What are the implications for what we do now?
  3. 3. HOW QUICKLY WILL THEY BE ADOPTED?
  4. 4. AV % of new sales – Aggressive
  5. 5. Projected growth in AV fleet
  6. 6. Adoption rate of other technologies Others Airbags: 0-100% in 25 years (1973-1998) Automatic transmission: 0-80% in 70 yrs (1940’s) Hybrid vehicles: 0-5% in 25 years (1990’s) Smartphone: 0-80% in 9 years (2007)
  7. 7. MODELLING APPROACH
  8. 8. 4S Structure Stochastic: ● Monte Carlo methods to draw values from probability distributions ● Random variable parameters ● Number of slices can be varied SIMULTANEOUS Segmented: ● Comprehensive breakdown of travel markets (20 private + 40 CV segments) ● Behavioural parameters vary by market segment EXPLICIT RANDOM UTILITY Slice: ● Takes slices of the travel market ○ across model area ○ through probability distributions ● Very efficient – detailed networks, large models Simulation: ● Uses state-machine with very flexible transition rules ● Simulates all aspects of travel choice ● Complex public transport ● Multimodal freight ● Easily extended
  9. 9. Key features of 4S model  No matrices, no skims, no zones, no centroid connectors  All travel is from node to node  Models constructed with MUCH less manual effort  Usually include all roads, all paths, timetabled transit  Can build from OpenStreetMap and GTFS  Population and employment can come from multiple sources with different zoning, including point data (schools, hospitals etc)  Multimodal with all modes assigned  Continuous time and simultaneous choice (DTA)  Easily include any demand based effects and capacity constraints (not just roads and transit)  Much more detailed outputs (volumes by purpose)
  10. 10. South East Queensland Network Population: 3.4m Growth rate: 2.4%
  11. 11. Network detail – Brisbane CBD
  12. 12. Stages of AV Modelling  Stage 1: Driver must be present but inattentive  Stage 2: No driver required, can sleep etc  Shared AV Taxi: single passenger vehicles  Shared multi-occupant AV: allows for car-sharing, however not picking up people along a journey
  13. 13. Mobility-as-a-Service  ‘Mobility-as-a-Utility’ - have a right to this service  Complete re-think of how we think of travel  Door-to-door transport service  Different payment plans - pay-as-you-go or a monthly fee  Supports shared AV use  Huge potential to reduce car ownership  Likely to increase the efficiency and utilisation of transport providers  Possibility for public transport to become more competitive and affordable due to increase efficiency of the network and the use of AVs  The model used in this analysis considers fully multi- modal travel so in affect we already consider a basic model for MaaS.
  14. 14. ASSUMPTIONS
  15. 15. Assumptions
  16. 16. Assumptions: Value of time  Stage 1: Driver present but inattentive  VOT multiplier: 75%-100% c.f. standard  Stage 2: No driver required  VOT multiplier: 60%-100%  Shared AV Taxi: Assume same as Stage 2  Shared multi-occupant AV: 65%- 100%
  17. 17. Assumptions: Trip rates  Multiple reasons for more travel  Reduced cost (perceived and actual)  Easier sharing of car within family  Reduced parking hassles  Travel by non-drivers (children, elderly, unlicensed, disability)  Travel in non-driving state (drunk, tired)  Assume 10% increase in Stage 1 15% in Stage 2 10% for Shared AV Taxi 15% for Shared multi-occupant AV
  18. 18. Assumptions: Veh. operating costs  AV are likely to be plug in electric  Significantly lower energy cost and maintenance costs  Even traditional ICE cars will have lower costs due to better driving  Stage 1: 50%-75% of current VOC  Stage 2: 50% of current VOC
  19. 19. Assumptions: Capacity  Stage 1: Mixed AV and Manual  5% capacity increase  reduced crash rates and improved operations from connected vehicles  Stage 2: 100% AV  no manually driven cars - significant operational improvements; high density; higher speeds; improved intersection operations  20% capacity increase  20% improvement in free flow speeds  25% decrease in intersection delays
  20. 20. HOW WILL THEY CHANGE OUR TRANSPORT NETWORKS?
  21. 21. Mode Share Impacts
  22. 22. CHANGES IN DISTANCE TRAVELLED
  23. 23. Changes in average driving speed 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%110%120% Cumulative%oftimespentdriving Congested speed factor (% of posted speed) Base11 Base36 Av36High Av46Mod
  24. 24. Speeds in 2046 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% Cumulative%oftimespentdriving Congested speed factor (% of posted speed) Base11 Base46 Av46Mod Av46High Av46HighShared
  25. 25. CHANGES IN NET UTILITY
  26. 26. WHAT ARE THE EFFECTS OF SHARED AUTONOMOUS VEHICLES
  27. 27. Behavioural Response to Shared Autonomous Taxis  Change from an up front model (buy a car, annual registration and insurance) to a pay-as- you-go model  Lower annual cost, but higher trip cost (for most trips)  For modelling, assume that people make travel choices based on marginal costs  This may overstate the impact of shared AV  If people only consider annualised costs then they will do more travel
  28. 28. Effects of shared Autonomous Taxis on mode share
  29. 29. Other effects of shared Autonomous Taxis  25% drop in time spent travelling: 8.4 to 6.3 m h/d or 76 to 56 min/person/day  55% drop in distance travelled: 269 to 147 m km/d or 40.4 to 22 km/person/day  Increase in daily costs and drop in per capita net utility  But annual costs are equivalent to $14- $24/day  40% cost savings: $38 to $23/person/d  Net utility increases by $9.60/person/day
  30. 30. Effects of Multi-occupant Shared AVs  Reduced cost leads to increased car demand, but higher vehicle occupancy  Reduced public transport  More efficient use of road space  Better environmental outcomes (due to higher efficiency and smaller vehicle fleet)
  31. 31. HOW WILL AV CHANGE OUR CITIES?
  32. 32. Differential effect of improvements
  33. 33. WHAT ARE THE IMPLICATIONS FOR WHAT WE DO NOW?
  34. 34. Overall consequences Operate AV as improved private cars Big problems! 100% AV Capacity + speed improves Mitigate extra demand 100% AV with shared autonomous taxis Better operations Reduced demand
  35. 35. Overall Consequences  Best with shared vehicles and mobility- as-a-service  Reduce car footprint, share released road  Revolutionise transport and big changes in urban form
  36. 36. Conclusions on Infrastructure  Will need to justify infrastructure spending based on much shorter projected benefit streams  Best approach (as usual) would be to implement road pricing - it could take us over the hump  Need more modelling Time infrastructure requirements

×