driving alone versus riding together - how shared autonomous vehicles can change the way we drive
Post on 15-Jan-2017
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Peter DavidsonAnabelle SpinoulasTransPosition
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
networks? What are the effects of shared AV? How will they change our cities? What are the implications for what we
do now?
HOW QUICKLY WILL THEY BE ADOPTED?
AV % of new sales – Aggressive
Projected growth in AV fleet
Adoption rate of other technologies
OthersAirbags: 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)
MODELLING APPROACH
4S
StructureStochastic:● Monte Carlo methods to draw
values from probability distributions
● Random variable parameters● Number of slices can be
variedSIMULTANEOUS
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
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)
South East Queensland NetworkPopulation: 3.4mGrowth 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 Shared AV Taxi: single passenger
vehicles Shared multi-occupant AV: allows
for car-sharing, however not picking up people along a journey
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.
ASSUMPTIONS
Assumptions
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%
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
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
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
HOW WILL THEY CHANGE OUR TRANSPORT NETWORKS?
Mode Share Impacts
CHANGES IN DISTANCE TRAVELLED
Changes in average driving speed
-10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%110%120%0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Base11 Base36Av36High Av46Mod
Congested speed factor (% of posted speed)
Cum
ulati
ve %
of ti
me
spen
t driv
ing
Speeds in 2046
-10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120%0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Base11 Base46 Av46Mod Av46HighAv46HighShared
Congested speed factor (% of posted speed)
Cum
ulati
ve %
of ti
me
spen
t driv
ing
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 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
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
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
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)
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
demand
100% AV with shared
autonomous taxis
Better operationsReduced demand
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
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
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