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

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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

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