future radar on self driving vehicles: impact assessment ...€¦ · •creative economy...
TRANSCRIPT
Future Radar on Self Driving Vehicles:
Impact assessment on the city we want to be
Eric de Kievit | Senior Advisor Transport & Traffic Research | Mobility & Public Space
Manchester, Friday November 23th, 2018
After my presentation…
▪ You know what future
radar is
▪ Why we developed it
▪ How we use it
▪ Why you might find it
useful too
2
The city we want to be
Context
Self drivingVehicles
Sustainable
E-mobility
Self-parking
Efficient useof space
Speed
traveltimeSafety
No human errors
Sharing
More or lessmobility?
Savingmoney
personel, parking, data
But from impact studies we know:
Three scenario’s based on complexity driving task (highway, corridors, everywhere in city (high/low sharing))
0 0
11 11
25
0 0
8
13
23
0 0
9
11
10
0
28
31
33 9
30
8
60 0
7
7
00 0
12
12
65 5
6
6
00 019 13
11 9 9
26 26 19 18 18
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Basecase (2050) Scenario1 Scenario2 Scenario3a Scenario3b
% kilometres travelled per modality
Bicycle
Train
Tram
Metro
Bus
Car
SDCar
SDTaxi
SDTaxi (shared)
SDBus
% kilometres travelledon the road
And Modal Shift
More kilometres travelled by car, more use of spacein contrast of reducing # cars in city centre
More in-car safety devices doesn’t mean safer driving
Without ADAS: 14%With 4 or more ADAS: 23% equal to + €75mln repair costs
Clear effects, but…. a lot of uncertainties
Research & Pilots
Knowledge base
(Community of
Practice)
Time critical
Interventions
Therefore actionplan on self driving vehicles :
Future radar
to summarize
▪ We are worried about impact of SDV– On trips
– On traffic safety
– On modal shift
– On use of public space (parking)
▪ Investments in public transport or parking facilities take
long time
▪ Public transport concessions/contracts last for many years
➢So we need to know what time critical interventions we
have to take now → Future Radar can help
10
Future RadarWhat it is
• Individual freedom• High acceptance• New targetgroups• Diversity of vehicle
types, mobilityservices
• Chaotic traffic• Creative economy• Suburbanisation
• smart roads regulate traffic• ‘Internet of Cars’• Less sharing• Selfdriving wins from PT• Creative economy
(entertainment)• A’dam heart of the Metropole
•Efficiency of the system•Sharing is popular•Governance by data•Crowdedness decreases•Strong spatial andeconomic diversion
• Liveability• Strong voice of inhabitant• No SDV within ringroad• Hubs along ringroad• Sustainablity important• More space for
pedestrian, cyclist and PT• Competitive disadvantage
“What will Amsterdam (people, economy, urban planning) look like under level 5 autonomous vehicles?”
ExampleSelfdriving E-taxi
Future RadarExample E-taxi
Indicators
Attractiveness private vehicles
High-medium-low
Attractiveness of
sharing
High-limited-low
Number of
journeys in the city
Grow-same-decrease
Number of
vehicles in the city
Grow-same-decrease
Impact on public
transport
More important
Less important replace
PT
Road safety Worse-same-better
Impact on
residential and
business locations
(commuting)
Increase-same- fewer
Who is leading the
transition?
Market-government
Old Amsterdam Amsterdam
Market
Square
Amsterdam
On Demand
Hub
Amsterdam
High Medium Low Very low
Limited High High Very high
Decreases Grows Grows Decreases
Decreases Grows Grows Decreases
Public transport
to hubs more
important
Autonomous
replaces public
transport
Autonomous
replaces public
transport
Shift to self-
driving public
transport
Same Worse Worse Better
Fewer
commutes from
and to
Amsterdam
Fewer
commutes
from and to
Amsterdam
Significant
increase of
commuting
journeys
Significant
increase of
commuting
journeys
Government Market Market Government
Future RadarExample E-taxi
▪ Impact of self driving vehicles is not always beneficial to
city goals
▪ Road authorities face uncertainty about developments
▪ Future Radar is a helpful tool to discuss impact
▪ Example of E-taxi shows most likely Scenario Amsterdam
On Demand
▪ Time critical interventions can be defined
To conclude
