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Future of Urban and Autonomous Mobility: Bringing
Autonomy On and Beyond the Streets of Boston
Andrey Berdichevskiy, World Economic Forum
2
Why does the World Economic Forum care about self-driving vehicles?
1. After 10 years; Note: Potential rewards calculated for a model city of ~5M inhabitants; Source: International Organisation for Road Accident Prevention, European Parking
Association, UCS, World Economic Forum; BCG analysis
Less waiting time
— Seamless, multi-modal end-to-end
mobility can be offered to consumers
Productivity boost
— Over 1.2B hours of pure driving time
savings over 10 years possible
Reduced public transport spending
— Reduction in losses from often non-profitable public
transport service in lower density areas
Decrease in pollution
— Better fuel efficiency of ~20% can
lower overall pollution (absent an
increase in mileage)
— Even higher decrease of emissions
possible with electrification
Increased traffic efficiency
— Traffic congestion can be improved by ~70% 1
due to smoother traffic flow and fewer cars on
the road
Improved road safety
— 90% of accidents today occur due to human error
— Reduction in accidents by 70%1 feasible if self-driving
vehicles represent considerable share of car fleet
Freed up space
— Need for parking space in the city can
be reduced by up to 60%1
Rewards
Decreased cost of mobility
— Cost savings of up to 50% per km for ride
shared self-driving taxi service vs. traditional
car ownershipEquitable access to mobility
— Elderly, children and people with disabilities can
make use of new end-to-end mobility options
Individual benefits
3
Multi year Forum initiative to shape the future of urban mobility
— Develop vision for mobility
and strategy for autono-
mous vehicles (AV)
— Support the set-up of AV
testing
ImplementationDetailed analysis
2014 2015 2016
Annual
Meeting 2016
Annual
Meeting 2017
Benchmarking of
traffic management
concept in leading
cities
Traffic management
Customer
research
SDVs: consumer acceptance
Self-driving vehicle (SDVs)
City policy
maker
interviews
SDVs: city perspective
Analysis of
obstacles in regu-
lation, society,
and technology
Benchmarking of
currently available
implementations
Intermodal travel assistants
Future urban mobility
scenarios
Scenarios for
urban mobility
Source: World Economic Forum; BCG analysis
— Extend AV strategy to
include goods mobility
— Contribute to a network of
cities to share best
practices and key learnings
on the global level
Shape future urban mobility globally
in cooperation with the City of Boston
4
The City of Boston realized first tests in 4 months, now three AV
partners approved
Dec 2016 End Jan 2017Sep 2016 Jan 2017Oct 2016 Mar 2017Nov 2016 Jun 2017 Jun 2017
State and City
issue AV
executive
orders
State approves
nuTonomy tests
Off-road tests
completed
Phase B1
completed
100 miles
completed
Collaboration
announced to
work on user
interface /
experience
Source: World Economic Forum, City of Boston, MassDOT, nuTonomy
AV initiative
announced MoU signed
On-road
testing kicks
off Jan 4th
Phase B2
completed and
C1 launched
200 miles
completed
Second and
third testing
partners
announced
5
A phased incubator approach is instrumental to scale technology and
business model trials in a controllable environment
Detail testing site for phase C1
Raymond Flynn Marine Park
Seaport District
Source: World Economic Forum; nuTonomy, MassDOT, City of Boston, BCG analysis
Off-site testing
100 miles, Marine Industrial
Park, day time only,
good weather
100 miles, Marine Industrial
Park, day and night time,
mixed weather
200 miles, South Boston
Waterfront, day time only,
good weather
200 miles in South Boston
Waterfront, day and night,
mixed weather
400 miles in City of Boston ,
day time only,
good weather
City of Boston
day and night time,
mixed weather
Testing phases
A
B1
B2
C1
C2
D1
D2
6
Impact simulation: We built a real-life traffic model for downtown Boston
Custom
House
Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab
We took a real world environment
incorporating geospatially accurate data …
City Hall
Plaza
Boston
Common
Financial
District
North
End
Faneuil
Hall
Downtown
Crossing
Haymarket
Old
State
House
Post Office
Square
BOSTON
Downtown
Study Area
0.45 km² study area
12.6 km of streets
22.8 km of sidewalks
12 bus routes
53 traffic signals
Minibuses
Taxis
Buses
Cars
Following distance
Traffic lights
Speed
Streets
Traffic rules Capacity
… and simulated traffic flows in its streets
Traffic
participants
Dynamic
behaviors
Environment
and infra-
structure
Pedestrians
7
We looked at one evolutionary and one revolutionary scenario
Scenario
description
Primary
transport
modes
Private Car Evolution Robo-Transport Revolution
Today's status quo in Boston
downtown study area
— Most trips into and out of
study area are work
commutes
— Public transit and personal
car as key transport modes
Boston today
Shift to autonomous technology
with increased sharing
— Many car owners switch to self-
driving cars or using shared
self-driving taxis
— Some public transit shifts to
shared taxi
Disruptive shift to shared,
autonomous transportation
— Shift from personal car to
(shared) self-driving taxi
and minibus
— Considerable shift from
public transit to minibus
Note: Model assumes simplified modal mix without walking and cycling. Boston today modal mix representative of study area only. Modal mix expressed as % of trips taken
Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab
1. Public transit: 56%
2. Personal car: 33%
3. Taxi and e-hailing: 11%
1. Public transit: 50%
2. Shared self-driving taxi: 22%
3. Self-driving personal car: 11%
4. Traditional personal car: 11%
1. Public transit: 34%
2.Self-driving mini-bus: 28%
3. Self-driving taxi: 24%
4. Shared self-driving taxi: 14%
8
Number of vehicles: Considerable reduction due to sharing
0
1,000
400
200
600
800
1,200
1,400
1,600
1,800
Robo-transport revolutionPrivate car evolutionBase case
Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab
Total number of vehicles moving in
the study area
12am 12am6am 12pm 6pm
Private car evolution -11%
— Increase in shared self-driving taxis from personal car
— Counterbalanced by shift from bus to shared self-
driving taxi (occupancy of 2-3 PAX)
Robo-transport revolution -28%
— Strong increase in use of shared modes
— Slightly counterbalanced by bus trips shifting to
smaller capacity minibuses (occupancy 2-15 PAX)
9
Average travel speed: Significant improvements of up to 30% possible
0
5
10
15
20
25
30
35
40
45
50
55
60
65
Robo-transport revolutionPrivate car evolutionBase case
Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab
Total average speed in km/h for
private cars
12am 12am6am 12pm 6pm
Private car evolution +15%
— Driven by small reduction in the number of vehicles (-11%)
— Lower safety distance between autonomous vehicles
— Traffic flow is improved
Robo-transport revolution +30%
— Driven by strong reduction in the number of vehicles (-28%)
— Large share of autonomous vehicles with lower safety
distance
— Traffic flow is considerably improved
10
In both scenarios we found significant impact along key KPIs
Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab
Private car evolution
Parking space needed
Impact
Robo-transport
revolution
Scenario
-42%
-16%
-11%
+13%
-11% -28%
-30%
-66%
-48%
+6%
CO2 emissions
Average travel time
Vehicle distance traveled
Number of vehicles
11
Six key takeaways from the collaboration with the City of Boston
Autonomous vehicles enhance, but do not replace public transit
Autonomous vehicles are a crucial building block to make
transportation more accessible, safe and reliable
Boston envisions one city-wide mobility platform where all its mobility
offers converge
Experimentation with different industry partners key to learn, always in
close cooperation with state level
Majority of cities do not want to own assets for shared mobility models
Getting used to AVs takes time—public's awareness for them must be
created early on
Source: World Economic Forum; BCG analysis; City of Boston
1
2
3
4
5
6
12
Outlook: The project focusses on 5 key topics in 2017
Analyze new
business models
for urban goods
delivery
Facilitate a best and worst practice on autonomy leveraging digital capabilities
and existing city networks & collaborations
Urban logistics
models
City
Network
1
5
Develop framework
for city mobility
platform
Mobility
Platform
4
AV testing
Assist in expanding
autonomous
vehicles testing in
Boston
2
Broaden scope of
traffic simulation
Impact study
AVs
3
Contact Information
Andrey Berdichevskiy
Practice Lead Urban Mobility
Global Leadership Fellow
World Economic Forum
Tel.: +1 646 203 2755
E-mail: abed@weforum.org
Twitter: @andreyberd
© 2017 World Economic Forum. All rights reserved. No part of this
publication may be reproduced or transmitted in any form or by any means,
including photocopying and recording, or by any information storage and
retrieval system.

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Future of Urban and Autonomous Mobility: Bringing Autonomy On and Beyond the Streets of Boston

  • 1. Future of Urban and Autonomous Mobility: Bringing Autonomy On and Beyond the Streets of Boston Andrey Berdichevskiy, World Economic Forum
  • 2. 2 Why does the World Economic Forum care about self-driving vehicles? 1. After 10 years; Note: Potential rewards calculated for a model city of ~5M inhabitants; Source: International Organisation for Road Accident Prevention, European Parking Association, UCS, World Economic Forum; BCG analysis Less waiting time — Seamless, multi-modal end-to-end mobility can be offered to consumers Productivity boost — Over 1.2B hours of pure driving time savings over 10 years possible Reduced public transport spending — Reduction in losses from often non-profitable public transport service in lower density areas Decrease in pollution — Better fuel efficiency of ~20% can lower overall pollution (absent an increase in mileage) — Even higher decrease of emissions possible with electrification Increased traffic efficiency — Traffic congestion can be improved by ~70% 1 due to smoother traffic flow and fewer cars on the road Improved road safety — 90% of accidents today occur due to human error — Reduction in accidents by 70%1 feasible if self-driving vehicles represent considerable share of car fleet Freed up space — Need for parking space in the city can be reduced by up to 60%1 Rewards Decreased cost of mobility — Cost savings of up to 50% per km for ride shared self-driving taxi service vs. traditional car ownershipEquitable access to mobility — Elderly, children and people with disabilities can make use of new end-to-end mobility options Individual benefits
  • 3. 3 Multi year Forum initiative to shape the future of urban mobility — Develop vision for mobility and strategy for autono- mous vehicles (AV) — Support the set-up of AV testing ImplementationDetailed analysis 2014 2015 2016 Annual Meeting 2016 Annual Meeting 2017 Benchmarking of traffic management concept in leading cities Traffic management Customer research SDVs: consumer acceptance Self-driving vehicle (SDVs) City policy maker interviews SDVs: city perspective Analysis of obstacles in regu- lation, society, and technology Benchmarking of currently available implementations Intermodal travel assistants Future urban mobility scenarios Scenarios for urban mobility Source: World Economic Forum; BCG analysis — Extend AV strategy to include goods mobility — Contribute to a network of cities to share best practices and key learnings on the global level Shape future urban mobility globally in cooperation with the City of Boston
  • 4. 4 The City of Boston realized first tests in 4 months, now three AV partners approved Dec 2016 End Jan 2017Sep 2016 Jan 2017Oct 2016 Mar 2017Nov 2016 Jun 2017 Jun 2017 State and City issue AV executive orders State approves nuTonomy tests Off-road tests completed Phase B1 completed 100 miles completed Collaboration announced to work on user interface / experience Source: World Economic Forum, City of Boston, MassDOT, nuTonomy AV initiative announced MoU signed On-road testing kicks off Jan 4th Phase B2 completed and C1 launched 200 miles completed Second and third testing partners announced
  • 5. 5 A phased incubator approach is instrumental to scale technology and business model trials in a controllable environment Detail testing site for phase C1 Raymond Flynn Marine Park Seaport District Source: World Economic Forum; nuTonomy, MassDOT, City of Boston, BCG analysis Off-site testing 100 miles, Marine Industrial Park, day time only, good weather 100 miles, Marine Industrial Park, day and night time, mixed weather 200 miles, South Boston Waterfront, day time only, good weather 200 miles in South Boston Waterfront, day and night, mixed weather 400 miles in City of Boston , day time only, good weather City of Boston day and night time, mixed weather Testing phases A B1 B2 C1 C2 D1 D2
  • 6. 6 Impact simulation: We built a real-life traffic model for downtown Boston Custom House Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab We took a real world environment incorporating geospatially accurate data … City Hall Plaza Boston Common Financial District North End Faneuil Hall Downtown Crossing Haymarket Old State House Post Office Square BOSTON Downtown Study Area 0.45 km² study area 12.6 km of streets 22.8 km of sidewalks 12 bus routes 53 traffic signals Minibuses Taxis Buses Cars Following distance Traffic lights Speed Streets Traffic rules Capacity … and simulated traffic flows in its streets Traffic participants Dynamic behaviors Environment and infra- structure Pedestrians
  • 7. 7 We looked at one evolutionary and one revolutionary scenario Scenario description Primary transport modes Private Car Evolution Robo-Transport Revolution Today's status quo in Boston downtown study area — Most trips into and out of study area are work commutes — Public transit and personal car as key transport modes Boston today Shift to autonomous technology with increased sharing — Many car owners switch to self- driving cars or using shared self-driving taxis — Some public transit shifts to shared taxi Disruptive shift to shared, autonomous transportation — Shift from personal car to (shared) self-driving taxi and minibus — Considerable shift from public transit to minibus Note: Model assumes simplified modal mix without walking and cycling. Boston today modal mix representative of study area only. Modal mix expressed as % of trips taken Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab 1. Public transit: 56% 2. Personal car: 33% 3. Taxi and e-hailing: 11% 1. Public transit: 50% 2. Shared self-driving taxi: 22% 3. Self-driving personal car: 11% 4. Traditional personal car: 11% 1. Public transit: 34% 2.Self-driving mini-bus: 28% 3. Self-driving taxi: 24% 4. Shared self-driving taxi: 14%
  • 8. 8 Number of vehicles: Considerable reduction due to sharing 0 1,000 400 200 600 800 1,200 1,400 1,600 1,800 Robo-transport revolutionPrivate car evolutionBase case Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab Total number of vehicles moving in the study area 12am 12am6am 12pm 6pm Private car evolution -11% — Increase in shared self-driving taxis from personal car — Counterbalanced by shift from bus to shared self- driving taxi (occupancy of 2-3 PAX) Robo-transport revolution -28% — Strong increase in use of shared modes — Slightly counterbalanced by bus trips shifting to smaller capacity minibuses (occupancy 2-15 PAX)
  • 9. 9 Average travel speed: Significant improvements of up to 30% possible 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Robo-transport revolutionPrivate car evolutionBase case Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab Total average speed in km/h for private cars 12am 12am6am 12pm 6pm Private car evolution +15% — Driven by small reduction in the number of vehicles (-11%) — Lower safety distance between autonomous vehicles — Traffic flow is improved Robo-transport revolution +30% — Driven by strong reduction in the number of vehicles (-28%) — Large share of autonomous vehicles with lower safety distance — Traffic flow is considerably improved
  • 10. 10 In both scenarios we found significant impact along key KPIs Source: World Economic Forum; BCG analysis in cooperation with MIT Media Lab Private car evolution Parking space needed Impact Robo-transport revolution Scenario -42% -16% -11% +13% -11% -28% -30% -66% -48% +6% CO2 emissions Average travel time Vehicle distance traveled Number of vehicles
  • 11. 11 Six key takeaways from the collaboration with the City of Boston Autonomous vehicles enhance, but do not replace public transit Autonomous vehicles are a crucial building block to make transportation more accessible, safe and reliable Boston envisions one city-wide mobility platform where all its mobility offers converge Experimentation with different industry partners key to learn, always in close cooperation with state level Majority of cities do not want to own assets for shared mobility models Getting used to AVs takes time—public's awareness for them must be created early on Source: World Economic Forum; BCG analysis; City of Boston 1 2 3 4 5 6
  • 12. 12 Outlook: The project focusses on 5 key topics in 2017 Analyze new business models for urban goods delivery Facilitate a best and worst practice on autonomy leveraging digital capabilities and existing city networks & collaborations Urban logistics models City Network 1 5 Develop framework for city mobility platform Mobility Platform 4 AV testing Assist in expanding autonomous vehicles testing in Boston 2 Broaden scope of traffic simulation Impact study AVs 3
  • 13. Contact Information Andrey Berdichevskiy Practice Lead Urban Mobility Global Leadership Fellow World Economic Forum Tel.: +1 646 203 2755 E-mail: abed@weforum.org Twitter: @andreyberd © 2017 World Economic Forum. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, including photocopying and recording, or by any information storage and retrieval system.