SlideShare a Scribd company logo
1 of 23
Download to read offline
micromobility
when
attacks
US National Household Travel Survey 2017
VEHICLE TRIPS 220,430,000,000
VEHICLE MILES OF TRAVEL (VMT) 2,105,882,000,000 mi
PERSON TRIPS 371,152,000,000
PERSON MILES OF TRAVEL (PMT) 3,970,287,000,000 mi
2.1
Trillion miles
3.9
Trillion miles
Car Trip Distance Distribution (US) (n=748,918)
OneWayTrips(%)
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
Trip Distance (miles)
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79
One-way Trips (%)
Lognormal approximation
𝝻 Location parameter 1.65
𝞂 scale parameter 0.95
Arithmetic Mean 8.2
Median 5.2
Mode 2.1
Arithmetic Std. Dev. 7.8
Geometric Mean 5.21
Geometric Std. Dev 2.59
2/3 Min 2.01
2/3 Max 13.46
95% Min 0.78
95% Max 34.81
US Car
Miles/trip
(MEASURED)
TripsProbability
0
5
10
15
20
0 1.5 3 4.5 6 7.5 9 10.5 12 13.5 15 16.5 18 19.5
Trip Distance Probability
Log-normal Approximation
NYC Taxi
New York CITI Bike n = 42.7 million
Miles/trip
TripsProbability
Probability Density Functions
0
0.03
0.06
0.09
0.12
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
Log-normal Approximations
0
0.03
0.06
0.09
0.12
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
Miles/trip
(MEASURED, EUCLIDEAN)
New York CITI Bike n = 42.7 million
(MEASURED, EUCLIDEAN)
TripsProbability
0
0.03
0.06
0.09
0.12
Miles
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
0
0.03
0.06
0.09
0.12
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
TripsProbability0
0.024
0.048
0.072
0.096
0.12
-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3
0
0.024
0.048
0.072
0.096
0.12
-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3
ln(Distance)
Boston Hubway n = 3.3 million
Miles/trip
TripsProbability
Miles/trip
(MEASURED, EUCLIDEAN)
Probability Density Functions
0
0.04
0.08
0.12
0.16
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
Log-normal Fit
0
0.025
0.05
0.075
0.1
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
Chicago Divvy n = 11.5 million
Miles/trip
TripsProbability
Miles/trip
(MEASURED, EUCLIDEAN)
Probability Density Functions
0
0.025
0.05
0.075
0.1
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
Log-normal Fit
0
0.025
0.05
0.075
0.1
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
DC Capital Bike Share n = 5.7 million
Miles/trip
TripsProbability
Miles/trip
(MEASURED, EUCLIDEAN)
Probability Density Functions
0
0.023
0.045
0.068
0.09
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
Log-normal Approximations
0
0.023
0.045
0.068
0.09
0.1
0.6
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
5.6
6.1
6.6
7.1
7.6
Zürich E-Bike Sharing
Miles/trip
TripsProbability
Miles/trip
(MEASURED, EUCLIDEAN)
0
0.018
0.035
0.053
0.07
0.1
0.5
0.9
1.3
1.7
2.1
2.5
2.9
3.3
3.7
4.1
4.5
4.9
5.3
5.7
6.1
6.5
6.9
7.3
7.7
8.1
8.5
8.9
9.3
9.7
PDF
0
0.018
0.035
0.053
0.07
0.1
0.5
0.9
1.3
1.7
2.1
2.5
2.9
3.3
3.7
4.1
4.5
4.9
5.3
5.7
6.1
6.5
6.9
7.3
7.7
8.1
8.5
8.9
9.3
9.7
Log-Normal Approximation Z Bikes
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2017-07-NE
2017-06-NE
2017-05-NE
2017-04-NE
2017-03-NE
2017-02-NE
2017-01-NE
2016-12-NE
2016-11-NE
2016-10-NE
2016-09-N
2016-08-N
2016-07-NE
2016-06-N
2016-04-NE
2016-03-N
2016-02-N
2016-01-NE
2015-12-NE
2015-11-NE
2015-10-NE
2015-09-N
2015-08-N
2015-07-NE
2015-06-N
2015-05-N
2015-04-NE
2015-03-N
2015-02-N
2015-01-NE
2014-12-NE
2014-11-NE
2014-09-N
2014-08-N
2014-07-NE
2014-06-N
2014-05-N
2014-03-N
2014-02-N
2014-01-NE
2013-12-NE
2013-11-NE
2013-10-NE
2013-09-N
2013-08-N
2013-07-NE
<𝝻, 𝞂> parameters Seasonal variance Boston v. NYC
𝞂
𝝻
Boston
NYC
NYC
Boston
Swiss Modal Distributions
0%
8%
15%
23%
30%
0.01.0
3.04.0
6.07.0
9.010.0
12.013.0
15.016.0
18.019.0
21.022.0
24.025.0
27.028.0
30.031.0
33.034.0
36.037.0
39.040.0
42.043.0
45.046.0
48.049.0
51.052.0
54.055.0
57.058.0
60.061.0
63.064.0
66.067.0
69.070.0
72.073.0
75.076.0
78.079.0
81.082.0
84.085.0
87.088.0
90.091.0
93.094.0
96.097.0
99.0100.0
CH 1 CHWalking CH 1
0%
4%
8%
12%
16%
0.01.0
3.04.0
6.07.0
9.010.0
12.013.0
15.016.0
18.019.0
21.022.0
24.025.0
27.028.0
30.031.0
33.034.0
36.037.0
39.040.0
42.043.0
45.046.0
48.049.0
51.052.0
54.055.0
57.058.0
60.061.0
63.064.0
66.067.0
69.070.0
72.073.0
75.076.0
78.079.0
81.082.0
84.085.0
87.088.0
90.091.0
93.094.0
96.097.0
99.0100.0
2 CHCycling 2
0%
1%
3%
4%
5%
0.01.0
3.04.0
6.07.0
9.010.0
12.013.0
15.016.0
18.019.0
21.022.0
24.025.0
27.028.0
30.031.0
33.034.0
36.037.0
39.040.0
42.043.0
45.046.0
48.049.0
51.052.0
54.055.0
57.058.0
60.061.0
63.064.0
66.067.0
69.070.0
72.073.0
75.076.0
78.079.0
81.082.0
84.085.0
87.088.0
90.091.0
93.094.0
96.097.0
99.0100.0
3 CHSmall moped 3
0%
1%
2%
2%
3%
0.01.0
3.04.0
6.07.0
9.010.0
12.013.0
15.016.0
18.019.0
21.022.0
24.025.0
27.028.0
30.031.0
33.034.0
36.037.0
39.040.0
42.043.0
45.046.0
48.049.0
51.052.0
54.055.0
57.058.0
60.061.0
63.064.0
66.067.0
69.070.0
72.073.0
75.076.0
78.079.0
81.082.0
84.085.0
87.088.0
90.091.0
93.094.0
96.097.0
99.0100.0
5 CHMotorcycle driver 5
0%
1%
2%
2%
3%
0.01.0
3.04.0
6.07.0
9.010.0
12.013.0
15.016.0
18.019.0
21.022.0
24.025.0
27.028.0
30.031.0
33.034.0
36.037.0
39.040.0
42.043.0
45.046.0
48.049.0
51.052.0
54.055.0
57.058.0
60.061.0
63.064.0
66.067.0
69.070.0
72.073.0
75.076.0
78.079.0
81.082.0
84.085.0
87.088.0
90.091.0
93.094.0
96.097.0
99.0100.0
7 CHCar driver 7
0%
1%
2%
2%
3%
0.01.0
3.04.0
6.07.0
9.010.0
12.013.0
15.016.0
18.019.0
21.022.0
24.025.0
27.028.0
30.031.0
33.034.0
36.037.0
39.040.0
42.043.0
45.046.0
48.049.0
51.052.0
54.055.0
57.058.0
60.061.0
63.064.0
66.067.0
69.070.0
72.073.0
75.076.0
78.079.0
81.082.0
84.085.0
87.088.0
90.091.0
93.094.0
96.097.0
99.0100.0
8 CHCar passenger 8
Addressable Market in Trip Distances for Various Modes
0.1mi.
1mi.
10mi.
100mi.
1000mi.
10000mi.
WalkUK
ZHWalking
ikes(NYCCitiBike)
-bike(NereZurich)
ZHschoolbus
ZHCycling
ZHTram/metro
NYCTaxi
CHWalking
CHTaxi
PersonalBicycle
etransport(Uber?)
BusinLondon
ZHCardriver
ZHCarpassenger
rlocalbusEngland
Taxi/minicabUK
USCar
CHMicromobility
ZHTrain
ublictransportUK
vanpassengerUK
CardriverUK
MotorcycleUK
ndonUnderground
CHCycling
ike(max.25km/h)
CHTram/metro
CHSmallmoped
CHOther
CHschoolbus
SurfaceRailUK
ar/Funicular/skilift
otorcycle(<50cc)
late(max45km/h)
Motorcycledriver
CHCardriver
torcyclepassenger
CHBus(Postauto)
CHCarpassenger
CHBoat
CHTruck
Bus(longdistance)
CHTrain
Non-localbusUK
assengerAirTravel
CHAircraft
2/3 Min
2/3 Max
Median
95% Min
95% Max
MICROMOBILITY
Car Trip Distance Distribution (US) (n=748,918)
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79
Lognormal approximation
US Car
US Car Trips <31, >30mi. Vehicle Trips Distribution (US) (n=748,918)
OneWayTrips(count)
0
10,000,000,000
20,000,000,000
30,000,000,000
40,000,000,000
50,000,000,000
60,000,000,000
70,000,000,000
80,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
US Car VMT VMT Distribution (US) (n=748,918)
VehicleMilesTraveled
0
100,000,000,000
200,000,000,000
300,000,000,000
400,000,000,000
500,000,000,000
600,000,000,000
700,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
US Car VMT Buckets VMT BUCKETS (US) (n=748,918)
VehicleMilesTraveled
0
100,000,000,000
200,000,000,000
300,000,000,000
400,000,000,000
500,000,000,000
600,000,000,000
700,000,000,000
800,000,000,000
900,000,000,000
1,000,000,000,000
1,100,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1,067,622,603,274
612,861,811,859
425,397,584,867
VMT Distribution (US) (n=748,918)
VehicleMilesTraveled
0
100,000,000,000
200,000,000,000
300,000,000,000
400,000,000,000
500,000,000,000
600,000,000,000
700,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
US Car VMT Buckets VMT BUCKETS (US) (n=748,918)
VehicleMilesTraveled
0
100,000,000,000
200,000,000,000
300,000,000,000
400,000,000,000
500,000,000,000
600,000,000,000
700,000,000,000
800,000,000,000
900,000,000,000
1,000,000,000,000
1,100,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1,067,622,603,274
1,038,259,396,726
0
100,000,000,000
200,000,000,000
300,000,000,000
400,000,000,000
500,000,000,000
600,000,000,000
700,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1.1
Trillion miles
1.0
Trillion miles
How Much Does a Mile Cost?
PRICING VMT IN NYC
$0.00
$3.00
$6.00
$9.00
$12.00
$15.00
$18.00
TRIP DISTANCE
0.5 1 1.5 2 3
$0.50
IRS DEDUCTION
Taxi
Citibike
MICROMOBILIT Y
$0.00
$0.75
$1.50
$2.25
$3.00
1 2 3 4 5 6 7 8 9 10
US Revenue Buckets Dollar BUCKETS (US) (n=748,918)
PersonDollar-Miles
0
100,000,000,000
200,000,000,000
300,000,000,000
400,000,000,000
500,000,000,000
600,000,000,000
700,000,000,000
800,000,000,000
900,000,000,000
1,000,000,000,000
1,100,000,000,000
1,200,000,000,000
1,300,000,000,000
1,400,000,000,000
1,500,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
$1,111,051,834,588
$1,408,549,860,197
0
100,000,000,000
200,000,000,000
300,000,000,000
400,000,000,000
500,000,000,000
600,000,000,000
700,000,000,000
800,000,000,000
Trip Distance (miles)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
$1.1
Trillion
$1.4
Trillion
SMALL DISTANCES IN SMALL
VEHICLES
ELECTRIC EARLY,
AUTONOMOUS LATE
AUTONOMOUS EARLY,
ELECTRIC LATE
2026: 40 million units/yr
Micromobility market will grow fastest
LARGE DISTANCES IN
LARGE VEHICLES
The Unbundling is Coming

More Related Content

What's hot

Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...
Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...
Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...Boston Consulting Group
 
MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...
MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...
MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...McKinsey & Company
 
Russian Consumers and the New Economic Reality
Russian Consumers and the New Economic RealityRussian Consumers and the New Economic Reality
Russian Consumers and the New Economic RealityBoston Consulting Group
 
Digital Consumers, Emerging Markets, and the $4 Trillion Future
Digital Consumers, Emerging Markets, and the $4 Trillion FutureDigital Consumers, Emerging Markets, and the $4 Trillion Future
Digital Consumers, Emerging Markets, and the $4 Trillion FutureBoston Consulting Group
 
A.T. Kearney Consolidation of the US Banking Industry
A.T. Kearney Consolidation of the US Banking IndustryA.T. Kearney Consolidation of the US Banking Industry
A.T. Kearney Consolidation of the US Banking IndustryKearney
 
McKinsey - Covid 19 - Global Auto Consumer Insights - November 2020
McKinsey -  Covid 19 - Global Auto Consumer Insights - November 2020McKinsey -  Covid 19 - Global Auto Consumer Insights - November 2020
McKinsey - Covid 19 - Global Auto Consumer Insights - November 2020Martin Hattrup
 
The 4th Annual New Mobility Study 2019
The 4th Annual New Mobility Study 2019The 4th Annual New Mobility Study 2019
The 4th Annual New Mobility Study 2019L.E.K. Consulting
 
World Economic Forum: The power of analytics for better and faster decisions ...
World Economic Forum: The power of analytics for better and faster decisions ...World Economic Forum: The power of analytics for better and faster decisions ...
World Economic Forum: The power of analytics for better and faster decisions ...PwC
 
The Platform Model: Mind the Myths
The Platform Model: Mind the MythsThe Platform Model: Mind the Myths
The Platform Model: Mind the MythsMirakl
 
Accenture Digital Accelerators for Public Service Transformation
Accenture Digital Accelerators for Public Service TransformationAccenture Digital Accelerators for Public Service Transformation
Accenture Digital Accelerators for Public Service Transformationaccenture
 
Smart Cities – how to master the world's biggest growth challenge
Smart Cities – how to master the world's biggest growth challengeSmart Cities – how to master the world's biggest growth challenge
Smart Cities – how to master the world's biggest growth challengeBoston Consulting Group
 
Turning big data into big revenue
Turning big data into big revenueTurning big data into big revenue
Turning big data into big revenuePwC
 
TALKWALKER - Social Media Trends 2023.pdf
TALKWALKER - Social Media Trends 2023.pdfTALKWALKER - Social Media Trends 2023.pdf
TALKWALKER - Social Media Trends 2023.pdfdigitalinasia
 
5 Opportunities in the Nutritional Supplements Industry
5 Opportunities in the Nutritional Supplements Industry5 Opportunities in the Nutritional Supplements Industry
5 Opportunities in the Nutritional Supplements IndustryL.E.K. Consulting
 
Shaping the Sustainable Organization | Accenture
Shaping the Sustainable Organization | AccentureShaping the Sustainable Organization | Accenture
Shaping the Sustainable Organization | Accentureaccenture
 
18th Annual Global CEO Survey - Technology industry key findings
18th Annual Global CEO Survey - Technology industry key findings18th Annual Global CEO Survey - Technology industry key findings
18th Annual Global CEO Survey - Technology industry key findingsPwC
 
Australia: Taking Bigger Steps | A.T. Kearney
Australia: Taking Bigger Steps | A.T. KearneyAustralia: Taking Bigger Steps | A.T. Kearney
Australia: Taking Bigger Steps | A.T. KearneyKearney
 
Unlocking First Party Data
Unlocking First Party DataUnlocking First Party Data
Unlocking First Party DataMediaPost
 

What's hot (20)

Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...
Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...
Chinese Internet Economy White Paper 2.0 - Decoding the Chinese Internet 2.0:...
 
MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...
MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...
MGI: From poverty to empowerment: India’s imperative for jobs, growth, and ef...
 
Russian Consumers and the New Economic Reality
Russian Consumers and the New Economic RealityRussian Consumers and the New Economic Reality
Russian Consumers and the New Economic Reality
 
Digital Consumers, Emerging Markets, and the $4 Trillion Future
Digital Consumers, Emerging Markets, and the $4 Trillion FutureDigital Consumers, Emerging Markets, and the $4 Trillion Future
Digital Consumers, Emerging Markets, and the $4 Trillion Future
 
A.T. Kearney Consolidation of the US Banking Industry
A.T. Kearney Consolidation of the US Banking IndustryA.T. Kearney Consolidation of the US Banking Industry
A.T. Kearney Consolidation of the US Banking Industry
 
McKinsey - Covid 19 - Global Auto Consumer Insights - November 2020
McKinsey -  Covid 19 - Global Auto Consumer Insights - November 2020McKinsey -  Covid 19 - Global Auto Consumer Insights - November 2020
McKinsey - Covid 19 - Global Auto Consumer Insights - November 2020
 
The 4th Annual New Mobility Study 2019
The 4th Annual New Mobility Study 2019The 4th Annual New Mobility Study 2019
The 4th Annual New Mobility Study 2019
 
World Economic Forum: The power of analytics for better and faster decisions ...
World Economic Forum: The power of analytics for better and faster decisions ...World Economic Forum: The power of analytics for better and faster decisions ...
World Economic Forum: The power of analytics for better and faster decisions ...
 
The Platform Model: Mind the Myths
The Platform Model: Mind the MythsThe Platform Model: Mind the Myths
The Platform Model: Mind the Myths
 
The Electric Car Tipping Point
The Electric Car Tipping PointThe Electric Car Tipping Point
The Electric Car Tipping Point
 
Accenture Digital Accelerators for Public Service Transformation
Accenture Digital Accelerators for Public Service TransformationAccenture Digital Accelerators for Public Service Transformation
Accenture Digital Accelerators for Public Service Transformation
 
Smart Cities – how to master the world's biggest growth challenge
Smart Cities – how to master the world's biggest growth challengeSmart Cities – how to master the world's biggest growth challenge
Smart Cities – how to master the world's biggest growth challenge
 
Turning big data into big revenue
Turning big data into big revenueTurning big data into big revenue
Turning big data into big revenue
 
TALKWALKER - Social Media Trends 2023.pdf
TALKWALKER - Social Media Trends 2023.pdfTALKWALKER - Social Media Trends 2023.pdf
TALKWALKER - Social Media Trends 2023.pdf
 
5 Opportunities in the Nutritional Supplements Industry
5 Opportunities in the Nutritional Supplements Industry5 Opportunities in the Nutritional Supplements Industry
5 Opportunities in the Nutritional Supplements Industry
 
Shaping the Sustainable Organization | Accenture
Shaping the Sustainable Organization | AccentureShaping the Sustainable Organization | Accenture
Shaping the Sustainable Organization | Accenture
 
Onopia - Business Model de Number26 / N26
Onopia - Business Model de Number26 / N26Onopia - Business Model de Number26 / N26
Onopia - Business Model de Number26 / N26
 
18th Annual Global CEO Survey - Technology industry key findings
18th Annual Global CEO Survey - Technology industry key findings18th Annual Global CEO Survey - Technology industry key findings
18th Annual Global CEO Survey - Technology industry key findings
 
Australia: Taking Bigger Steps | A.T. Kearney
Australia: Taking Bigger Steps | A.T. KearneyAustralia: Taking Bigger Steps | A.T. Kearney
Australia: Taking Bigger Steps | A.T. Kearney
 
Unlocking First Party Data
Unlocking First Party DataUnlocking First Party Data
Unlocking First Party Data
 

Similar to When Micromobility Attacks

Local road rehabilitation program charts (1)
Local road rehabilitation program charts (1)Local road rehabilitation program charts (1)
Local road rehabilitation program charts (1)Jeffrey C. Hicks
 
1Q16 Conference Call
1Q16 Conference Call1Q16 Conference Call
1Q16 Conference CallLocaliza
 
Webcast 1 q16 ing(1)
Webcast 1 q16 ing(1)Webcast 1 q16 ing(1)
Webcast 1 q16 ing(1)Localiza
 
Scott-Macon Aerospace, Defense and Government Industry Update
Scott-Macon Aerospace, Defense and Government Industry UpdateScott-Macon Aerospace, Defense and Government Industry Update
Scott-Macon Aerospace, Defense and Government Industry UpdateMichael Papazis
 
3Q16 Webcast
3Q16 Webcast3Q16 Webcast
3Q16 WebcastLocaliza
 
Webcast 3Q16
Webcast 3Q16Webcast 3Q16
Webcast 3Q16Localiza
 
Webcast - 2Q18
Webcast - 2Q18Webcast - 2Q18
Webcast - 2Q18Localiza
 
DISA Energy Presentation Draft
DISA Energy Presentation DraftDISA Energy Presentation Draft
DISA Energy Presentation DraftVictor Morocho
 
AssumptionsAssumptions that can be changed for the caseAs the ins.docx
AssumptionsAssumptions that can be changed for the caseAs the ins.docxAssumptionsAssumptions that can be changed for the caseAs the ins.docx
AssumptionsAssumptions that can be changed for the caseAs the ins.docxrock73
 
Venda por modelos OUT 2016
Venda por modelos OUT 2016Venda por modelos OUT 2016
Venda por modelos OUT 2016Eduardo Abbas
 
Aerospace, Defense and Government Industry Update
Aerospace, Defense and Government Industry UpdateAerospace, Defense and Government Industry Update
Aerospace, Defense and Government Industry UpdateMichael Papazis
 
2010科管局企業社會責任報告
2010科管局企業社會責任報告2010科管局企業社會責任報告
2010科管局企業社會責任報告CSRone
 

Similar to When Micromobility Attacks (20)

MRM APRIL 2023.pptx
MRM APRIL 2023.pptxMRM APRIL 2023.pptx
MRM APRIL 2023.pptx
 
Call 2 t13_eng
Call 2 t13_engCall 2 t13_eng
Call 2 t13_eng
 
Local road rehabilitation program charts (1)
Local road rehabilitation program charts (1)Local road rehabilitation program charts (1)
Local road rehabilitation program charts (1)
 
Project Selection for Highway Widening: A Systemic Approach
Project Selection for Highway Widening: A Systemic ApproachProject Selection for Highway Widening: A Systemic Approach
Project Selection for Highway Widening: A Systemic Approach
 
1Q16 Conference Call
1Q16 Conference Call1Q16 Conference Call
1Q16 Conference Call
 
Webcast 1 q16 ing(1)
Webcast 1 q16 ing(1)Webcast 1 q16 ing(1)
Webcast 1 q16 ing(1)
 
Scott-Macon Aerospace, Defense and Government Industry Update
Scott-Macon Aerospace, Defense and Government Industry UpdateScott-Macon Aerospace, Defense and Government Industry Update
Scott-Macon Aerospace, Defense and Government Industry Update
 
P33_Total_Nal.pdf
P33_Total_Nal.pdfP33_Total_Nal.pdf
P33_Total_Nal.pdf
 
Revenues
RevenuesRevenues
Revenues
 
P33_Nal_Comercial.pdf
P33_Nal_Comercial.pdfP33_Nal_Comercial.pdf
P33_Nal_Comercial.pdf
 
3Q16 Webcast
3Q16 Webcast3Q16 Webcast
3Q16 Webcast
 
Webcast 3Q16
Webcast 3Q16Webcast 3Q16
Webcast 3Q16
 
Webcast - 2Q18
Webcast - 2Q18Webcast - 2Q18
Webcast - 2Q18
 
Margins
MarginsMargins
Margins
 
DISA Energy Presentation Draft
DISA Energy Presentation DraftDISA Energy Presentation Draft
DISA Energy Presentation Draft
 
AssumptionsAssumptions that can be changed for the caseAs the ins.docx
AssumptionsAssumptions that can be changed for the caseAs the ins.docxAssumptionsAssumptions that can be changed for the caseAs the ins.docx
AssumptionsAssumptions that can be changed for the caseAs the ins.docx
 
Vendas por Marca
Vendas por MarcaVendas por Marca
Vendas por Marca
 
Venda por modelos OUT 2016
Venda por modelos OUT 2016Venda por modelos OUT 2016
Venda por modelos OUT 2016
 
Aerospace, Defense and Government Industry Update
Aerospace, Defense and Government Industry UpdateAerospace, Defense and Government Industry Update
Aerospace, Defense and Government Industry Update
 
2010科管局企業社會責任報告
2010科管局企業社會責任報告2010科管局企業社會責任報告
2010科管局企業社會責任報告
 

Recently uploaded

Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...
Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...
Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...gajnagarg
 
VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...
VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...
VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...amitlee9823
 
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...amitlee9823
 
➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men 🔝pathankot🔝 Esc...
➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men  🔝pathankot🔝   Esc...➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men  🔝pathankot🔝   Esc...
➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men 🔝pathankot🔝 Esc...nirzagarg
 
What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5
What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5
What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5Bavarian Workshop
 
如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一
如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一
如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一ozave
 
Dubai Call Girls R0yalty O525547819 Call Girls Dubai
Dubai Call Girls R0yalty O525547819 Call Girls DubaiDubai Call Girls R0yalty O525547819 Call Girls Dubai
Dubai Call Girls R0yalty O525547819 Call Girls Dubaikojalkojal131
 
Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...
Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...
Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...amitlee9823
 
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...amitlee9823
 
West Bengal Factories Rules, 1958.bfpptx
West Bengal Factories Rules, 1958.bfpptxWest Bengal Factories Rules, 1958.bfpptx
West Bengal Factories Rules, 1958.bfpptxPankajBhagat45
 
➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men 🔝narsinghpur🔝 ...
➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men  🔝narsinghpur🔝  ...➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men  🔝narsinghpur🔝  ...
➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men 🔝narsinghpur🔝 ...nirzagarg
 
➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men 🔝Asansol🔝 Escorts...
➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men  🔝Asansol🔝   Escorts...➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men  🔝Asansol🔝   Escorts...
➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men 🔝Asansol🔝 Escorts...amitlee9823
 
Marathi Call Girls Santacruz WhatsApp +91-9930687706, Best Service
Marathi Call Girls Santacruz WhatsApp +91-9930687706, Best ServiceMarathi Call Girls Santacruz WhatsApp +91-9930687706, Best Service
Marathi Call Girls Santacruz WhatsApp +91-9930687706, Best Servicemeghakumariji156
 
Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...
Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...
Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...amitlee9823
 
➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men 🔝Moradabad🔝 Esc...
➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men  🔝Moradabad🔝   Esc...➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men  🔝Moradabad🔝   Esc...
➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men 🔝Moradabad🔝 Esc...amitlee9823
 
Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...
Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...
Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...gajnagarg
 
Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...
Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...
Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...amitlee9823
 
FULL NIGHT — 9999894380 Call Girls In Jagat Puri | Delhi
FULL NIGHT — 9999894380 Call Girls In Jagat Puri | DelhiFULL NIGHT — 9999894380 Call Girls In Jagat Puri | Delhi
FULL NIGHT — 9999894380 Call Girls In Jagat Puri | DelhiSaketCallGirlsCallUs
 
Why Does My Porsche Cayenne's Exhaust Sound So Loud
Why Does My Porsche Cayenne's Exhaust Sound So LoudWhy Does My Porsche Cayenne's Exhaust Sound So Loud
Why Does My Porsche Cayenne's Exhaust Sound So LoudRoyalty Auto Service
 

Recently uploaded (20)

Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...
Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...
Top profile Call Girls In dharamshala [ 7014168258 ] Call Me For Genuine Mode...
 
VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...
VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...
VVIP Mumbai Call Girls Mumbai Central Call On 9920725232 With Elite Staff And...
 
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
 
➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men 🔝pathankot🔝 Esc...
➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men  🔝pathankot🔝   Esc...➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men  🔝pathankot🔝   Esc...
➥🔝 7737669865 🔝▻ pathankot Call-girls in Women Seeking Men 🔝pathankot🔝 Esc...
 
What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5
What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5
What Does The Engine Malfunction Reduced Power Message Mean For Your BMW X5
 
如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一
如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一
如何办理麦考瑞大学毕业证(MQU毕业证书)成绩单原版一比一
 
Dubai Call Girls R0yalty O525547819 Call Girls Dubai
Dubai Call Girls R0yalty O525547819 Call Girls DubaiDubai Call Girls R0yalty O525547819 Call Girls Dubai
Dubai Call Girls R0yalty O525547819 Call Girls Dubai
 
Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...
Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...
Top Rated Call Girls South Mumbai : 9920725232 We offer Beautiful and sexy Ca...
 
(INDIRA) Call Girl Nashik Call Now 8617697112 Nashik Escorts 24x7
(INDIRA) Call Girl Nashik Call Now 8617697112 Nashik Escorts 24x7(INDIRA) Call Girl Nashik Call Now 8617697112 Nashik Escorts 24x7
(INDIRA) Call Girl Nashik Call Now 8617697112 Nashik Escorts 24x7
 
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
 
West Bengal Factories Rules, 1958.bfpptx
West Bengal Factories Rules, 1958.bfpptxWest Bengal Factories Rules, 1958.bfpptx
West Bengal Factories Rules, 1958.bfpptx
 
➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men 🔝narsinghpur🔝 ...
➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men  🔝narsinghpur🔝  ...➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men  🔝narsinghpur🔝  ...
➥🔝 7737669865 🔝▻ narsinghpur Call-girls in Women Seeking Men 🔝narsinghpur🔝 ...
 
➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men 🔝Asansol🔝 Escorts...
➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men  🔝Asansol🔝   Escorts...➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men  🔝Asansol🔝   Escorts...
➥🔝 7737669865 🔝▻ Asansol Call-girls in Women Seeking Men 🔝Asansol🔝 Escorts...
 
Marathi Call Girls Santacruz WhatsApp +91-9930687706, Best Service
Marathi Call Girls Santacruz WhatsApp +91-9930687706, Best ServiceMarathi Call Girls Santacruz WhatsApp +91-9930687706, Best Service
Marathi Call Girls Santacruz WhatsApp +91-9930687706, Best Service
 
Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...
Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...
Sanjay Nagar Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalor...
 
➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men 🔝Moradabad🔝 Esc...
➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men  🔝Moradabad🔝   Esc...➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men  🔝Moradabad🔝   Esc...
➥🔝 7737669865 🔝▻ Moradabad Call-girls in Women Seeking Men 🔝Moradabad🔝 Esc...
 
Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...
Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...
Just Call Vip call girls Amroha Escorts ☎️9352988975 Two shot with one girl (...
 
Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...
Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...
Vip Mumbai Call Girls Colaba Call On 9920725232 With Body to body massage wit...
 
FULL NIGHT — 9999894380 Call Girls In Jagat Puri | Delhi
FULL NIGHT — 9999894380 Call Girls In Jagat Puri | DelhiFULL NIGHT — 9999894380 Call Girls In Jagat Puri | Delhi
FULL NIGHT — 9999894380 Call Girls In Jagat Puri | Delhi
 
Why Does My Porsche Cayenne's Exhaust Sound So Loud
Why Does My Porsche Cayenne's Exhaust Sound So LoudWhy Does My Porsche Cayenne's Exhaust Sound So Loud
Why Does My Porsche Cayenne's Exhaust Sound So Loud
 

When Micromobility Attacks

  • 2. US National Household Travel Survey 2017 VEHICLE TRIPS 220,430,000,000 VEHICLE MILES OF TRAVEL (VMT) 2,105,882,000,000 mi PERSON TRIPS 371,152,000,000 PERSON MILES OF TRAVEL (PMT) 3,970,287,000,000 mi 2.1 Trillion miles 3.9 Trillion miles
  • 3. Car Trip Distance Distribution (US) (n=748,918) OneWayTrips(%) 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% Trip Distance (miles) 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 One-way Trips (%) Lognormal approximation 𝝻 Location parameter 1.65 𝞂 scale parameter 0.95 Arithmetic Mean 8.2 Median 5.2 Mode 2.1 Arithmetic Std. Dev. 7.8 Geometric Mean 5.21 Geometric Std. Dev 2.59 2/3 Min 2.01 2/3 Max 13.46 95% Min 0.78 95% Max 34.81 US Car
  • 4. Miles/trip (MEASURED) TripsProbability 0 5 10 15 20 0 1.5 3 4.5 6 7.5 9 10.5 12 13.5 15 16.5 18 19.5 Trip Distance Probability Log-normal Approximation NYC Taxi
  • 5. New York CITI Bike n = 42.7 million Miles/trip TripsProbability Probability Density Functions 0 0.03 0.06 0.09 0.12 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6 Log-normal Approximations 0 0.03 0.06 0.09 0.12 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6 Miles/trip (MEASURED, EUCLIDEAN)
  • 6. New York CITI Bike n = 42.7 million (MEASURED, EUCLIDEAN) TripsProbability 0 0.03 0.06 0.09 0.12 Miles 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6 0 0.03 0.06 0.09 0.12 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6 TripsProbability0 0.024 0.048 0.072 0.096 0.12 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 0 0.024 0.048 0.072 0.096 0.12 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 ln(Distance)
  • 7. Boston Hubway n = 3.3 million Miles/trip TripsProbability Miles/trip (MEASURED, EUCLIDEAN) Probability Density Functions 0 0.04 0.08 0.12 0.16 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6 Log-normal Fit 0 0.025 0.05 0.075 0.1 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6
  • 8. Chicago Divvy n = 11.5 million Miles/trip TripsProbability Miles/trip (MEASURED, EUCLIDEAN) Probability Density Functions 0 0.025 0.05 0.075 0.1 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6 Log-normal Fit 0 0.025 0.05 0.075 0.1 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6
  • 9. DC Capital Bike Share n = 5.7 million Miles/trip TripsProbability Miles/trip (MEASURED, EUCLIDEAN) Probability Density Functions 0 0.023 0.045 0.068 0.09 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6 Log-normal Approximations 0 0.023 0.045 0.068 0.09 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1 6.6 7.1 7.6
  • 10. Zürich E-Bike Sharing Miles/trip TripsProbability Miles/trip (MEASURED, EUCLIDEAN) 0 0.018 0.035 0.053 0.07 0.1 0.5 0.9 1.3 1.7 2.1 2.5 2.9 3.3 3.7 4.1 4.5 4.9 5.3 5.7 6.1 6.5 6.9 7.3 7.7 8.1 8.5 8.9 9.3 9.7 PDF 0 0.018 0.035 0.053 0.07 0.1 0.5 0.9 1.3 1.7 2.1 2.5 2.9 3.3 3.7 4.1 4.5 4.9 5.3 5.7 6.1 6.5 6.9 7.3 7.7 8.1 8.5 8.9 9.3 9.7 Log-Normal Approximation Z Bikes
  • 12.
  • 13. Swiss Modal Distributions 0% 8% 15% 23% 30% 0.01.0 3.04.0 6.07.0 9.010.0 12.013.0 15.016.0 18.019.0 21.022.0 24.025.0 27.028.0 30.031.0 33.034.0 36.037.0 39.040.0 42.043.0 45.046.0 48.049.0 51.052.0 54.055.0 57.058.0 60.061.0 63.064.0 66.067.0 69.070.0 72.073.0 75.076.0 78.079.0 81.082.0 84.085.0 87.088.0 90.091.0 93.094.0 96.097.0 99.0100.0 CH 1 CHWalking CH 1 0% 4% 8% 12% 16% 0.01.0 3.04.0 6.07.0 9.010.0 12.013.0 15.016.0 18.019.0 21.022.0 24.025.0 27.028.0 30.031.0 33.034.0 36.037.0 39.040.0 42.043.0 45.046.0 48.049.0 51.052.0 54.055.0 57.058.0 60.061.0 63.064.0 66.067.0 69.070.0 72.073.0 75.076.0 78.079.0 81.082.0 84.085.0 87.088.0 90.091.0 93.094.0 96.097.0 99.0100.0 2 CHCycling 2 0% 1% 3% 4% 5% 0.01.0 3.04.0 6.07.0 9.010.0 12.013.0 15.016.0 18.019.0 21.022.0 24.025.0 27.028.0 30.031.0 33.034.0 36.037.0 39.040.0 42.043.0 45.046.0 48.049.0 51.052.0 54.055.0 57.058.0 60.061.0 63.064.0 66.067.0 69.070.0 72.073.0 75.076.0 78.079.0 81.082.0 84.085.0 87.088.0 90.091.0 93.094.0 96.097.0 99.0100.0 3 CHSmall moped 3 0% 1% 2% 2% 3% 0.01.0 3.04.0 6.07.0 9.010.0 12.013.0 15.016.0 18.019.0 21.022.0 24.025.0 27.028.0 30.031.0 33.034.0 36.037.0 39.040.0 42.043.0 45.046.0 48.049.0 51.052.0 54.055.0 57.058.0 60.061.0 63.064.0 66.067.0 69.070.0 72.073.0 75.076.0 78.079.0 81.082.0 84.085.0 87.088.0 90.091.0 93.094.0 96.097.0 99.0100.0 5 CHMotorcycle driver 5 0% 1% 2% 2% 3% 0.01.0 3.04.0 6.07.0 9.010.0 12.013.0 15.016.0 18.019.0 21.022.0 24.025.0 27.028.0 30.031.0 33.034.0 36.037.0 39.040.0 42.043.0 45.046.0 48.049.0 51.052.0 54.055.0 57.058.0 60.061.0 63.064.0 66.067.0 69.070.0 72.073.0 75.076.0 78.079.0 81.082.0 84.085.0 87.088.0 90.091.0 93.094.0 96.097.0 99.0100.0 7 CHCar driver 7 0% 1% 2% 2% 3% 0.01.0 3.04.0 6.07.0 9.010.0 12.013.0 15.016.0 18.019.0 21.022.0 24.025.0 27.028.0 30.031.0 33.034.0 36.037.0 39.040.0 42.043.0 45.046.0 48.049.0 51.052.0 54.055.0 57.058.0 60.061.0 63.064.0 66.067.0 69.070.0 72.073.0 75.076.0 78.079.0 81.082.0 84.085.0 87.088.0 90.091.0 93.094.0 96.097.0 99.0100.0 8 CHCar passenger 8
  • 14.
  • 15. Addressable Market in Trip Distances for Various Modes 0.1mi. 1mi. 10mi. 100mi. 1000mi. 10000mi. WalkUK ZHWalking ikes(NYCCitiBike) -bike(NereZurich) ZHschoolbus ZHCycling ZHTram/metro NYCTaxi CHWalking CHTaxi PersonalBicycle etransport(Uber?) BusinLondon ZHCardriver ZHCarpassenger rlocalbusEngland Taxi/minicabUK USCar CHMicromobility ZHTrain ublictransportUK vanpassengerUK CardriverUK MotorcycleUK ndonUnderground CHCycling ike(max.25km/h) CHTram/metro CHSmallmoped CHOther CHschoolbus SurfaceRailUK ar/Funicular/skilift otorcycle(<50cc) late(max45km/h) Motorcycledriver CHCardriver torcyclepassenger CHBus(Postauto) CHCarpassenger CHBoat CHTruck Bus(longdistance) CHTrain Non-localbusUK assengerAirTravel CHAircraft 2/3 Min 2/3 Max Median 95% Min 95% Max MICROMOBILITY
  • 16. Car Trip Distance Distribution (US) (n=748,918) 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 Lognormal approximation US Car
  • 17. US Car Trips <31, >30mi. Vehicle Trips Distribution (US) (n=748,918) OneWayTrips(count) 0 10,000,000,000 20,000,000,000 30,000,000,000 40,000,000,000 50,000,000,000 60,000,000,000 70,000,000,000 80,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
  • 18. US Car VMT VMT Distribution (US) (n=748,918) VehicleMilesTraveled 0 100,000,000,000 200,000,000,000 300,000,000,000 400,000,000,000 500,000,000,000 600,000,000,000 700,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
  • 19. US Car VMT Buckets VMT BUCKETS (US) (n=748,918) VehicleMilesTraveled 0 100,000,000,000 200,000,000,000 300,000,000,000 400,000,000,000 500,000,000,000 600,000,000,000 700,000,000,000 800,000,000,000 900,000,000,000 1,000,000,000,000 1,100,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1,067,622,603,274 612,861,811,859 425,397,584,867 VMT Distribution (US) (n=748,918) VehicleMilesTraveled 0 100,000,000,000 200,000,000,000 300,000,000,000 400,000,000,000 500,000,000,000 600,000,000,000 700,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
  • 20. US Car VMT Buckets VMT BUCKETS (US) (n=748,918) VehicleMilesTraveled 0 100,000,000,000 200,000,000,000 300,000,000,000 400,000,000,000 500,000,000,000 600,000,000,000 700,000,000,000 800,000,000,000 900,000,000,000 1,000,000,000,000 1,100,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1,067,622,603,274 1,038,259,396,726 0 100,000,000,000 200,000,000,000 300,000,000,000 400,000,000,000 500,000,000,000 600,000,000,000 700,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1.1 Trillion miles 1.0 Trillion miles
  • 21. How Much Does a Mile Cost? PRICING VMT IN NYC $0.00 $3.00 $6.00 $9.00 $12.00 $15.00 $18.00 TRIP DISTANCE 0.5 1 1.5 2 3 $0.50 IRS DEDUCTION Taxi Citibike MICROMOBILIT Y $0.00 $0.75 $1.50 $2.25 $3.00 1 2 3 4 5 6 7 8 9 10
  • 22. US Revenue Buckets Dollar BUCKETS (US) (n=748,918) PersonDollar-Miles 0 100,000,000,000 200,000,000,000 300,000,000,000 400,000,000,000 500,000,000,000 600,000,000,000 700,000,000,000 800,000,000,000 900,000,000,000 1,000,000,000,000 1,100,000,000,000 1,200,000,000,000 1,300,000,000,000 1,400,000,000,000 1,500,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 $1,111,051,834,588 $1,408,549,860,197 0 100,000,000,000 200,000,000,000 300,000,000,000 400,000,000,000 500,000,000,000 600,000,000,000 700,000,000,000 800,000,000,000 Trip Distance (miles) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 $1.1 Trillion $1.4 Trillion
  • 23. SMALL DISTANCES IN SMALL VEHICLES ELECTRIC EARLY, AUTONOMOUS LATE AUTONOMOUS EARLY, ELECTRIC LATE 2026: 40 million units/yr Micromobility market will grow fastest LARGE DISTANCES IN LARGE VEHICLES The Unbundling is Coming