MIT Media Lab | Inventing a Better Future
MIT MEDIA LAB - CONFIDENTIAL
MIT Media Lab | RoboScooter Mobility-on-Demand [mod]
William J. Mitchell
Professor of Architecture and Media Arts and Sciences
Julius Akinyemi
Resident Entrepreneur, MIT Media Lab
Ryan Chin
PhD Candidate, MIT Media Lab
Claire Abrahamse
Masters Candidate, MIT Urban Planning
Dimitris Papanikolaou
Research Scientist, MIT Media Lab
Michael Chia-Liang Lin
Masters Candidate, MIT Media Lab
mod Who Are We?
MIT
Global Innovation and Technology Research Leader, Known worldwide for building a better future for humanity.
MIT Media Lab
Media Technology Incubation Center (projects include: Robotics,
One-Laptop-Per-Child, Wearable Computing, Intelligent
Prostheses, and Artificial Intelligence).
Smart Cities
MIT Media Lab research home of the RoboScooter led by Prof.
William J. Mitchell
Global Problems World Population Estimates
1.
50% of Global Population – Currently live in dense urban areas (red line)
2.
Increased Urban Densification – Urbanization trend will continue for the
foreseeable future (rural populations will flatten and decrease)
3.
Increased Inefficient Energy Use – leading to climate change
Current Challenges South Africa
Transportation Underdeveloped public transportation system and massive
congestion
Environment
Carbon emissions and other forms of pollution created by extensive
use of private gasoline powered 2 and 4-wheeled vehicles
Socio-economic Post-apartheid economic class division and the need to eradicate
poverty in compliance with the Millennium Development Goals.
Current Challenges South Africa
Transportation
Over the past 10 years, South Africa has seen its major cities sprawl, their
populations grow, and traffic on the roads and rail networks explode. Existing public
transportation infrastructure is being stretched beyond capacity and congestion on roads has
increased.
Extensive World Cup transportation plans are in place for moving players, the FIFA family,
visitors and local citizens to venues, however, flexible movement options for visitors to enjoy the
wider urban area need to be provided without significantly adding to existing congestion.
Current Challenges South Africa
Environment
Carbon emissions and other forms of pollution are being created
by the extensive use of private gasoline-powered vehicles: the majority of urban
commuters use minibus taxis, and car ownership has increased dramatically with
the growth of the middle class.
During the World Cup, the additional transportation requirements could
dramatically increase the dependency on petrol and diesel, greatly aggravating air
pollution. This could negatively impact the athletes, as was seen at the Beijing
Olympics in 2008.
Current Challenges South Africa
Socio-economic As transportation infrastructure was used as a major divisional
urban element by apartheid urban planners, transportation systems often continue
to reinforce unequal levels of access to opportunity for most South Africans.
The investment in transportation infrastructure for the World Cup must grasp the
unique opportunity to redress the prejudiced movement systems of the past, to not
only meet transportation requirements during the tournament, but to leave a
positive, lasting legacy, a sustainable infrastructure for all South Africans that outlives the World Cup.
Our Solution A Mobility-on-Demand System
1. Fleet of shared-use low cost, lightweight electric vehicles placed at charging stations
distributed throughout the city
2. Users pick up and drop off at any station (one-way rental)
3. Unique folding electric vehicles designed specifically for one-way sharing
4. Management system dynamically manages demand and vehicle supply
The RoboScooter
Clean, Green Mobility for Today’s Crowded Cities
The RoboScooter: A Folding Electric Scooter
Mobility-on-Demand Fleet Management and IT Network
RoboScooter
Station B (Rack & Kiosk)
RoboScooter
(GPS enabled)
Mobility-on-Demand
Network
management engine
Scooter Station A (Rack & Kiosk)
Personal Electronic Devices
mod Solving the Social Issues
Democratizing Mobility
Achieve social equity by creating mobility access
No longer restrict transportation to those who can
afford expensive form of private vehicle
Creating a Scooter Economy
Create local employment to develop
advanced electric scooter product
Potential to export product to other countries
Creating an Efficient Economy
Integrates various modes of transportation
to optimally reduce mean travel time, volatility
of travel time, cost of transportation, and
frequency of congestion
Creating a Greener Economy
Reduce absolute amount of energy consumption
from increased utilization of public transportation
and eliminating the need to look for parking
Reduce gasoline pollutant level within the city
Potential to significantly reduce greenhouse gas
emission from alternative power generation
RoboScooter Application in South Africa
Local Knowledge
Research into urban mobility problems of South African Cities
Current Problems
1. Access to transportation systems is unequal, reflecting the urban
segregation of people during apartheid.
2. The state of the current infrastructure is poor, with roads
becoming increasingly congested, and busses and
trains overcrowded, badly maintained and often unsafe.
3. 65% of all daily commutes are by minibus taxis, which
are not yet regulated and thus are often unroadworthy,
overcrowded and exploit passengers. Taxi violence has
been a significant urban problem over the last 10 years.
4. Intra-urban transport systems are poor, and people are
not able to easily, safely and cheaply travel around the
major cities, particularly at night.
5. Tourists must rely on private touring company vehicles,
restricting the distribution of “tourist investment” in cities.
Specific Solution
RoboScooter Mobility-on-Demand
How it fits at all levels
Works in conjunction with Mass Transit Systems, Private, and
Ad Hoc vehicle networks
Case Study
Cape Town (see next)
RoboScooter Application in South Africa
Cape Town Use Case
City Bowl: Busses, trains and taxis only go to the edge
of the City of Cape Town, leaving poor connections to
the rest of the city and to the new "Greenpoint"
Stadium.
City Bowl: Access and connections from the Station to
the major attractions and public services of the city are
inconsistent and unreliable. Gradients often make
walking difficult, particularly for the elderly.
RoboScooter Application in South Africa
Cape Town Use Case
City Bowl: RoboScooter to provide inner-city
connections between tourist spots, hotels, civic
facilities and World Cup venues.
"Greenpoint": Mobility-on-demand systems to be
incorporated into existing transport infrastructure and
urban structure.
RoboScooter Solving the Social Issues
Social Equity
Mobility-on-Demand creates mobility access for all by connecting to current
transportation and local nodes, but allowing for greater flexibility of movement to
bridge transport infrastructure barriers and start to meaningfully integrate the city.
Democratizing Mobility
As RoboScooter and the Mobility-on-Demand system will support local public
transportation – both formal and informal – but also has the flexibility to solve the ‘last
mile problem’ (getting from the transport stop to your destination), it serves to make
public transportation more attractive to all citizens, helping to achieve the critical
mass of users required in providing a world-class transport system for all.
Creating a Scooter Economy
With vehicle manufacturing and assemblage skills in centers such as Port Elizabeth
and Johannesburg, there is a possibility for manufacturing scooters for the African
market, as well as the global market. The current hike in fuel prices has also lead to
the creation of a greater market for scooters in South African cities, which could be
regulated and grown by the DoT through the Mobility-on-Demand project.
What Are Others doing?
Bicycles
Shared-Use Systems like Velib are spreading globally
Public Transit
Incremental improvement of subway systems and implementation
of bus rapid transit (BRT) systems
Private Autos
Incremental improvement in energy efficient vehicles
Shared Autos
Companies like Zipcar are making inroads in shared-use cars.
Existing Current Solutions & Problems
Sharing Systems: Many users share a limited number of vehicles
Two-Way Car Sharing (Zipcar):
Users pick up a car from a station and return it to the same station
Problems:
Non-Flexible
Needs too much parking space
Pollution
One-Way Bike Sharing (Velib):
Users pick up a bike from a station and return it to any other station.
Problems:
Bad Management: many stations get empty, while many other stations saturate
(needs trucks to redistribute fleet)
Truck redistribution is not easily applicable to heavier vehicles (scooters, cars)
Requires user effort (cycling can be difficult on hilly landscapes);
Our Solution Mobility-on-Demand:
A One-way Self-Correcting Electrically Powered Sharing System
1. Fleet of shared-use lightweight electric vehicles placed at charging stations distributed
throughout the city
2. Users pick up and drop off at any station (one-way rental)
3. Unique folding design to minimize parking space occupation
4. Intelligent self-correcting management system (dynamic pricing) creates incentives to users
to efficiently allocate vehicles to stations
Our Management System Self-Correcting Dynamic Pricing
Dynamic Pricing: How it Works
Guaranteed Service at any station under a fixed maximum waiting time
Trip price depends on supply and demand: Price consists from a Pick-up, Drop-off,
and Distance part. Pick-up and Drop-off parts depend on inventory needs of origin
and destination station, and may vary throughout the day
Every station knows exactly how many vehicles it currently needs based on (a) long
term forecast and (b) current net-flow and intelligently adjusts its drop-off and pick-up
prices accordingly
Price depends on how ‘far’ is the system from the desired state
System entices users to bring it to the desired state while deters them from bringing it
to an undesired state
System State - Desired System State
4
2.04
0
-0.08
0
5
10
Real System State : Current
Desired System State : Current
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Dynamic Pricing: How it Works
Dynamic Pricing | System Behavior
Parameters that affect MOD System Behavior
•Number of stations
•Number of scooters (fleet size)
•Dynamic Pricing policy (pricing formula)
•Pattern of forecasted demand schedule
•Average daily driving distances and times
Incentive
Adjustment
Incentive
State
System Response
Cycle Time
B
Incentive Adjustment
Cycle Time
System
State
System
Response
•Number, utility, availability, and price of alternative modes (buses, taxes, trams, etc.)
•Market segmentation of trip purposes (work, leisure, shopping, fitness, education,
etc)
•User profile segmentation
For every different combination of the above factors the system yields a
different average maximum service time
Dynamic Pricing: Simulation of a Station using System
Dynamics
Price Utility
Lookup Table
•Simulation time: 24h
Departure Input
Function
Utility of
Outgoing
Departure
Price
Max Waiting
Time
•Focuses only on a single rack
User Carrying
Capacity
Real Departure
Demand Rate
Turnover Rate
•Initial Demand and Supply
are independent variables.
The actual ones are affected
by price and service time
Queue
Saturation
Users in Queue
<Time>
Users
Variability
Avg Trips Per
Person
Service Rate
Demand Rate
<Max Inventory>
Time to Get a
Vehicle
Actual Service
Rate
Deviation to Other
Racks Rate
Unaffected
Arrival Rate
Arrival Input
Function
Inventory
Saturation
Avg Trip Time
Arrivals in
Transit
Vehicles in Rack
(Inventory)
Actual Arrival Rate
Departure Rate
Time to Adjust
Price
Initial Affected
Arrival Rate
Correction
Inventory
Shortfall
Rack Price
•Assumes infinite number of
vehicles, but the rack has
finite Capacity (in users queue
and vehicle inventory)
Price Adjustment Rate
Forecasted Inventory
Forecasted Netflow
Sustem's Net Profitability
from Inventory
Adjustments
Magnitude Factor
Time to Average
Forecasting
Max Inventory
Min Inventory
Desired Inventory
Utility of
Incoming
<Price Utility
Lookup Table>
Safety Inventory
Coverage
Arrival Price
•Does not model accidents,
cash flows, fleet or rack
purchases
Dynamic Pricing: Simulation Results
Departures
Arrivals
Real Departure Demand Rate
Unaffected Arrival Rate
20
40
17
32.5
14
25
11
17.5
Initial Demand Rate
8
10
0
2
4
6
8
10
12
Time (Hour)
14
16
18
20
22
24
Real Departure Demand Rate : Current5
0
2
4
6
8
10
12
Time (Hour)
14
16
18
20
22
24
Unaffected Arrival Rate : Current5
Departure Price
Arrival Price
40
40
20
20
0
0
-20
-20
-40
Price Fluctuation
-40
0
2
4
6
8
10
12
Time (Hour)
14
16
18
20
22
24
Departure Price : Current5
0
2
4
6
8
10
12
Time (Hour)
14
16
18
20
22
24
Arrival Price : Current5
Initial Affected Arrival Rate
Demand Rate
40
40
30
30
20
20
10
10
Actual Demand Rate (after
price)
0
0
0
2
4
6
8
10
12
Time (Hour)
14
16
18
20
22
0
24
2
4
6
8
10
12
Time (Hour)
14
16
18
20
22
24
Initial Affected Arrival Rate : Current5
Demand Rate : Current5
Actual Arrival Rate
Departure Rate
60
20
45
17
30
14
15
11
Service Rate
8
0
0
2
Departure Rate : Current5
4
6
8
10
12
Time (Hour)
14
16
18
20
22
24
0
2
Actual Arrival Rate : Current5
4
6
8
10
12
Time (Hour)
14
16
18
20
22
24
RoboScooter Why Us?
MIT Brand
Strong research background on Technology, Design, City Planning
Value Added
1. Foldability for Less public Space usage
2. Low Cost Manufacturing for affordability and sustenance
2a. Local Manufacturing and/or Assembly for entrepreneurial
development that will result in local economic growth.
3. Green Technology for environmental sustenance
4. Assists to meet the Millennium Development Goal of world
collaboration and eradication of poverty
5. Convenient – Easy Access for all that improves mobility
efficiency and access to jobs otherwise constrained due to lack of
affordable transportation.
6. Democratic – Easy Access to all – Rich and Poor
7. Adoptability of Stations Each “MOD” station can be adopted by
local and International companies as contribution to community
development and social responsibility.
8. Self-Sustainable reallocation of vehicles due to proprietary
unique pricing model offered by the “MOD” team.
Sanyang (SYM)— Scooter and Motorcycle Manufacturer
(Taiwan)
ITRI MIT Industrial Partner – Industrial Technology Research
Institute of Taiwan
MIT Partners
The RoboScooter | Our Investigation of the Brand
mod brand survey analysis
person
a
b
c
d
e
1
modular bike design
Green Technology
(electric motor)
latest technology
(dynamic pricing +
foldability)
clean
motorized
2
comprehensive
holistic system
solution
Dynamic Pricing
fun experience
(dynamic pricing+
gadgets)
green
Clean energy
3
proprietary fleet
management/distrib
ution system
Self Management
convenience & no
problems
silent
individual mobility
4
group has strong
expertise in
deploying solution in
societies
LOW (or
reasonable) cost
green city
convenient
public urban
investment
5
group is highly
multi-disciplinary
Experience (not just
a sharing system,
but a social
network, a dynamic
market)
social interaction
flexible
local production/job
creation
6
great reputation and
experience in terms
of dealing with
government and
investors
In-Wheel
Technology (Wow
factor)
educative role
sustainable
no fixed
infrastructure
requirements/very
low space demands
hierarchy
mod adoption
mod slogans
green mod-green mood
green modwheelers
your own green trip
modify your ride
moderate your commuting
moderate traffic
modern riding
a la mode
mod out
mod South Africa
“unity in diversity”
48M population: 80% black people
Poverty- income inequality
9 provinces
4 economic centers
Capetown
Port Elizabeth
Durban
Pretoria/ Johannesburg
The RoboScooter | Building a Future State
RoboScooter on the Street
The RoboScooter is also designed to be effective in one-way, shared use mobility systems,
similar to the one-way bicycle rental system that has recently been successfully implemented,
on a large scale, in Paris.
RoboScooter in Public Spaces
In one-way shared-use systems, RoboScooters are available in parking-and-charging racks
throughout the city. When you want to go somewhere, you just swipe your credit card to
unhook a scooter from its rack, drive it to where you want to go, and drop it off again at
another rack. It’s like having valet parking everywhere.
RoboScooter Station
The one-way shared-use model is particularly effective in highly congested urban centers, and
in synergistic combination with transit systems – where scooter racks can be located at transit
stops.
Not just RoboScooters
Mobility-on-Demand Station: CityCars and RoboScooters
Building a Future Smart City
With large-scale use, RoboScooters and CityCars throw enormous battery capacity into the
electrical grid.
Effective utilization of inexpensive, off-peak power and clean but intermittent power sources –
solar, wind, wave, etc.
A smart, distributed power generation system composed of these sources (the entire city as a
virtual power plant) minimizes transmission losses.
RoboScooter Demo
RoboScooter Robot wheel
Animation: Michael Chia-Liang Lin
The robot wheel architecture enables the RoboScooter to be produced in both one-wheel-drive
and two-wheel-drive versions. Two-wheel drive, which is complex and difficult with more
traditional location of a motor, offers many potential performance advantages.
Folding
Folding is accomplished by means of a special central pivot, which shifts the wheels in and out
of alignment as required.
Folding is automatic and powered by the wheel motors. It does not require manual effort by
the user.
Electrical Power with Automatic Battery Charging
The RoboScooter is electrically powered, which means that it is silent, and has no tailpipe
emissions.
Its batteries recharge automatically whenever it is parked in the special scooter rack that has
been designed for it.
Because it automatically recharges in this way, it does not need very long range and it does
not need to carry around a large, heavy battery pack.
RoboScooter Intelligence
The RoboScooter makes maximum use of digital control technology. This is highly synergistic
with its robot wheel architecture. Its overall effect is not only to provide excellent performance,
but also to simplify and lighten the scooter.
The RoboScooter is equipped with GPS navigation, and it provides a unique, single-screen
display for all driver information – eliminating traditional dials and indicator lights.
RoboScooter Video
Insert Video here
Video: Paula Aguilera
RoboScooter: A Fun, Inexpensive, Environmentally
Responsible Way to Get Around
RoboScooter Team (SYM, ITRI, and MIT) at Milan Motor Show
RoboScooter
at Milan Motorcycle
and Scooter Show
Nov 6-11, 2007
RoboScooter | Next Steps
Co-Develop the Mobility On Demand Implementation
Phase 1
10 to 50 Stations with 200 to 500 RoboScooters
(DoT can utilize anywhere)
World Cup 2010 Technology Demo
Phase 2
Mobility-on-Demand Deployment in Cape Town (RoboScooter)
Phase 3
Mobility-on-Demand Deployment in other South African cities
mod Implementation Concept
During World Cup
Demonstrate RoboScooter Technology with 200 to 500
RoboScooters, 10 to 20 stations
Concentrate Stations at Specific World Cup Locations
After World Cup
Create Cape Town RoboScooter Mobility-on-Demand
Optimally Redistribute Stations Throughout the City to
Year
City
Deploytment
Schedule
2009
2010
2011
Cape Town and Johannesburg
Technology Demo
at CT and JoBurg
2012
2013
2014
Cover All Major Cities of South Africa
Full-Scale Commercialization at South Africa
2015
Cover Rural Parts of South Africa
Export to Other African Countries via South Africa
Creation of Scooter Parts (SYM and Various Suppliers )
Electric Motor and Motor Controls (In Discussion with Johnson Electric in China)
Kiosks System (In Disussion with Saia-Burgess in Switzerland)
Batteries (In Discussion with Sancus Group in China)
Partnerships
Assembly of Electric Bicycle (Find Local Company and Work Force from South Africa)
Local Business / Community Networks (Restaurants, Hotels, Tourist Guides, Gas Stations, etc)
FIFA
South Africa Department of Transportation
Resources
Required
Electric Power Supply
Permission for Public Space Acquisition
2016
MIT Media Lab | Smart Cities Design Team
William J. Mitchell, Professor of Architecture and Media Arts and Sciences
Claire Abrahamse, M.S. Candidate
Ryan Chin, PhD. Candidate
Chao-Chih Chuang, MS Candidate
Charles Guan, B.S. Candidate
Itaru Hiromi, B.S. Candidate
William Lark, Jr., PhD Candidate
Michael Chia-Liang Lin, MS. Candidate
Dimitris Papanikolaou, Research Affiliate
Arthur Petron, M.S. Candidate
Raul-David “Retro” Poblano, PhD Candidate
Somnath Ray, SMarchS Candidate
Zenovia Toloudi, Harvard GSD
Website: http://cities.media.mit.edu
Contact: rchin@media.mit.edu