MIT Media Lab | Inventing a Better Future

MIT MEDIA LAB - CONFIDENTIAL

MIT Media Lab | RoboScooter Mobility-on-Demand [mod]

William J. MitchellProfessor of Architecture and Media Arts and Sciences

Julius AkinyemiResident Entrepreneur, MIT Media Lab

Ryan ChinPhD Candidate, MIT Media Lab

Claire AbrahamseMasters Candidate, MIT Urban Planning

Dimitris PapanikolaouResearch Scientist, MIT Media Lab

Michael Chia-Liang LinMasters Candidate, MIT Media Lab

mod Who Are We?

MIT Global Innovation and Technology Research Leader, Known world-wide 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 AfricaTransportation 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 out-lives 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 sharing4. Management system dynamically manages demand and vehicle supply

The RoboScooterClean, Green Mobility for Today’s Crowded Cities

The RoboScooter: A Folding Electric Scooter

Mobility-on-DemandNetwork management engine

Personal Electronic Devices

Scooter Station A (Rack & Kiosk)

RoboScooter Station B (Rack & Kiosk)

RoboScooter(GPS enabled)

Network Link

Net

wor

k Li

nk

Network Link

Network

Link

Mobility-on-Demand Fleet Management and IT Network

mod Solving the Social Issues

Democratizing Mobility Achieve social equity by creating mobility accessNo 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 productPotential to export product to other countries

Creating an Efficient Economy Integrates various modes of transportationto 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 consumptionfrom increased utilization of public transportation and eliminating the need to look for parkingReduce gasoline pollutant level within the cityPotential 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.

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 Application in South Africa Cape Town Use Case

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-FlexibleNeeds too much parking spacePollution

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 occupation4. 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

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0

2.04

4

-0.08

System State - Desired System State

Real System State : CurrentDesired System State : Current

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

•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

IncentiveState

SystemState

IncentiveAdjustment

SystemResponse

B

Incentive AdjustmentCycle Time

System ResponseCycle Time

Dynamic Pricing: Simulation of a Station using System Dynamics

Vehicles in Rack(Inventory) Departure RateActual Arrival Rate

Users in QueueService RateDemand Rate

Turnover Rate

Time to Get aVehicle

Actual ServiceRate

Max WaitingTime

Real DepartureDemand Rate

Utility ofOutgoing

Forecasted InventoryForecasted Netflow

Time to AverageForecasting

Correction

Desired Inventory

InventoryShortfall

DeparturePrice

Sustem's Net Profitabilityfrom InventoryAdjustments

Arrival Price

Price UtilityLookup Table

Min Inventory

Max Inventory

Safety InventoryCoverage

Utility ofIncoming

<Price UtilityLookup Table>

Arrivals inTransit

Initial AffectedArrival Rate

Avg Trip Time

UnaffectedArrival Rate

<Time>

Departure InputFunction

Users Avg Trips PerPerson

Variability

Arrival InputFunction

User CarryingCapacity

<Max Inventory>

InventorySaturation

Deviation to OtherRacks Rate

Rack PricePrice Adjustment Rate

Time to AdjustPrice

Magnitude Factor

QueueSaturation

•Simulation time: 24h

•Focuses only on a single rack

•Initial Demand and Supply are independent variables. The actual ones are affected by price and service time

•Assumes infinite number of vehicles, but the rack has finite Capacity (in users queue and vehicle inventory)

•Does not model accidents, cash flows, fleet or rack purchases

Dynamic Pricing: Simulation Results

Initial Demand Rate

Price Fluctuation

Actual Demand Rate (after price)

Service Rate

DeparturesReal Departure Demand Rate

20

17

14

11

8

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Real Departure Demand Rate : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Demand Rate

40

30

20

10

0

1

1

11

1 11 1

11 1 1

1 1

1

11

1 1

11

1 1 1 1 1 1 11 1 1 1 1 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Demand Rate : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Departure Rate

60

45

30

15

0

1

1

1

11

11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Departure Rate : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Departure Price

40

20

0

-20

-40

1

1

11

1 11 1

11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Departure Price : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Unaffected Arrival Rate

40

32.5

25

17.5

10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1

1 1 1 1 1 1 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Unaffected Arrival Rate : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Arrival Price

40

20

0

-20

-40

1

1

11

1 11 1

11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Arrival Price : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Initial Affected Arrival Rate

40

30

20

10

0

1

1

11

1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Initial Affected Arrival Rate : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Actual Arrival Rate

20

17

14

11

8

1

1

1

1

1

1

1 1 1 11

11

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1

1 1

1

11 1 1

0 2 4 6 8 10 12 14 16 18 20 22 24Time (Hour)

Actual Arrival Rate : Current5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Arrivals

RoboScooter Why Us?

MIT Brand Strong research background on Technology, Design, City Planning

Value Added 1. Foldability for Less public Space usage2. 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 sustenance4. 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 Poor7. 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.

MIT Partners Sanyang (SYM)— Scooter and Motorcycle Manufacturer (Taiwan)ITRI MIT Industrial Partner – Industrial Technology Research Institute of Taiwan

The RoboScooter | Our Investigation of the Brand

mod brand survey analysis

personhierarchy

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/distribution 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

mod adoption

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

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.

Animation: Michael Chia-Liang Lin

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

Video: Paula Aguilera

Insert Video here

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 stationsConcentrate Stations at Specific World Cup Locations

After World Cup Create Cape Town RoboScooter Mobility-on-Demand Optimally Redistribute Stations Throughout the City to

Year 2009 2010 2011 2012 2013 2014 2015 2016

Technology Demo at CT and JoBurg

FIFA

Electric Motor and Motor Controls (In Discussion with Johnson Electric in China)

Partnerships

Cover All Major Cities of South AfricaCity

Deploytment Schedule

Cape Town and Johannesburg Cover Rural Parts of South Africa

Creation of Scooter Parts (SYM and Various Suppliers )

Kiosks System (In Disussion with Saia-Burgess in Switzerland)

Resources Required

Assembly of Electric Bicycle (Find Local Company and Work Force from South Africa)

Local Business / Community Networks (Restaurants, Hotels, Tourist Guides, Gas Stations, etc)

Electric Power Supply

Batteries (In Discussion with Sancus Group in China)

South Africa Department of Transportation

Export to Other African Countries via South AfricaFull-Scale Commercialization at South Africa

Permission for Public Space Acquisition

William J. Mitchell, Professor of Architecture and Media Arts and Sciences Claire Abrahamse, M.S. CandidateRyan Chin, PhD. CandidateChao-Chih Chuang, MS CandidateCharles Guan, B.S. CandidateItaru Hiromi, B.S. CandidateWilliam Lark, Jr., PhD CandidateMichael Chia-Liang Lin, MS. CandidateDimitris Papanikolaou, Research AffiliateArthur Petron, M.S. CandidateRaul-David “Retro” Poblano, PhD CandidateSomnath Ray, SMarchS CandidateZenovia Toloudi, Harvard GSD

Website: http://cities.media.mit.eduContact: rchin@media.mit.edu

MIT Media Lab | Smart Cities Design Team