Page 1

the first

A User-Flocksourced Bus Intelligence System in Dhaka ---

the world

----------

by albert ching May 7, 2012 Master’s Thesis Defense


In collaboration with Stephen J. Kennedy and Muntasir Mamun Advised by Chris Zegras with the gracious help of Zia Wadud, Paul Barter, and Eran Ben-Joseph Inspired by the Kewkradong team in Dhaka as well as all the entrepreneurs promoting sustainable transport in developing Asia


research question(s)

A!

While smartphones can be designed to collect vast swaths of data, can flocks of people be organized and incentivized to collect data for a targeted period of time and place? Yes, in a big way.


research question(s)

B!

If not all data in a city can be collected by flocks, can a sampled set be useful, especially if certain behaviors are predictable? Yes, less data can become big data.


theory

context

experiment

results

1

2

3

4

ITERATIVE CITY

MOBILE MOBILITY

FLOCKSOURCING

1000 SURVEYS

future

URBAN LUNCHPAD


theory

1 ITERATIVE CITY


theory

1 ITERATIVE CITY


The future of cities is no longer held in one big plan but in a thousand little, measured strokes.

1


Cheap measurement (spatial + temporal)

1


Masterplanà Simulation à Iteration

1


WHICH CITIES WILL BENEFIT?

1


context

2 MOBILE MOBILITY


DHAKA

18 Million People 100,000 Cars <1%


JAKARTA

9 Million People 9 Million Two-Wheelers 3 Million Cars >100%


Asia Rest of the World

inflection of $5,000 per capita GDP

60.0

Sandra and Archaya (2007) motorization

80.0

Cars, trucks and person per 100 persons

Motorization

100.0

United States

Japan

40.0

20.0

Singapore

Indonesia Barter â&#x20AC;&#x153;lock-inâ&#x20AC;? line of 10% car ownership

Hong Kong -

(20.0)

Income per person (GDP per capita, $USD, inflation adjusted) 100

Bangladesh

India

1,000

China

10,000

100,000

Income


Can Owning a Cell Phone Reduce the Desire to Own a Car?

Mobile rickshaw wallah in India


Users

1

Operators

2 (Real-Time)

Marketing

User Services

Cars = aspiration

Information can improve accessibility to, comfort and safety of shared vehicles

3

(Real-Time) Operator Services Information can improve efficiency, management and profitability of shared fleets

Regulators

4 Responsive City Planning

Information can help monitor and evaluate city performance in a more precise and timely manner than ever before


GO-Jek Dial-a-Motorcycle Transport in Jakarta, August 2011


Fazilka Dial-a-Rickshaw in Punjab, August 2011


entrepreneurs

Are these business sustainable + scalable?


Constellation of Mobile-Driven Mobility Experiments

Unsustainable Navigation Congestion

On-Demand On-Demand On-Demand Real Time Arrival Info Safety Alerts Fare-Tracking

Tracking Vehicle-Security

Sustainable Real Time On-Demand Arrival Info

Bicycle Sharing

Bus Delays

Singapore Kuala Lumpur

Bangkok

Jakarta Delhi Bangalore Fazilka Dhaka

Can an outside institution accelerate experimentation?

August 2011


experiment

3 FLOCKSOURCING Guided crowdsourcing


Real-time urban data collection techniques

UBIQUITOUS SENSING

CROWDSOURCING

All the data, all the time

Some data for lots of disparate times and places

Sensors

Crowds + Sensors

Privacy Closed Expensive Data processing Only objective metrics

Gathering sufficient and relevant data


Predictability of mobility (Song, Qu, Blumm, Barabasi 2010)


Real-time urban data collection techniques

FLOCKSOURCING

Lots of data for a specific time and place Flocks + Sensors Organizing the flock Flock bias


Flocksourcing Workflow Sensors

Hardware

Platform

Involuntary Tracking

Unsmartphones

None

Incentivized Volunteers

Organized Flock

Organized Vehicles

Smartphones

Tablets

PC

Android

iPhone

Web

Software/App

Connectivity

Bluetooth

Datastorage Dataverification &analysis Visualization

main bottlenecks

MIT App Inventor

Excel

Cell network

Mobile data

Local

Cloud

Statistical Packages

Visualization APIs

Wi-Fi

Machine learning


â&#x20AC;&#x153;Launch and iterateâ&#x20AC;? co-development


Survey

Passenger Count

Bus Details


Cost Structure Sensors

$10-$15

per person per day

Hardware

$175

and rapidly declining

Software/App

Free

Connectivity

$4

per 1 GB

Datastorage

Free


Experimental Design

Dhaka

Boston

Flock size & nature

Flock size & nature

8 paid volunteers

3-8 unpaid volunteers

($10 per person per day) Organized by Kewkradong Bangladesh

($30 per data plan)

Target buses

Target buses lines

36 & 27 Lines (10 km each)

Any

Data collection target

Data collection target

100 surveys 120 one-way rides the worldâ&#x20AC;&#x2122;s first

Flocksourcing

experiment

None

Crowdsourcing


Metrics Quantitative

Qualitative

Bus Details

Survey

Bus Number Bus Destination Bus Company No. of Seats Speed

Location Time Crowding

Passenger Count Female Passenger Count

Gender Age Home Location Work Location One-Way Commute Income Phone Ownership Rider Satisfaction Biggest Complaint Riding Frequency

*Survey data linked to bus data


results

4 1000 SURVEYS


Data Collection Dash


Kb16

Individual Flock Traces

Kb10

Kb2

Kb14

Kb20

Kb7

Kb8

Kb13


research question(s)

A!

While smartphones can be designed to collect vast swaths of data, can flocks of people be organized and incentivized to collect data for a targeted period of time and place?


research question(s)

B!

If not all data in a city can be collected by flocks, can a sampled set be useful, especially if certain behaviors are predictable?


Ubiquitous Sensing High

Need More Data

Data Value

Need Less Data

Low Low

Crowdsourcing

High Predictability Dimensions of Data Itself

Dimensions of Data Collection


1

BUS CROWDING

2

BUS TRAVEL TIMES

3 BUS ROUTES


passenger count Average

Sample Size

Std Dev

%Std Dev

Min

Max

9!

27

15

15

58%

5

52

8!

36

85

9

25%

11

50

7!

32

62

11

35%

11

50

6!

38

64

12

32%

11

52

5!

41

47

11

27%

14

49

4!

33

64

14

41%

9

54

3!

30

34

12

39%

9

47

2!

23

32

16

68%

5

51

BUS 1! CROWDING

24

32

15

64%

2

51

variability

#36


empty seats Average

9! 8! 7! 6! 5! 4! 3! 2!

+16! +17! +10! +7!

(0)! +2! +8! +4!

BUS +13! 1! CROWDING

7!

8!

9!

10!

am

pm

11!

12!

1!

2!

3!

4!

5!

6!

#36


one-way commute

OVERALL 12.4 km

1!

9!

Inbound

1:01

0:52

0:59

0:49

Average 7:00

8:00

9:00

1:02

Outbound

Weekday

0:46

0:59

8:00

9:00

0:46

0:58

Average 7:00

8:00

1:03

10:00

10:00

11:00

12:00

9:00

0:52

0:59

0:49

Average 7:00

8:00

9:00

10:00 1:22

10:00

13:00

12:00

13:00

0:54

1:01

14:00

15:00

1:32

16:00

8:00

9:00

10:00

17:00

18:00

19:00

20:00

1:32

0:56

1:01

14:00

15:00

16:00

17:00

18:00

19:00

20:00

15:00

16:00

17:00

18:00

19:00

20:00

1:22 0:41 11:00

12:00

1:22

0:55

11:00

13:00

12:00

13:00

0:52

Weekend Average 7:00

11:00

0:49

0:53

BUS TRAVEL TIMES

1:22

0:54

1:42

Average 7:00 0:59

1:22

#36

11:00

14:00

1:32

0:55

1:01

14:00

15:00

16:00

17:00

18:00

19:00

20:00

15:00

16:00

17:00

18:00

19:00

20:00

0:52

12:00

13:00

14:00


BUS ROUTING


Ubiquitous Sensing

High

BUS TRAVEL TIMES

BUS CROWDING + Machine Learning

Data Value

BUS ROUTING

Low

Crowdsourcing

Low

High Predictability


BUS RIDERSHIP BUS TRAVEL TIMES

BUS CROWDING

BUS SATISFACTION

BUS ROUTING

Self-organizing flock


future The Urban Launchpad is a social-mission driven company launched to generate big data insights in places, and on problems where there is less data. URBAN ---------LUNCHPAD

launchpad


PUBLIC INFOSTRUCTURE 30 buses (position, speed)

BEST BUS MAP IN THE WORLD flock of 30, 15 days (counts)

public public public public

50 buses (position, speed)

flock of 15, 5 days (crowding)

public

Who will build?

flock of 25, 10 days (satisfaction)


OUR FIRST PRODUCT the cheapest and easiest --A BUS INTELLIGENCE

-------SERVICE IN DHAKA the world


1!

2

TECHNOLOGY + YOUR FLEET

TECHNOLOGY + OUR FLOCKS

Ongoing data collection

One-time data collection

CUSTOMERS

Private bus and mini-bus operators, Paratransit (taxis, auto-rickshaws cycle rickshaws)

City government, non-profits, academic institutions, new mobility startups, citizen groups


PRICING

$50*

$50*

per seat per month

per flock member per day

Bus tracking hardware retails in US for $8-$20K per bus

Retails to less than $3 per survey using pilot results

*50% discount if data is made open to public for mash-up

Is there a viable business model?


Collaborators Stephen Kennedy, MIT DUSP Muntasir Mamun, Kewkradong Tonmoy Saad Bin Hussain, Kewkradong Xitu Masuk Ahmed, Kewkradong Swapon, Kewkradong Chonchol Morshed Alam, Kewkradong Raian Md. Shakhawat Chowdhury, Kewkradong Mamun Bhai, Kewkradong Share My Bus Dhaka & Boston Volunteers

Mahalo!

Principal Advisors Chris Zegras, MIT Asst. Prof. of Urban Studies and Planning Zia Wadud, BUET Prof of Civil Engineering Paul Barter, NUS Asst. Prof. at LKY School of Public Policy Eran Ben-Joseph, MIT Prof. of Urban Studies and Planning Entrepreneurs Navdeep Asija, Fazilka Eco-Cabs Ravee Aahluwalia, Patiala Eco-Cabs Sundara Raman, Ideophone Anenth Guru, Ideophone Sandeep Bhaskar, Ideophone Sanjeev Garg, Delhi Cycles Atul Jain, Delhi Cycle HR Murali, Namma Cycle Anthony Tan, My Teksi Hooi Ling Tan, My Teksi Nadiem Makarim, GO-Jek Arup Chakti, NITS

Leading Thinkers Apiwat Ratanwahara, Chulalongkorn University Sorawit Narupiti, Chulalongkorn University Charisma Chowdhury, BUET Moshahida Sultana, University of Dhaka Geetam Tewari, IIT-Delhi Anvita Arora, IIT-Delhi Rajinder Ravi, cycle rickshaw expert Tri Tjahjono, Univesiti Indonesia Jamillah Mohamad, University of Malaya Advocates Debra Efroymson, Work for a Better Bangladesh Maruf Rahman, Work for a Better Bangladesh Akshay Mani, EMBARQ Madhav Pai, EMBARQ Chhavi Dhingra, GTZ-India Eric Zusman, IGES Yoga Adiwinarto, ITDP Indonesia Restiti Sekartini, ITDP Indonesia Government Anisur Rahman, Dhaka Transport and Coordination Board Rajendar Kumar, Indian Dept of Information Technology Anil Sethi, Mayor of Fazilka Prodyut Dutt, ADB India Penny Lukito, BAPPENAS Indonesia Firdaus Ali, Jakarta Water Provision Industry RD Sharma, HI-BIRD Bicycles Comfort Cab Malaysia Jacob Yeoh, Yes! 4G Mobile Internet Malaysia Pornthip Konghun, Googlers Thailand James McClure, Google Singapore Kapil Goswami, Google India


Collaborators Stephen Kennedy, MIT DUSP Muntasir Mamun, Kewkradong Tonmoy Saad Bin Hussain, Kewkradong Xitu Masuk Ahmed, Kewkradong Swapon, Kewkradong Chonchol Morshed Alam, Kewkradong Raian Md. Shakhawat Chowdhury, Kewkradong Mamun Bhai, Kewkradong Share My Bus Dhaka & Boston Volunteers Principal Advisors Chris Zegras, MIT Asst. Prof. of Urban Studies and Planning Zia Wadud, BUET Prof of Civil Engineering Paul Barter, NUS Asst. Prof. at LKY School of Public Policy Eran Ben-Joseph, MIT Prof. of Urban Studies and Planning Entrepreneurs Navdeep Asija, Fazilka Eco-Cabs Ravee Aahluwalia, Patiala Eco-Cabs Sundara Raman, Ideophone Anenth Guru, Ideophone Sandeep Bhaskar, Ideophone Sanjeev Garg, Delhi Cycles Atul Jain, Delhi Cycle HR Murali, Namma Cycle Anthony Tan, My Teksi Hooi Ling Tan, My Teksi Nadiem Makarim, GO-Jek Arup Chakti, NITS

Leading Thinkers Apiwat Ratanwahara, Chulalongkorn University Sorawit Narupiti, Chulalongkorn University Charisma Chowdhury, BUET Moshahida Sultana, University of Dhaka Geetam Tewari, IIT-Delhi Anvita Arora, IIT-Delhi Rajinder Ravi, cycle rickshaw expert Tri Tjahjono, Univesiti Indonesia Jamillah Mohamad, University of Malaya Advocates Debra Efroymson, Work for a Better Bangladesh Maruf Rahman, Work for a Better Bangladesh Akshay Mani, EMBARQ Madhav Pai, EMBARQ Chhavi Dhingra, GTZ-India Eric Zusman, IGES Yoga Adiwinarto, ITDP Indonesia Restiti Sekartini, ITDP Indonesia Government Anisur Rahman, Dhaka Transport and Coordination Board Rajendar Kumar, Indian Dept of Information Technology Anil Sethi, Mayor of Fazilka Prodyut Dutt, ADB India Penny Lukito, BAPPENAS Indonesia Firdaus Ali, Jakarta Water Provision Industry RD Sharma, HI-BIRD Bicycles Comfort Cab Malaysia Jacob Yeoh, Yes! 4G Mobile Internet Malaysia Pornthip Konghun, Googlers Thailand James McClure, Google Singapore Kapil Goswami, Google India


appendix

A


REVENUE POTENTIAL (FLEET ONLY)

$50 per seat per month

x

9,000 buses in Dhaka

5% 10% 25% 50% 75% 100%

$270K $540K $1.4M $2.7M $4.1M $5.4M

penetration rate

annual revenue


Current Bus Riders in Dhaka 16% female (of those counted)

100% with a mobile phone (18% with smartphone, 50% with internet-enabled multimedia phone)

Young, Male, Captive, Mobile, Hates Crowding 85% surveyed btwn 24-34 years

57% ride at least 5 times a week

* Potential flock bias

Happiness

2.7

Most common complaint about buses (23%)

Long waits (21%) and Too few buses (20%) were also common


Determinants of Happiness crowding Rider Happiness

slowness

Crowding and Happiness

Happiness 5.0 4.5

y = 0.0493x + 3.1012 R² = 0.21825

Significant correlation between crowding and happiness

4.0 3.5 3.0

#27

2.5 2.0

#36 (20)

Crowded

(15)

(10)

(5)

y = 0.0514x + 2.0214 R² = 0.52836

1.5 1.0

-

Full

5

10

15

Empty Seats

20

Empty


one-way commute Average OVERALL

Sample Size

Std Dev

%Std Dev

Min

Max

1:01

24

0:18

30%

0:30

1:42

Inbound

1:02

15

0:16

26%

0:46

1:42

Outbound

0:59

9

0:21

36%

0:30

1:39

Weekday

1:03

20

0:18

29%

0:30

1:42

0:53

4

0:12

23%

0:39

1:10

12.4 km

1!

9!

Weekend BUS TRAVEL TIMES

variability

#36


wi-fi bus stops 12.4 km

9!

24

11.4 km

23

8!

8.0 km

Purobi Bus Stand, Section 11

7!

30

Shewrapara Bus Stand, Shewrapara

6!

6.5 km 5.1 km

2.5 km

0.6 km

ASAUB, Agargaon

4!

38

40

BUS CROWDING

Agargaon High School, Agargaon

5!

3341

3.2 km Avg Bus Size

#36

Pallabi Model School, Pallabi

32

Asad Gate, Jatiya Sangsad Bhaban

3!

36 27

New Model Degree College, Dhanmondi

2!

Dhaka College, New Market

1!

Home Economics College, Azimpur


BUS ROUTING


Dimensions of Data Itself

Data Value

High

Data Collection Predictable Dash

Dimensions of Data Collection

Qualitative + Quantitative

Real-Time

All the Data

Ubiquitous

Flocksou

Crowdsour Analog

Low

Unpredictable

Quantitative Only

Slow-Time

Sampled


Bus Survey

1

Marketing

Transport survey on the pedestrian bridge in Mirpur 1, Jan 2012


Bus Travel Times

Weekday 2:07

Bad day

Weekend 1:50

Average

1:04 0:43

Uttara 20 km

1:47

1:25

#27

8 am 10 am

Good day

6 pm

2 (Real-Time)

User Services *Data based on 42 Rides in March 2012


Bus Speed Map Live Bus Location Map

3

(Real-Time) Operator Services


4 Responsive City Planning

Dhaka Bus Dashboard

Updated March 2012


Bus health Indicators

Accessibility

2

Current Ridership

marketing slowness

1

Rider Happiness

Future Ridership

crowding Affordability of alternatives

operator profitability


Uttara #27

#36 Pallabi

Dhanmondi

New Market

Slowness


Gazipur

Accessibility

2.5 hours Most painful commute

Uttara

Most popular commutes

Banani

Dhanmondi

1.3 hours

Average one-way commute time

Azimpur

#27


Happiness by bus company #27

3.6

BRTC

2.8

Suchona

2.3

VIP

#36

2.5 2.3

Bikolpa Safety


crowding

#27

Bigger buses = happier passengers and more women!

BRTC

52 seats per bus

2.8

Suchona

48 seats per bus

2.3

VIP

39 seats per bus

3.6


Urban data collection techniques Qualitative + Quantitative (vs. Only Quantitative)

Flocksourcing

Analog Crowd Sourcing

Real-Time (vs. Slow)

Ubiquitous Sensing All the Data (vs. Sampled)

A User-Flocksourced Bus Intelligence System - THESIS DEFENSE PRESENTATION  

Presentation for Albert Ching's MIT DUSP Master's Defense on May 7, 2012

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