Green urban delivery system with crowdsourcing using mobile phone

usage data

Thanh-Cong Dinh PhD Student

University of Trento, Italy

1

Agenda

• Motivation

• Problem description

• Framework of methodology

• Current work

• Conclusion & Future work

2

Motivation 1

3

• Urban population rise

• Congestion

• Pollution

• Reduction in the level of service

• Transportation activities 34% of CO2 emissions EPA, Inventory of U.S. Greenhouse Gas

Emissions and Sinks: 1990-2010 (2012)

Motivation 2

4

CITY AGENDA CITY GOVERNANCE

City Services People Businesses Water Communication Energy Transport

City Operations Systems

City User Systems

City Infrastructure Systems

Smart city assessment – IBM report 2009

‘Smart City’? Most rely on the use of technology and evidence to improve cities/services etc..

Motivation 3

•Urban delivery service

5

Motivation 4

• Monitoring of movement Better planning transportation

6

7

“…refers to the act of taking a task traditionally performed by an employee or contractor, and outsourcing it to an undefined (generally large) group of people (i.e., the crowd) in the form of an open call”

(Jeff Howe, 2008)

The crowd is anyone with access the internet, and the right conditions include a virtual network and motivation (Jeff Howe, 2006)

Motivation 5

Crowdsourcing

Motivation 6

• Green urban delivery with crowdsourcing

• Telecommunication data

• People moveing around the city

8

Agenda

• Motivation

• Problem description

• Framework of methodology

• Current work

• Conclusion & Future work

9

Problem Statement 1

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CDR record: • User ID • Cell-tower ID • Longitude and latitude of cell-tower • Time-stamp at which the call was made

Time t1 t2

UserID CellID LonLat Time

A 1 -4.1;5.34 t1

A 2 -3.9; 5.8 t2

Call Detail Records (CDRs)

Time t1 t2 t3

Spatial-temporal information at cell-tower level Spatial-temporal information at location level

C1

C

Problem Statement 2

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B5,

C2

A1

A2

A3 A4 B4

C3

C4

Origination O

Destination T

Movement Location Cell-tower’s coverage

D C

B C

A B

B1

B

A

P1. Human mobility patterns: • Accurately identify periodical (e.g. daily,

weekly and monthly) movements of individuals, such as A1;A2;A3;A4?

P2. Delivery with crowdsourcing model: • Mitigate CO2 emissions • Satisfy the service level

B2

B3

Agenda

• Motivation

• Problem description

• Framework of methodology

• Current work

• Conclusion & Future work

12

Framework of methodology 1

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Simulation

Travel route optimization Policies

Green urban delivery system

Understanding behavior of passenger and pedestrian

Optimization models that minimize CO2 emissions and maximize service level

Maximize benefits of stakeholders

Framework of methodology 2

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Understanding behavior human mobility: Data mining • Behavior of citizens

• Private, public vehicles • Pedestrian movement.

• Step by step applying new transportation model concepts. • Compare by simulation.

Crowds travel route optimization • Search for most common route of crowds (pedestrian, motorcycle, bicycle) who

join delivery network • Minimizes the travel time.

Integration of all transportation modes (e.g. people, public vehicle, car-sharing) • Delivery scheduling. • Multi-objectives: minimizing number of trips, maximizing service level.

Policies • Maximizing benefits of stakeholders (e.g. delivery providers, people) • Trust model: reduce risks of delivery.

Agenda

• Motivation

• Problem description

• Framework of methodology

• Current work

• Conclusion & Future work

15

D4D challenge

• Orange D4D challenge • Help address society development questions in novel • Contribute to the socio-economic development and well-being

• Call Detail Records (CDR)

• Phone calls, SMS exchanges • 5 million Orange customers in Ivory Coast • 5 Dec 2011 - 28 April 2012.

Understanding behavior human mobility

D4D dataset

• High resolution trajectories

Individual movement trajectories are approximated from the geographic location of the cell-tower/sub-prefecture during calls.

user_id connection_datetime antenna_id

437690 2011-12-10 10:51:00 980

316462 2011-12-10 16:12:00 607

Data Preprocessing

• Creating sequence of movement

Event data

• Interesting regional and national events

Date Location Event Eventy type

Dec 25, 2011 Ivory Coast Christmas Day public holiday

Jan 01, 2012 Ivory Coast New Year's Day public holiday

Apr 25, 2012 Sakre Violence attack emergency event

Dec 17-18, 2011 Yale Violence attack emergency event

.. .. .. ..

• Correlation

• Movements of

customers

• Social events

• Significant signs of events

• Location

• Time

Procedure:

1) Find abnormal cell-towers.

2) Check significant signs.

Analysis of human mobility

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users’ trajectory

cell-tower

abnormal

monitoring

Events

Analysis of human mobility Tota

l nu

mbe

r of

dis

pla

cem

en

ts

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Analysis of human mobility

Vio

len

ce

Ch

ristm

as

New

Year

Carn

ival

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Analysis of human mobility

The New Year holiday

To

tal n

um

be

r o

f d

istin

ct vis

ito

rs

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Analysis of human mobility

One day before the Carnival

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Agenda

• Motivation

• Problem description

• Framework of methodology

• Current work

• Conclusions & Future work

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• Concept of green urban delivery with crowdsourcing

• Case of understanding movements of people

• Correlate human mobility to location, time, events

• On-working: Predict next location of user, upcoming events, …

Conclusions

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• bringBuddy concept: http://www.youtube.com/watch?v=fwhbij0tDyw

Practical application: not validate yet

• Crowdsourcing Physical Package Delivery Using the Existing Routine Mobility of a Local Population. James Mclnerney, Alex Rogers, Nicholas R. Jennings. D4D

Challenge, NetMob 2013, May 1—3, 2013, MIT.

• Feasibility

• Routing: Markov decision process

• Mobility habits: Bayesian

• Simulation

Related works

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Thank you

Questions?

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