This slides will give basics about crowd analysis for transport planning

1. Context-aware Crowd Analysis for
Improved Traffic and Infrastructure
Planning: A Review
​​Anglia Ruskin IT Research Institute
Anglia Ruskin University, Chelmsford
Problems and Perspectives

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Presentation Outline
• Why Wireless?
• Challenges of Wireless Network
• Cognitive Radio Network
• Multi-Hop Networking
• Relay Selection Algorithm
• Power Allocation Algorithm
• UWSN
• Anomaly in UWSN
• WBAN

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Crowd: Definition
A crowd is a large group of people that are gathered or
considered together.
A crowd may be definable through
• a common purpose or
• set of emotions, such as a political rally, a sports event, or
• may simply be made up of many people going about their
business in a busy area.
Jacob’s Method (1960):
It involves dividing the area occupied by a crowd into sections,
determining an average number of people in each section, and
multiplying by the number of sections occupied.

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Crowd on special event

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Context
• Web is a huge, heterogeneous data source
• Structured, unstructured and semi-structured data
• Known problems of trust, reputation, consistency
• User needs to solve real-time problems

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Cloud: data source
Smart Cards
Transportation Camera at infrastructure
Mobile User

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Cloud: data source (Cont.)
Data Source
• Smart cards
• Mobile communications
• Social media, hitting at different event websites
• Video surveillance
Crowds can be detected without hampering the privacy
information.

8. 8
Why Social Network?

9. 9
Cloud: Applications
This detection can be extremely important for
real-time transportation operation and management,
• urban planning,
• food and water stock planning,
• resource allocation optimally,
• safety and crowd management.
The big data produced by these mentioned computing
technologies gives microscopic details to understand crowd
mobility and plan accordingly.

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• The management agencies can handle usual-crowd due
to peak-hour commuting and such crowd mobility is
always considered in their plan,
• But the same management plan can not be applicable for
unusual crowds due to any event such as games,
sports, concerts, political rallies, festivals etc.
• These agencies can not design their management plan
only based on information from ubiquitous computing
technologies.
• The local context knowledge is important to extract
explanatory challenges.

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Why Mobile Data??

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Crowd:
2G/3G
LTE advance/5G

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CDR contains attributes such as:
• Location Area Information
• The phone number of the subscriber originating the call (calling party, A-
party) (No Need, may be assigned a tag)
• the phone number receiving the call (called party, B-party) (No Need)
• the starting time of the call (date and time) and the call duration
• the billing phone number that is charged for the call
• a unique sequence number identifying the record
• additional digits on the called number used to route or charge the call
• the disposition or the results of the call, indicating, for example, whether
or not the call was connected
• the route by which the call entered the exchange
• the route by which the call left the exchange
• call type (voice, SMS, etc.)
• any fault condition encountered

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Crowd-types
• Usual-crowd: can be estimated from the average hourly crowd
pattern.
• Overcrowd: This is the busy hour crowd, also called overcrowd. It
is understand by the government agencies and people and design
their public services accordingly.
• Unusual-overcrowd: It is due to a special event in geo-location.
That is why additional arrangements must be taken into
consideration by the government agencies to handle
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Crowd-types (cont.)
(a) Usual crowd
(b) Over-crowd
(c) Unusual over-crowd

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Chelmsford

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Mobile Coverage @ Chelmsford

21. 21
Mobile Coverage @ Chelmsford

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Video surveillance
Video Surveillance for Transportation Services
• Helps prevent crime and deter criminals
• Prevents vandalism
• Creates safer environment for passengers
• Holds employees accountable for their responsibilities
• Allows for remote viewing off-site from a smartphone or
tablet
• Reduces liability in cases of passenger injuries
Crowd count
Rate of change of Crowd

27. Thank you very much!!!
Any suggestions!!!!