Passenger Dwell Time Analysisd2zmdbbm9feqrf.cloudfront.net/2013/eur/pdf/CCSEWN-1236.pdf · Passenger Dwell Time Analysis at Copenhagen Airport Esben Kolind, Senior Business Analyst,

© 2013 Cisco and/or its affiliates. All rights reserved. CCSEWN-1236 Cisco Public

Passenger Dwell Time Analysis

at Copenhagen Airport Esben Kolind,

Senior Business Analyst,

Copenhagen Airports

Kevin O'Sullivan

Lead Engineer, SITA Lab

CCSEWN-1236


Agenda

• About Copenhagen Airports and SITA Lab

• Using Wi-Fi data in CPH – what are the possibilities?

• Scoping of SITA Lab project

• SITA Airport Geolocation Platform Vision

• Technical aspects

‒ Device accuracy

‒ Zoning

‒ Path finding

• Using and validating Wi-Fi data

• Next steps

3


Copenhagen Airport (CPH)

• Largest airport in the Nordic countries with 22,7 million passengers in 2011 (#16 in Europe)

• 140 destinations served

• Best airport in Northern Europe 2011

• Most efficient airport in Europe 2011

• Europe’s Best Airport Shopping Experience 2011 (125 shops and restaurants)

• Biggest workplace in Denmark (22.000 employees)

• 800 Wi-Fi access points in the terminals

• Free membership of CPH loyalty scheme ”CPH Advantage” gives free Wi-Fi access


SITA Lab

5

• SITA are global supplier of IT services and products to Air Transport

Industry

• SITA Lab is a strategic research group at SITA

‒ Mobile Technologies

‒ BIG DATA

‒ NFC

‒ Tablet devices at airport & onboard

‒ Augmented Reality

‒ Indoor Geo Location


Using Wi-Fi data in CPH

6


What can we do with Wi-Fi data in CPH?

Real-time tracking

Crowded areas and busy periods

What routes do the passengers take through the airport?

How much time do passengers spend in different areas?

When do departing passengers show up?

How does passenger behaviour change? Over the day? By season? By airline?

Indoor navigation

Combine with flight data ”You are in the wrong end of the airport. Go to gate now!”

Combine with loyalty program ”We know you like fashion, and there is a great offer in the shop on your left right now”

Behaviour of frequent flyers

What is the effect of new infrastructure?


Initial scope of SITA Lab project

• Collect historical data

• Focus on passenger dwell times and passenger counts

‒ Different zones of the airport

‒ Passenger classification (departure/arrival/transfer)

‒ Different time periods and granularity

• Reporting

‒ Graphs and tables

‒ Export functionality


Business goals

• Increased insight into passenger behaviour

‒ Use knowledge for master planning, manning of facilities, design of processes,

selection of shops etc.

• More data and higher accuracy lead to better analysis (and better

decisions)

• Reduce the need for costly, manual surveys

‒ Passenger counts

‒ Questionnaires (Where have you been? How long time did you spend there?)


Initial questions

• Penetration

‒ How much data will we get?

• Accuracy

‒ 5 m? 10 m? 50 m?

‒ Accurate enough to locate a passenger in a specific gate? Shop? Even a queue?

• Frequency

‒ Accurate observations every 5 seconds on the entire route?

‒ Partial routes?

‒ Random, scattered observations?


Technical aspects of CPH Pilot

11


SITA Passenger Flow Monitoring

MSE

wireless network

location data

1

3

2 Controllers

Access Points

Analytics Engine Location Engine

Cisco Advanced Location Analytics

-BCBP

-Bluetooth

-People counts

-Other data

sources

4

Wi Fi Location Data

Additional

Data

Solution Overview


Device Detection


Device accuracy – Example 1






Zone marking – Example 1


Path finding


Zone marking – Example 2


Using and validating Wi-Fi data

20


Reporting interface

• Filters

‒ Date and time

‒ Passenger types

‒ Airport zones

‒ Granularity

• Output

‒ Table

‒ Graphs

‒ Export


Dwell time distributions


Validation of Wi-Fi data

• Existing waiting time system installed at Central Security Check (CSC)

‒ Based on active bluetooth devices

‒ Records entrance and exit from security area

Entry: Virtual gates

Exit: Nodes installed in security doorways


Dwell times at CSC


Device counts at CSC


What’s next?

26


Cisco Connected Mobile Experiences with

SITA Passenger Flow Monitoring for Airports

27


SITA Passenger Flow Monitoring

MSE

wireless network

location data

1

3

2 Controllers

Access Points

Analytics Engine Location Engine

Cisco Advanced Location Analytics

-BCBP

-Bluetooth

-People counts

-Other data

sources

4

Wi Fi Location Data

Additional

Data

Solution Overview


Solution Overview Going Forward

• Cisco’s Wi-Fi infrastructure with MSE

‒ Cisco’s Advanced Location Analytics

Focused on horizontal analytics, e.g. applicable to malls, urban areas, as well as airports

‒ Cisco’s Mobile Concierge

• SITA’s Airport iFlow passenger flow monitoring platform

‒ Air Travel Industry Extensions to Analytics

Airline Gate and Airport Security Tracking and Resource Management

Interfaces to FIDS (Flight Information Display Systems) and AODB (Airport Operations

Database) for flight specific statistical analysis

‒ Integration of other data sources and airport systems

BCBP (Bar Code Boarding Pass) tracking, security queue times via Bluetooth, with links to

FIDS and resource management (staffing) systems, as well as other 3rd party data sources

• 3rd party mobile application providers, e.g. Meridian


Data rules and filters for

date, time, path

Icon colors = number

of devices per region

Which duty free region had the most traffic last week?


Call to Action

• Visit the Cisco Campus at the World of Solutions to experience Cisco innovations in action

• Get hands-on experience attending one of the Walk-in Labs

• Schedule face to face meeting with one of Cisco’s engineers

at the Meet the Engineer center

• Discuss your project’s challenges at the Technical Solutions Clinics

31

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Passenger Dwell Time Analysisd2zmdbbm9feqrf.cloudfront.net/2013/eur/pdf/CCSEWN-1236.pdf · Passenger Dwell Time Analysis at Copenhagen Airport Esben Kolind, Senior Business Analyst,

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