Data based planning optimizes public transport capacity utilization. Floating phone data enhances customers’ travel experience.

1. DATA BASED PLANNING
OPTIMIZES PUBLIC TRANSPORT
CAPACITY UTILIZATION
WITH FLOATING PHONE DATA ENHANCING
CUSTOMERS’ TRAVEL EXPERIENCE
Manfred Bock
Global Industry Leader Rail - T-Systems International GmbH
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2. SMART DATA: MANAGEMENT & VALUE
CREATION
Smart
Data
Management
End-2-end
behaviour
Actual data from
APTMS
Individual
Track construction
work
Intermodal
IndividualizationAsset Data Base
Status signals /
crossings
Passenger
Information
Journey
planning
Train
operator
Infrastruc-
ture
External/Public data:
Weather / congestions / events / seasons
Data Link
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Predictive
Analytics

3. SMART DATA SOURCE
FLOATING PHONE DATA
Information from
cellular network
▪ Volume, frequency, dwell time
▪ Direction of movement, origin,
destination, mode of transport
▪ “Overnight ZIP”
Information from
CRM-Database
▪ Age (age group)
▪ Gender
▪ Residence (ZIP Code)
Mobile
Data-
sharing
with
Telco
provider
Geo
Informa-
tion
Socio-
demo-
graphics
Anony-
miSation
Transport
PUBLIC
services
Retail
Media &
Advertising
Deutsche
Telekom
CRM
value-
able
Business
Insights
For
Tourism
▪ Aggregated
to the total
population
▪ Conversion
to „tile-grid“
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4. USE CASES TRANSPORT
1. COMMUTER FLOW ANALYSIS
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Commuter Behavior & Traffic Measurement
District 1
City center
Industrial
area 2Industrial
area 1
Industrial
area
City center
Subway/
railway
LOCAL
▪ Commuter flow
analysis
(Origin/destinati
on matrix)
▪ Route utilization
▪ Scenarios:
simulating a
diversion
INNER-CITY
▪ Travel patterns
▪ Flow velocity
▪ Passenger transfer
behavior
▪ Tourist flows
▪ District commuter
volume
▪ Distribution of
transport modes
District 2

5. USE CASES TRANSPORT
2. ROUTE PLANNING
Commuter Behavior & Traffic Measurement
BENEFIT
Visualization of volume
of commuter flows:
▪ Capacity planning
▪ Flow velocity
▪ Effect of diversions
City 1 City 4
City 2 City 3
Industrial
area 1
Industrial
area 2
City center
District 2District 1
Subway/
railway
Industrial
area
City center
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6. FLOATING PHONE DATA
SET – UP TRAFFIC MANAGEMENT
SYSTEMS
▪ Design of transport management system
▪ Usage of empirical OD-Data from mobile networks
▪ Referred to 136 traffic districts in Karlsruhe
 Differentiation based on the day of the week
 Calibration of final destination
▪ Dynamization of demand
 Departure and arrival times referred to traffic districts
 Input variables for dynamic traffic assignment
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7. PREDICTIVE ANALYTICS / FORECASTING
MAIN COMPONENTS
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Modeling
▪ Uses metrics that are already
known
▪ Time of day, type of day, type of
train, weather conditions,
delays, etc.
▪ Modeling with non-self-learning
processes: edge-node-specific
histograms
▪ Modeling with self-learning
artificial neural networks
Creating forecasts
▪ Forecasting cumulative delays
▪ Use of probability density function
▪ Granularity specific to type of public
transport (trains, buses, etc..)
▪ Deployment and evaluation of
combinations of methods for
forecasts
▪ Deployment and evaluation of
methods in accordance with
operational conditions

8. ▪ Travel itinerary is tailored individually
and matched with personal
preferences
 e.g. end-2-end travel plan, favoured
means of transportation, etc.
Personalized
travel
schedule
Individual travel prognosis /
Plan in real time
▪ Arrival and departure times
▪ Occupancy/capacity
▪ Real –time intermodal routing -
Real-time recommendations for rout
optimization
Individual
navigation
Real-time
travel
optimization
CREATING INDIVIDUAL
PROGNOSIS/TRAVEL PLAN FOR
COMMUTER
▪ Easy/not crowded access to
stations and trains through better
utilization of transportation
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9. TARGET ARCHITECTURE
historical data back-up HDFS
models, forecasting HBASE
preprocessor modelling forecast control forecast jobs
KAFKA
postprocessor
Processed input data Train running
forecast models forecast models
forecasts
result data of the
forecast run
Forecasts
ForecastsPublic Transport Operator
Train data
Real Time Data
Nighttime timetable
planning
data/dispositions
data/master data
(https file transfer)
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10. BENEFITS AT A GLANCE
FOR YOUR PASSENGERS AND YOUR OPERATIONS
IMPROVED QUALITY OF SERVICE
▪ Travelers are informed in time – they can
manage their multi-stage journeys them-
selves
▪ High customer satisfaction
GREATER COMMUTER
FLEXIBILITY
Real time information according to
individual preferences
SIGNIFICANT TIME SAVINGS
Automation of travel information including
delays forecast and movement forecasts
saves time and improves quality of service
DIVERSE USES FOR FORECASTS
▪ Dynamic customer information systems
and mobile apps
▪ Integrated data for a range of multimodal
travelling (rail, bus, air, car sharing…)
▪ Ensuring passengers make individual real
time travel connections
▪ Optimization of multi-stage journeys
ONE-STOP SOLUTION
End-to-end offering – technological innovation, industry business
solution, dynamic infrastructure – the key to digital transformation.
The combination of public transport expertise and data
analytics supports faster, easier decision-making.
SMART FORECASTING METHODS
Dynamic generation of real-time information,
such as through integration of data on transport
disruptions that affect the traveler’s journey
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11. CONTACT
Manfred Bock
T-Systems International GmbH
Global Industry Leader Rail
Manfred.Bock@t-systems.com
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