Investigating potential transit
ridership by fusing
smartcard and GSM data
ir. Karin de Regt
dr. Oded Cats
dr. ir. Niels van Oort
prof. dr. ir. Hans van Lint
• https://nielsvanoort.weblog.tudelft.nl/
• @Niels_van_Oort
Introduction
• Demand for efficient public transport systems
• Passengers have different mobility patterns
• Dynamic in time and space
• Insights required into: Interaction with the overall travel
demand: modal split
• Different datasets offer opportunities to analyse mobility
3
Introduction
4
• Spatial and temporal dynamic information of public transport
passengers versus the overall travel demand
Characteristics Survey data
Included
modalities
All – distinction
between modes
Spatial
dynamicsYes
Temporal
dynamicsNo
Research
population
Sample of total
population
Characteristics Survey data GSM data
Included
modalities
All – distinction
between modes
All – no distinction
between modes
Spatial
dynamicsYes Yes
Temporal
dynamicsNo Yes
Research
population
Sample of total
population
Sample of total
population
Characteristics Survey data GSM data OV-chipkaart
data
Included
modalities
All – distinction
between modes
All – no distinction
between modesPublic transport
Spatial
dynamicsYes Yes Yes
Temporal
dynamicsNo Yes Yes
Research
population
Sample of total
population
Sample of total
population
Public transport
passengers
IntroductionResearch question
5
How can fusion of smartcard data and GSM data contribute
to the identification of spatial and temporal dynamic mobility
patterns of public transport passengers versus the overall
travel demand?
• Anonymous OV-chipkaart & GSM data
• Spatial and temporal boundaries
• Scenarios (to compare)
• Anonymous OV-chipkaart & GSM data
• Spatial and temporal boundaries
• Scenarios (to compare)
• Anonymous OV-chipkaart & GSM data
• Spatial and temporal boundaries
• Scenarios (to compare)
Basic methodology structure
• Anonymous OV-chipkaart & GSM data
• Spatial and temporal boundaries
• Scenarios (to compare)
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Input data
Data pre-processing
Data fusion
Interpretation of the results
Input data
Data pre-processing
Data fusion
Interpretation of the results
Input data
Data pre-processing
Data fusion
Interpretation of the results
• Different spatial and temporal perspectives
• Measuring disceprancies per perspective
per scenario
Input data
Data pre-processing
Data fusion
Interpretation of the results
• Different spatial and temporal perspectives
• Measuring disceprancies per perspective
per scenario
Data pre-processingAnonymous OV-chipkaart data
• Value: Passenger flow on a specific day, per hour, from origin to
destination (stop-stop)
• Nationwide (since 2012); tap-in and tap-out
• All modes: train, metro, tram, bus, ferry
• Only data of one public transport operator
• Completely anonymized data: no distinction of individuals
• 19 million smartcards; 42 million transactions every week
• Van Oort et al. (2015), Short-Term Prediction of Ridership on Public Transport with Smart Card Data, Transportation Research Record, No. 2535.
8
Data pre-processingGSM data
Value: Amount of visitors detected in a zone, on a specific day,
per hour
• Data from one network operator
• Algorithm to increase sample data to total population
• Distinction inhabitants or visitors
• Place of residence based on overnight stays per month
• Spatial level of detail: zone level
• Antennas have overlapping reach
• Difference occupancy between subsequent hours is a net
change
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×
×
×
Data fusionOV-chipkaart data and GSM data of occupancy
• Spatial perspectives:
• Total of all zones
• Per zone
• Per Origin-Destination relation
• Temporal perspectives:
• Per day
• Per hour
• MPE: Mean Percentage Error
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Rotterdam late evenings & early morningsContext
• Public transport supply related to overall travel demand
• Late evenings and early mornings
• Start and end of operations in line with respective increase and
decrease in overall travel demand
• Scenarios include long period of time
• January to May 2015
• Discrepancies measured with dynamic base scenario
• With respect to the previous hour
13
Rotterdam late evenings & early morningsWorking day scenario: zonal hourly perspective
15
Several zones show potential market for
exploiting additional urban public transport
Conclusions and recommendationsCurrent data fusion methodology
• Added value
• Public transport usage versus overall travel demand
• Dynamic information in both time and space
• Identification spatial and temporal features of interest
• Results have to be examined in more detail
• Match supply according to the demand
• Improvements based on characteristics dataset
• GSM data: localisation-error
• Smart card data: linking data from multiple operators
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Questions and contact
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Amsterdam
May 2017
• [email protected]• [email protected]
• https://nielsvanoort.weblog.tudelft.nl/
• @Niels_van_Oort
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