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

• N.vanOort@TUDelft.nl

• https://nielsvanoort.weblog.tudelft.nl/

• @Niels_van_Oort

INTRODUCTION

2

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?

METHODOLOGY

DEVELOPMENT

6

• 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)

7

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

9

×

×

×

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

10

Data fusionOV-chipkaart data and GSM data of occupancy

11

CASE STUDY:

LATE EVENINGS & EARLY

MORNINGS IN ROTTERDAM

17

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

14

Rotterdam late evenings & early morningsWorking day scenario: zonal hourly perspective

15

Several zones show potential market for

exploiting additional urban public transport

CONCLUSIONS AND

RECOMMENDATIONS

25

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

17

Questions and contact

18

Amsterdam

May 2017

• N.vanOort@TUDelft.nl• O.Cats@TUDelft.nl

• https://nielsvanoort.weblog.tudelft.nl/

• @Niels_van_Oort