Using Big data Analytics for Improved Public Transport

USING BIG DATA ANALYTICS FOR IMPROVED
PUBLIC TRANSPORT
PARTNER: JAKARTA SMART CITY, JAKARTA PROVINCIAL GOVERNMENT
TECHNOLOGY READINESS LEVEL: MINIMUM VIABLE PRODUCT (LEVEL 5)
SUMMARY
Pulse Lab Jakarta collaborated with Jakarta Smart City on a project to enhance transport planning and operational decision-making
through real-time data analytics. Using data from TransJakarta – the city’s rapid bus transit system – buses and passenger stations, the
project mapped origin-destination trends and identified bottleneck locations, information which can be used to identify whether new
routes are needed. The project also explored the possibility of using real-time data to determine passenger-waiting times in order to
enhance the efficiency of the bus dispatching system. The collaboration contributed to improvements in TransJakarta’s operations and
enhanced capacity within Jakarta Smart City.
BACKGROUND
Jakarta is well-known for its traffic jams, which, among other effects,
prolong commutes and complicate the scheduling of public
transportation. In an effort to improve public transportation services,
the city government – through its Jakarta Smart City initiative – is
looking to big data analytics for insights.
Established in December 2014, the Jakarta Smart City platform
collects data through government, third parties and citizen
crowdsourcing and provides information about the city to improve
public service delivery. Applications such as Qlue, Waze and others
deliver real-time information to the platform through citizen reporting.
The platform also includes data on the city’s Bus Rapid Transit
system, called TransJakarta, which was designed to provide Jakarta
citizens with a fast public transportation system to help reduce
commuting times and rush hour traffic. The buses use dedicated
lanes and tickets are subsidized by the regional government. In
2016, 123.7 million passengers used the bus service (a 20%
increase from 2015). Some of the challenges affecting TransJakarta
include unpredictable service frequency, travel times and wait times.
In order to better understand these challenges, Pulse Lab Jakarta
partnered with Jakarta Smart City on a project to analyse real-time
TransJakarta data to help improve bus service planning and delivery.
The project is part of efforts to support and advance the
Government’s data-driven policy and practice strategies.
www.unglobalpulse.org info@globalpulse.org 2017
ANALYSING TRANSJAKARTA DATA TO
IMPROVE SERVICE DELIVERY
The project focused on two aspects: using GPS bus data to map
problematic locations, and using passenger tap-in data, or the
moment a passenger starts a ride, to gain insights on passenger
behaviour through origin-destination (OD) statistics and waiting
times.
The TransJakarta fleet uses GPS devices that update bus information
every five seconds, including geo-location, time and velocity data
among other information. In order to identify problematic areas, the
PROGRAMME AREA: REAL-TIME EVALUATION
project used the dataset to identify locations with bottlenecks and
abnormal traffic behaviours and uncover where dedicated lanes are
slowest.
The project also explored whether bus ticket tap-in data would
reveal useful insights on passenger behaviour, which could improve
TransJakarta planning and services. Based on this dataset, the
project analysed origin-destination patterns, which included
transaction and bus station ID, date and time of transaction, line
number, card type and serial number. Although the tap-in system
does not provide passenger details, the project used the existing
data to determine average passenger waiting times.
INSIGHTS AND OUTCOMES
The project used GPS data from April to June 2016 to analyse bus
speeds, number of buses operating and bottleneck ratios in 12
dedicated corridors. This enabled Jakarta Smart City to map
problematic lines and locations throughout the city in order to better
detect anomalies and reduce traffic bottlenecks on the affected bus
routes.
To gain insights on passenger bus use, the project explored tap-in
data from May and June 2016 and selected a well-balanced
one-week period to analyse weekly patterns. The analysis included
12 lines and 422,694 unique passengers, from which 202,933
passengers (48%) were included in the origin-destintion pair
analysis.
To infer origin-destination statistics, the project developed two
different approaches. First, based on passenger transactions within
a day, origin-destination was derived from two consecutive tap-ins:
one at origin and the second at destination. Second, based on
aggregated passenger transactions residence and workplace were
derived from passenger behaviour indicated by their morning and
evening tap-in locations.

Based on these methods, the project identified:
• Number of hours between consecutive trips (weekday, weekend);
• Three types of bus lines (peak time in morning/evening, peak
time in morning, peak time in evening);
• Top destination lines; and
• Top origin-destination pairs at different levels: station, line,
sub-district, city levels.
To determine which stations have the longest waiting times, the
project used tap-in data and fleet GPS data based on number of
buses (GPS) and number of passengers (tap-in). The methods and
findings from the project were shared with TransJakarta to help
improve route planning and regularity of service and inform plans to
expand station facilities. The project highlighted key questions that
TransJakarta could further research, such as exploring why some
passengers only travel one-way instead of a round trip.
Based on feedback received from the project, the city
administration has made a number of targeted changes and
improvements to their operations, such as deploying more officers
and barriers to secure dedicated lines in typically congested areas
and adding buses on certain routes.
Prism
CONCLUSIONS
The insights generated by this project have been used to improve
transportation planning and demonstrate the potential for develop-
ing analytical models to enhance public service delivery.
The TransJakarta system generates a huge amount of data each
day and these data sources can provide significant insights to
inform improvements in services and increase overall ridership. The
partnership with Jakarta Smart City provided an opportunity to
develop internal government capacity while also demonstrating the
benefits of data analytics for improved public services.
IMPLICATIONS AND RECOMMENDATIONS
In the future, similar projects would benefit from
involving more partners in order to access expanded
datasets. For example, when mapping problematic
locations, GPS data from bus lanes could be
combined with crowdsourced traffic data to compare
regular traffic lanes with bus-restricted lanes.
For more robust results, data analytics could be
complemented by qualitative research. Though GPS
data might pinpoint a problematic location, it can be
challenging to identify possible causes and gain a
complete picture of the issue without qualitative
analysis.
To validate findings, similar projects should consider
incorporating passenger perspectives.
Future models related to origin-destination statistical
analysis could be strengthened by incorporating
additional data, such as using CCTV information
from video cameras, to assess bus and station
capacity levels.
HOW TO CITE THIS DOCUMENT:
UN Global Pulse, ‘Using Big Data Analytics for Improved
Public Transport,’ Project Series, no. 25, 2017.
Image 1. Visualisation of TransJakarta’s passengers points of origin and destination
ilustrating frequently congested locations.
Image 2. Mapping TransJakarta bus routes speed in problematic locations to
improve services.
www.unglobalpulse.org info@globalpulse.org 2017
Development of the analytical model allowed Pulse Lab Jakarta to
experiment with methods of inferring origin-destination statistics
and passenger behaviour based on tap-in data. Jakarta Smart
City found the OD pair analysis to be a useful way to understand
passenger trip behaviour. As a result of the project, TransJakarta
added the tap-out feature, which adds flexible fare pricing and
enhances origin-destination analysis.

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