Analysing Transportation Data with Open Source Big Data Analytic Tools

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Vol. 5, No. 2, June 2017, pp. 174~184
ISSN: 2089-3272, DOI: 10.11591/ijeei.v5i2.297  174
Received April 28, 2016; Revised April 23, 2017; Accepted May 10, 2017
Analysing Transportation Data with Open Source Big
Data Analytic Tools
Y. Beeharry
1*
, T. P. Fowdur
1
, V. Hurbungs
2
, V. Bassoo
1
, V. Ramnarain-Seetohul
3
1,2,4
Department of Electrical and Electronic Engineering, University of Mauritius, Réduit, Mauritius
3
Department of Software and Information Systems, University of Mauritius, Réduit, Mauritius
5
Department of Information & Communication Technology, University of Mauritius, Réduit, Mauritius
e-mail: y.beeharry@uom.ac.mu
Abstract
Big data analytics allows a vast amount of structured and unstructured data to be
effectively processed so that correlations, hidden patterns, and other useful information can be
mined from the data. Several open source big data analytic tools that can perform tasks such as
dimensionality reduction, feature extraction, transformation, optimization, are now available.
One interesting area where such tools can provide effective solutions is transportation. Big data
analytics can be used to efficiently manage transport infrastructure assets such as roads,
airports, bus stations or ports. In this paper an overview of two open source big data analytic
tools is first provided followed by a simple demonstration of application of these tools on
transport dataset.
Keywords: Big Data, analytic tools, transportation
1. Introduction
Over the years, the traditional way of processing and analyzing data was to take data
from operational systems such as Enterprise Resource Planning (ERP), Customer relationship
management (CRM), or Supply Chain Management (SCM) systems and centralize the data in a
Data Warehouse. This data was structured in nature and business intelligence tools enabled
businesses to define key metrics and get answers to already known issues [1]. Businesses are
now being overloaded with modern sources of information such as social networks, online
media, sms, email, blogs and mobile activities.
With the urge to remain competitive, businesses can no longer rely on traditional
methods of decision making and must therefore be able to process and analyse all possible
sources of information to ensure business continuity [2]. This large set of data is referred to as
Big Data which combines both structured and unstructured data as compared to traditional data
frameworks. Big Data is characterized by 4 V‟s:
a. Volume: Very large data sets with both structured and unstructured data which can
be in terms of Terabytes, Petabytes, etc…
b. Velocity: Speed at which data is coming / generated. For example: high speed data
flow from IoT sensors, Twitter feeds, Facebook likes, among others.
c. Variety: Data comes from different sources. For example: smartphones, wearable
devices, IoT devices and sensors, and other mobile devices.
d. Veracity: All data which are being captured are not of good quality. Part of this data
may carry some level of uncertainty.
Some emerging big data applications are:
a. Healthcare: W. Raghupathi and V. Raghupathi [3] outlined an architectural
framework and methodology that describes the potential and promise of big data
analytics in healthcare. This framework would enable healthcare providers to obtain
insight from their clinical and other data repositories, and make informed decisions.
One such example would be to diagnose and treat patients in cost-effective ways
by analyzing patient records, disease patterns, and faster development of vaccines.
Additionally, D. Madhavi and B. V. Ramana [4] proposed a de-identified personal

IJEEI ISSN: 2089-3272 
Analysing Transportation Data with Open Source Big… (Yogesh Beeharry)
175
health care system which uses Map Reduce Pig querries which are required to be
executed on the datasets for health care.
b. Manufacturing: J. Lee et al [5] highlight the trends of industrial big data environment
and smart manufacturing. Key impact areas which they have identified are:
machine health prediction, transparency and organization across production lines,
reduced labour costs, and optimized machine maintenance.
c. Traffic management: Y. Lv et al [6] proposed a deep architecture model using
autoencoders as building blocks to represent traffic flow features for prediction.
Their experiments demonstrate that the proposed method for traffic flow prediction
has better performance. The main objectives of traffic management are: better
travel decisions, reduced traffic congestion and carbon emissions, and improved
traffic flow.
Many organizations have the expertise and equipment for handling large quantities of
structured data. However, the faster flows and increasing volumes of data, leaves them with the
inability to “mine” the data and derive actionable intelligence in a timely way. Not only is the
volume of this data growing too fast for traditional analytics, but the speed with which it arrives
and the variety of data types necessitates new types of data processing and analytic solutions
[7]. Big data doesn‟t always fit into neat tables of rows and columns. There are also many new
data types, both structured and unstructured, that can be processed to yield insight into a
business or condition [8]. Popular machine learning toolkits such as R [9] or Weka [10] were not
built for these kinds of workloads. Although Weka has distributed implementations of some
algorithms available, it is not on the same level as tools that were initially designed and built for
terabyte-scale. Some of the open-source Big Data Analytics Tools are: Mahout [11, 12], MLlib
[13, 14], H2O [15], SAMOA [16], and SparkR [17].
The remainder of this paper is organised as follows. Section II gives an overview of the
open source analytical tools. Section III gives a detailed overview of related works. Section IV
presents the application and testing of the two proposed tools. Finally Section V concludes the
paper.
2. Big Data in Transportation
Researchers have developed a complete transportation decision-making system called
the TransDec for the city of Los Angeles. The system acquires data from different sources in
real time and the amount of data that arrives per minute is around 46 megabytes [18]. The
system gathers traffic occupancy and volume data from more than 8,900 traffic loop detectors.
Data from buses and train are collected; the data is detailed and contains GPS location updated
every two minutes and delay information calculated by taking into account pre-defined
schedules. Information from ramp meters that are located at the entrance of highways is also
used. The system also accepts text format information about traffic incidents. All the data is then
cleaned, reducing the input rate from 46 megabytes per minute to 25 megabytes per minute.
Analytical techniques are applied to produce precise traffic patterns. TransDec can also predict
clearance times and resulting traffic jams after accidents [19].
London is a growing city with a population of around 8.6 million and is expected to hit 10
million by 2030. Transport for London is using big data to keep London moving and plan for
challenges arising from a growing city. The system gathers data from the „Oyster‟ cards, GPS
location of around 9,200 buses, 6,000 traffic lights and 1,400 cameras [19]. Transport for
London uses big data tools to develop accurate travel patterns of customers across rail and bus
networks. The information is then used by authorities to plan closures and diversions. The
system also enables authorities to send targeted emails to customers providing them alternative
routes thus minimizing the impact of scheduled and unscheduled changes [20].
In Sweden, the Stockholm train operators are big data analytics to predict train delays
up to two hours before they arise. The traffic control centre can then decide to make additional
train available to remedy the situation [21].
The Land Transport Authority (LTA) of Singapore is using big data to better serve their
customers. LTA uses data generated from logins to their public WIFI system available in the
MRT stations to produce a real time crowd heat map on each of their platforms. Additional trains
are added to the network if needed [22].

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3. Open Source Analytic Tools
The few open-source Big Data analytics tools mentioned (Mahout, MLlib, H2O,
SAMOA, and SparkR), are all frameworks which are scalable and contain abstractions for
machine learning algorithms on streaming data. The setup of these distributed frameworks is
very time-consuming. In the above list, the easiest tools which can be used are H2O and
SparkR with MLlib. H2O can be run on one local machine and has a Graphical User Interface
(GUI) with all the necessary documentation on steps to follow for data processing and analysis.
The Databricks community edition provides an online interface where users can create their
own notebooks using the R language amongst others, using the Spark framework for real-time
analytics. As such, the processes for transport data analysis with H2O and SparkR are
presented in this work.
3.1. H2O
H2O is a fast scalable open source software for distributed in-memory predictive
analytics, machine learning and deep learning. It is based on pure Java and Apache v2 Open
Source. It provides simple deployment with a single jar and automatic cloud discovery. H2O
allows data to be used without sampling and provides reliable predictions quicker. For this
reason it is suitable for several organisations such as PayPal, Nielsen, Cisco, etc. [23].
H2O can support billions of data rows in-memory even if the cluster size is relatively
small. This is possible by the use of sophisticated in-memory compression techniques. The H2O
platform has its own built-in Flow web interface so as to make analytic workflows become user-
friendly to users who do not have engineering background. It also includes interfaces for R,
Python, Scala, Java, JSON and Coffeescript/JavaScript. The H2O platform was built alongside
(and on top of) both Hadoop and Spark Clusters and is typically deployed within minutes [23-
25].
Several common machine learning algorithms are supported by H2O. Examples
include: Generalized Linear Modelling (GLM) such as linear regression, logistic regression, etc,
Naive Bayes, principal components analysis, time series analysis, k-means clustering etc. Best-
in-class algorithms such as Random Forest, Gradient Boosting and Deep Learning at scale are
also implemented by H2O [23-25]. A typical architecture for H2O is shown in Figure 1 [24].
Data Sources:
HDFS, S3,
NFS, WEB
CORE
Distributed tasks
Map/Reduce
Distributed in
memory K/V Store,
Column
Compressed data,
Memory Managed
Algorithms:
GBM, Random Forest,
PCA, K-Means, Deep
Learning
Front Ends:
REST API, R, PYTHON,
Web Interface
Figure 1. H2O Architecture
As observed in Figure 1, it supports: several data source, distributed memory and tasks,
a range of algorithms and multiple front-ends [24].

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3.2. SparkR
R is a common tool for building machine learning models. However, its effectiveness is
constrained by the processing power of a single machine. It is now possible to handle complex
machine learning problems with the power of clustered computers using a dedicated library
called MLib which is provided by Apache Spark. Spark MLlib is an open source API that is part
of the Apache Software Foundation. Spark DataFrames and MLlib provide tooling to make it
easier to integrate existing workflows developed on tools such as R and Python, with Spark. For
example, SparkR allows users to call MLlib algorithms using familiar R syntax [26]. Figure 2
illustrates the Spark ecosystem.
R SQLPythonJava
Spark SQL +
Data Frames
Streaming
Mlib
Machine Leaning
GraphX
Graph Computation
Scala
Spark Core API
Figure 2. The Spark ecosystem
As observed in Figure 2, Spark supports programming platforms such as R, SQL,
Python, Scala and Java. Additionally, it has several libraries which can provide functionalities
such such as graph computations, stream data processing, and real-time interactive query
processing in addition to machine learning [26].
MLlib provides distributes and fast implementations of common learning algorithms.
Various linear models are available to address regression problems. To cater for classification
problems, powerful algorithms such as Naïve Bayes, Random Forest, and Decision Tree are
provided. For collaborative filtering, least squares with explicit and implicit feedback can be
used. Unsupervised learning algorithms such as K-Means, and Principle Component Analysis
(PCA) for dimensionality reduction are also part of MLlib. A number of low-level primitives and
basic utilities for convex optimization, statistical analysis, feature extraction, and distributed
linear algebra, [27] are also provided in the library.
4. Application and Testing
In this section, the open source tools: H2O, and SparkR on Databricks have been used
to perform analytics on the transport related data obtained from [28]. A detailed explanation on
the configurations, and coding are given in the following sub-sections. The machine learning
algorithm used in H2O and SparkR on Databricks is: Generalised Linear Model (GLM).
4.1. H2O
The web-based user interface of H2O can be accessed by executing the jar file and
accessing the specified url through the web-browser as explained in [25]. The dataset to be
used can be imported using the importFiles option in the list on the homepage of H2O as
shown in Figure 3.

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Figure 3. Import data set on the cluster to be processed in H2O
The data frame is then split into Training set and Test set. 75% of the data is used to
train the model and 25% is reserved for testing purposes. Figure 4 shows the section where the
frame is selected and the percentage splits specified.
Figure 4. Split of Data Frame
4.1.1. GLM with H2O
The model is then built using the training data set as input. The Generalized Linear
Model (GLM) is used with the “Speed Value” column selected as the response column. The
“Local Date” and “Local Time” columns are ignored. These are shown in Figure 5. The equation
being modelled is: Speed Value ~ Day Type ID + Total Carriageway Flow + Total Flow vehicles
less than 5.2m + Total Flow vehicles 5.21m-6.6m + Total Flow vehicles 6.61m-11.6m + Total
Flow vehicles above 11.6m.

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Figure 5. Building a GLM
The output metrics on the training model is shown in Figure 6.
Figure 6. Output metrics for training model

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With the training model obtained, prediction can be performed on the test set data. A
comparison can then be performed on the predicted and already known “Speed Value”. Figure
7 shows the step where the model is used to predict the “Speed Value” for the test set.
Figure 7. Prediction using training model on test data set
The predicted “Speed Value” can be merged with the test data set and compared with
that already known. Figure 8 shows part of the combine frame.
Figure 8. Part of combined data frame of test data set and predicted “Speed Value”
An analysis of the percentage difference between the predicted and already known
“Speed Value” for the test data set can also be performed.

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4.2. SparkR with Databricks
The dataset to be used can be imported using the Create Table option in the list on the
homepage of Databricks as shown in Figure 9.
Figure 9. Import data set on the cluster to be processed in Databricks
The code to read the data into a data-frame for further processing is shown in Figure
10. The NA‟s are replaced by 0‟s in this case and the data-frame is then split into training (75%)
and test (25%) sets as shown in Figure 11. The Generalized Linear Model (GLM) is built as
shown in Figure 12. With the training model obtained, prediction can be performed on the test
set data. A comparison can then be performed on the predicted and already known “Speed
Value”. Figure 13 shows the step where the model is used to predict the “Speed Value” for the
test set. The predicted “Speed Value” can be merged with the test data set and compared with
that already known. Figure 14 shows part of the combine frame.
Figure 10. Read data into data-frame with SparkR
Figure 11. Replace NA‟s with 0‟s and split data set into training and test sets

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Figure 12. Generalized Linear Model with SparkR
Figure 13. Prediction using training model on test data set with SparkR
Figure 14. Part of combined data frame of test data set, predicted “Speed Value”, and the
percentage difference between them
5. Conclusion
This paper exemplifies the use of open source big data analytical tools, to show how
predictive modelling can be applied to data pertaining to transport. Only two big data analytical
tools (H2O and SparkR) have been chosen for demonstration in this paper. H2O can be easily
used by researchers in different fields with the basic knowledge of machine learning model
training and testing using the GUI. SparkR on the other hand, eases the tasks of experts in
statistics familiar with the R programming language and willing to venture in research works
pertaining to Big Data Analytics. However, as future work, other analytical tools can be used on

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different datasets, e.g., health data, manufacturing among others. Furthermore, these tools can
be used with infrastructures such as Hadoop to handle massive datasets from several sources.
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