Considering the Techniques for

Wrangling the Transportation Data

with Developing the Data Wrangling

Tool Based on Web Mashup Concept

A dissertation submitted to The University of Manchester for

the degree of Master of Science in the Faculty of Science and

Engineering

2016

Ganibek Zhakimbayev

School of Computer Science

Abstract

Nowadays the field of transportation attracts high attention while optimization of

transportation routes fosters the development of economics within the local regions.

At the level of cities, the optimization of transportation requires high-quality data. The

number of sources for this type of data had grown considerably together with

increasing the possibilities of information technologies. In these circumstances people

who are engaged in the transportation field faces difficulties in selecting and

preparation suitable datasets which frequently have considerable size and the format

which is not convenient for the analysis. Hence, one of the essential activity is Data

Wrangling which is the preparation the data in suitable form for the subsequent use.

The project focuses on the investigating the Data Wrangling techniques that could be

suitable for transportation data. The primary activity for performing this task is the

implementation of Traffic Data Wrangling Tool prototype. This prototype is

implemented using the Web Mashup concept that implies using the diversity of data

sources and development tools to mash them and obtain the integrated result that

has advantages compared to using different data and tools separately. After

consideration of project background and suitable sources of data, the prototype was

designed and implemented as a web application that is built using open source tools

such as Django Web Framework, Python Pandas library for scientific data analysis,

and D3 Java-Script library for a visual representation of data.

The objectives of the implementation were achieved. The experience has

demonstrated the effectiveness of using the mash of traffic data and open data from

online providers. Mashing the power of different tools which focus on various aspects

provides the possibility to use a domain-specific set of Data Wrangling functions in

one place including cleaning, transformation, and visualization of the datasets.

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