Real time predictive monitoring system for urban transport

Khan, Nauman Ahmed (2017) Real time predictive monitoring system for urban transport. (PhD thesis), Kingston University, .

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Abstract

Ubiquitous access to mobile and internet technology has influenced a significant increase in the amount of data produced, communicated and stored by corporations as well as by individual users, in recent years. The research presented in this thesis proposes an architectural framework to acquire, store, manipulate and integrate data and information within an urban transport environment, to optimise its operations in real-time. The deployed architecture is based on the integration of a number of technologies and tailor-made algorithms implemented to provide a management tool to aid traffic monitoring, using intelligent decision-making processes. A creative combination of Data Mining techniques and Machine Learning algorithms was used to implement predictive analytics, as a key component in the process of addressing challenges in monitoring and managing an urban transport network operation in real-time. The proposed solution has then been applied to an actual urban transport management system, within a partner company, Mermaid Technology, Copenhagen to test and evaluate the proposed algorithms and the architectural integration principles used. Various visualization methods have been employed, at numerous stages of the project to dynamically interpret the large volume and diversity of data to effectively aid the monitoring and decision-making process. The deliverables on this project include: the system architecture design, as well as software solutions, which facilitate predictive analytics and effective visualisation strategies to aid real-time monitoring of a large system, in the context of urban transport. The proposed solutions have been implemented, tested and evaluated in a Case Study in collaboration with Mermaid Technology. Using live data from their network operations, it has aided in evaluating the efficiency of the proposed system.

Item Type: Thesis (PhD)
Physical Location: This item is held in stock at Kingston University library.
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017)
Depositing User: Jennifer May
Date Deposited: 23 Aug 2017 09:42
Last Modified: 06 Nov 2018 10:16
URI: http://eprints.kingston.ac.uk/id/eprint/39019

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