Cellular Network Based Real-Time Road Traffic State Estimation Framework for Urban Road Networks

Ayalew, Belay Habtie (2017) Cellular Network Based Real-Time Road Traffic State Estimation Framework for Urban Road Networks. PhD thesis, Addis Ababa University.

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With the rapid increase of urban development and the surge in vehicle ownership, urban road transport problems like traffic accident and congestion caused huge waste of time, property damage and environmental pollution in recent years. To address these problems, use of Information Communication Technology–based transport systems that can support maximum utilization of the existing road transport infrastructure has been proposed by different researchers. Road monitoring systems are one of these solutions which support road users to make informed decisions. However, the current road traffic monitoring systems use road side infrastructures for road traffic data collection and these technologies lack accurate and up-to-date traffic data covering the whole road network. By comparison, cellular networks are already widely deployed and can provide large road network coverage. Besides, 3G and 4G cellular networks provide mobile phone positioning facility with better performance accuracy and this opportunity can help to obtain accurate traffic flow information in cost effective manner on the entire road networks. Mobile positioning technologies which aim to collect road traffic data in cellular networks can be either Handset-based or Network-based. Each of these technologies differ in their positioning accuracy as well as area coverage. But, there is no single cellular network positioning technology that can support wide area coverage with high accuracy simultaneously. To improve positioning accuracy, coverage and communication latency of positioning technologies, combination (hybrid) of Handset-based and Network-based positioning techniques has been proposed. The objective of this research is, therefore, to develop a real-time road traffic estimation framework, which utilizes a hybrid cellular network positioning technology as a source of road traffic data. To achieve this objective, the study uses a design science research approach. Detail literature review on past and current research studies was conducted to identify the need for better road traffic flow estimation accuracy together with improved performance of the procedures. With the help of systematic literature review better understanding on road traffic flow estimation procedures and tools was identified. The results of literature review on the different positioning principles and state estimation models formed a base to design a hybrid mobile phone positioning and tracking algorithm and real-time road traffic state estimation framework. An experimental research method is employed in evaluating the accuracy of the proposed hybridized positioning technology and validating the performance of road traffic state estimation framework. To evaluate the operational effectiveness of the framework sample road networks of Addis Ababa city are used. Data is gathered based on simulation experiment and also from field test conducted using J2ME location API (JSR-179) software installed on A-GPS enabled mobile phone moving in all journey of a moving vehicle. The evaluation of the framework using both simulation data and real-world data indicated that the developed estimation model could help to generate reliable traffic state information on urban roads. To validate the research process and evaluate practical utility of the research result, a theoretical literature support and expert survey were employed respectively. Accordingly, both the process and result are found to be valid, reliable and practical.

Item Type: Thesis (PhD)
Uncontrolled Keywords: cellular Network, Positioning Technology, Framework, Artificial Neural Network, State Estimation
Subjects: H Social Sciences > HE Transportation and Communications
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Africana
Depositing User: Selom Ghislain
Date Deposited: 10 Sep 2018 12:54
Last Modified: 10 Sep 2018 12:54
URI: http://thesisbank.jhia.ac.ke/id/eprint/5036

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