Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757120
Title: Using Bluetooth to estimate traffic metrics for traffic management applications
Author: Ayodele, Emmanuel Gbenga
ISNI:       0000 0004 7429 9439
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2017
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Abstract:
‘Bluetooth’ is a technology that can be integrated into Intelligent Transport Systems (ITS) to facilitate smarter and enhanced traffic monitoring and management to reduce congestion. The current research focus on Bluetooth is principally on journey time management. However, the applicability and viability of Bluetooth potential in problematic urban areas remains unknown. Besides the generic problem of unavailability of processing algorithms, there is gap in knowledge regarding the variability and errors in Bluetooth-derived metrics. These unknown errors usually cause uncertainty about the conclusions drawn from the data. Therefore, a novel Bluetooth-based vehicle detection and Traffic Flow Origin-destination Speed and Travel-time (TRAFOST) model was developed to estimate and analyse key traffic metrics. This research utilised Bluetooth data and other independently measured traffic data collected principally from three study sites in Greater Manchester, UK. The Bluetooth sensors at these locations generated vehicle detection rates (7-16%) that varied temporally and spatially, based on the comparison with flows from ATC (Automatic Traffic Counters) and SCOOT (Split Cycle Offset Optimisation Technique) detectors. Performance evaluation of the estimation showed temporal consistency and accuracy at a high level of confidence (i.e. 95%) based on criteria such as Mean Absolute Deviation (MAD) - (0.031 – 0.147), Root Mean Square Error (RMSE) - (0.041 – 0.195), Mean Absolute Percentage Error (MAPE) - (0.822 – 4.917) and Kullback-Leibler divergence (KL-D) (0.004 – 0.044). This outcome provides evidence of reliability in the results as well as justification for further investigation of Bluetooth applications in ITS. However, the resulting accuracy depends significantly on sample size, network characteristics, and traffic flow regimes. The Bluetooth approach has enabled a deeper understanding of traffic flow regimes and spatio-temporal variations within the Greater Manchester Networks than is possible using conventional traffic data such as from SCOOT. Therefore, the application of Bluetooth technology in ITS to enhance traffic management to reduce congestion is a viable proposition and is recommended.
Supervisor: Not available Sponsor: Petroleum Technology Development Fund (PTDF) ; University of Lagos ; Surveyors Council of Nigeria (SURCON)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.757120  DOI: Not available
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