Mobile Phone ‐ based Vehicle Positioning and Tracking and Its Application in Urban Traffic State Estimation

Enabling the positioning and tracking of mobile phones has emerged as a key facility of existing and future generation mobile communication systems. This feature provides opportunities for many value added location-based services and systems. For instance, mobile phones are increasingly employed in traffic information systems and present several advantages over traditional sensor-based traffic systems. However, there are still plenty of aspects that must be investigated and addressed towards the fully operational deployment. The aim of the research performed in this thesis is to examine and propose solutions to two of the problems in the deployment of a mobile phone-based smart traffic information system. The first problem investigated is the mobile phone-based vehicle positioning and tracking. The investigation starts with a comprehensive study of mobile positioning with emphasis on existing standardizations. Based on the mobile location methods standardized in UMTS, possible hybrid solutions are proposed. In addition, a tool for simulating one of the UMTS mobile positioning methods (i.e., OTDOA) in vehicular environment is developed. A Kalman filter-based hybrid method, which can track the mobile phones traveling on-board vehicles, is then implemented. This method fuses two of the UMTS standard methods (i.e., OTDOA and A-GPS) location estimates at the state-vector level. Statistical simulation results demonstrate that the hybrid method can provide better position and velocity estimations than each individual method. The second problem addressed is the mobile phone-based urban traffic state estimation. A traffic simulation-based framework is proposed to emulate and evaluate the operation of urban traffic state estimation with A-GPS mobile phones as probes. Based on the emulated mobile phone probe data, algorithms of location data processing/filtering and average speed estimation are developed and then evaluated by comparing against “ground truth” data from the traffic simulation. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a traffic map on the mobile phone display. The achieved simulation results demonstrate the effectiveness of the proposed method, which is fundamental for the subsequent development of a mobile phone-based smart traffic information system demonstrator.

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