Road traffic state estimation framework based on hybrid assisted global positioning system and uplink time difference of arrival data collection methods

With the rapid increase of vehicles on the road, road traffic flow information is indispensible to our daily life. Different Intelligent Transport System applications like advanced Traffic Transport System are dependent on proper road traffic state information. Among the different activities in road traffic flow estimation, road traffic data collection plays the great role. The current state-of-the-practice road traffic data collection tools used to gather information about traffic flow are fixed sensor technologies which are limited in road coverage and affected by maintenance and deployment costs. Using the existing cellular network infrastructure to gather road traffic data offers large coverage capability and it is faster to set up, easier to install and needs less maintenance. Based on the analysis of relevant studies on road traffic state estimation, this paper proposes a universal framework based on experimentally evaluated hybrid Assisted Global Positioning System (A-GPS) and Uplink Time Difference Of Arrival (U-TDOA) real-time road traffic data collection system. The framework integrates several models with appropriate technologies to realize traffic data collection, processing, analysis, state estimation and optimization and presentation of traffic flow information to road users. In Data analysis component a new approach of taking probe sample, i.e. dynamic “Pinpoint-Temporal” sampling frequency method is proposed.