Real time traffic congestion degree computation for minor sensorless roads using cost efficient context reasoning

Traffic congestion is the cause of pollution and economic loss. The Real time traffic state report can alleviate this problem by assisting drivers for route planning and choosing unblocked roads. More traffic information could lead to more accurate route planning and greater awareness of traffic situations and road conditions for drivers. However, investment into sensor infrastructure for small minor roads is rarely available. In this paper, we present a cost efficient approach to compute real time traffic congestion degrees for non-sensor infrastructure roads, such as small minor roads, based on context fusion and context reasoning method. Our framework intends to utilize real time acquirable context instead of relying on traffic sensory data which made our approach suitable for insufficient sensor infrastructure environment. The evaluation throughout several experimentations conducted in Bangkok has proven the feasibility of our approach. Besides, the experimental result indicated that the methodology of our experiment can be used as an alternative cost efficient tool for infrastructure investment decision making.