Analysing traffic condition based on IoT technique

Smart transportation is an application of intelligent system on transportation domain, expected to bring the society environmental and economic advantages. By combining with IoT techniques, the concept is being enhanced and raised to a system level. Numerous data are able to collect and effective analysis technique is needed. Here to relief the problem, we attempt to provide a candidate solution by quantifying the traffic condition based on kernel density estimation. With the traffic condition quantifier, one can estimate a function which approximate the traffic condition on the spatial space. This function can lead to further application by applying numerical techniques from data mining and machine learning domain.