| Summary: | 碩士 === 國立交通大學 === 運輸科技與管理學系 === 92 === Real-time travel information is becoming increasingly important in many intelligent transportation system (ITS) applications. In order to provide reliable information to the users, traffic information in all the ITS applications should be comprehensive and continually updated. It means that a continuous real-time data collection and processing effort is essential to provide the required information. However, data sometimes is not reliable since every source has a certain detecting range and the data volume is often small. These problems can be addressed by data fusion process.
Data fusion technology started in the late 1980s and many data fusion approaches had been developed and applied in recent years. In reviewing data fusion techniques in ITS field, the techniques can be divided into three levels. In our model, we propose data fusion techniques focus on the level two since level two data fusion provides a higher level of inference and delivers additional interpretive meaning suggested from the raw data.
Entropy is a concept proposed by C. Shannon in the 1948 and is used in “ Information Theory” first. Shannon’s entropy function has been used extensively as a measure of uncertainty. We propose a classifying approach so that we can cite the entropy to measure the uncertainty of the collected traffic data. Since entropy represents the uncertainty, we form an optimal weight scheme and use entropy to derive the weight of each sensor.
We perform a series of tests for model evaluation purpose. Since collecting real data is hard in practice and the volume of real data is often small, we also use simulated data to test our model. The testing results show that our proposed entropy data fusion technique is suitable in practice.
|
|---|
