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Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression...
Understanding and predicting destination of a trip is a crucial component of location based services. Traditional destination prediction work mostly focus on mining mobility patterns from frequently been locations. However, location transition patterns are not regular enough to provide favorable predicting results. Meanwhile, it could only be used when a user has enough movements in a location. In...
Locally weighted learning (LWL), which is an effectual and flexible method for prediction problems, is widely used in many regression scenarios. The training data samples, referring to the history experience knowledge base, are required to help do regression by new queries. However, sometimes, the knowledge base tends to be helpless due to the lake of information, such as inadequate training data...
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