Dense Learning for Robust Road Traffic Speed Prediction

Dense Learning for Robust Road Traffic Speed Prediction – We propose a novel algorithm for predicting the trajectory of an odometer moving in a given environment, by computing a distance function from the odometer’s sensor data. Based on the similarity of sensor data obtained by the odometer with the road traffic flow, a set of distance function values for road usage and a series of odometer movements along the road, we compute the trajectory distance of the odometer using a network of independent nodes that map the odometer to an arbitrary node location on the road for each movement. To provide a compact estimation of the trajectory distance as a function of road time, we derive a novel and highly efficient algorithm, which takes as input the odometer and the odometer movement, and outputs the path information between the odometer and the road. We provide experimental results showing that the proposed algorithms have a good performance in terms of the prediction performance of the odometer, relative to the state-of-the-art approaches.

We consider the task of recovering the full trajectory of an unknown object. Given data collection, we show that a low-dimensional feature space is essential. We study a low-dimensional classifier, which consists of a set of latent feature sets that can be used as an explicit feature descriptor. We develop an algorithm for learning from low-dimensional feature sets. Our system is evaluated on three public benchmark datasets (H3, H2, and G3).

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Dense Learning for Robust Road Traffic Speed Prediction

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