Deep Learning for Real-Time Traffic Prediction and Clustering


Deep Learning for Real-Time Traffic Prediction and Clustering – We propose a new deep learning based technique aimed at solving traffic prediction problems with limited knowledge about the current traffic. The existing work on deep networks and deep learning in general uses the maximum available information from the underlying networks, and thus does not properly model the network structures and dynamics. Besides, traffic prediction in this setting is not limited either. This paper is an application of deep learning to a multi-modal learning problem, where the data consists of multi-modal traffic signals. First, we propose a method to learn a prediction network which is capable of predicting a user’s preferences (i.e., road trip time, speed and lane width). Then we also consider the possibility of using different traffic signals to learn a neural network, and the network of such a network is shown to be well-defined. Finally, we use a multi-modal machine learning model called ResNet to predict the road trip time, which is described as a time-dependent function and can be used as a basis for a prediction network.

We propose a novel neural network-inspired framework to learn multi-frame HDR and Dynamic Cues (MD-DCA). MDACA is a general framework that models multiscale objects and a discriminative framework that aims at building an image representation. We first train a convolutional neural network to jointly estimate the color, foreground color and noise in objects and a scene, then use discriminative and adaptive CNNs to model the image representation. The proposed deep MDACA framework aims to learn a common representation, that is, a common representation that can predict the color and noise generated from a deep CNN. We demonstrate how MDACA’s CNN-based representation is learned and compared to CNN-based representations with the same training set and multiple training sets. The proposed framework is more interpretable, and improves the classification performance as compared to other existing multi-frame CNN-based approaches, including CNN-based CNN and CNN-based CNN-based CNN.

Using Global Perspectives to Influence Search and Feature Selection in HRIG

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Deep Learning for Real-Time Traffic Prediction and Clustering

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  • Scalable Large-Scale Image Recognition via Randomized Discriminative Latent Factor Model

    Improving Multi-Frame Multi Shot Hashing with Dynamic CuesWe propose a novel neural network-inspired framework to learn multi-frame HDR and Dynamic Cues (MD-DCA). MDACA is a general framework that models multiscale objects and a discriminative framework that aims at building an image representation. We first train a convolutional neural network to jointly estimate the color, foreground color and noise in objects and a scene, then use discriminative and adaptive CNNs to model the image representation. The proposed deep MDACA framework aims to learn a common representation, that is, a common representation that can predict the color and noise generated from a deep CNN. We demonstrate how MDACA’s CNN-based representation is learned and compared to CNN-based representations with the same training set and multiple training sets. The proposed framework is more interpretable, and improves the classification performance as compared to other existing multi-frame CNN-based approaches, including CNN-based CNN and CNN-based CNN-based CNN.


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