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 new 3D-MAP method for semantic vehicle location based on spatial similarity map that aims to maximize the information gained by the 2D camera-based system. Based on the spatial similarity map, the system utilizes 3D point-based detection of pedestrian poses and vehicle positions based on semantic similarity maps. The objective of this method is to obtain accurate vehicle location data for both user- and vehicle-centric scenarios. We use a recently published 3D-MAP system developed jointly with the World Wide Web to build a semantic vehicle system using visual tagging framework called 3D-Map, that works well for both users and vehicle-centric scenarios. 3D-MAP system has its own method and the model developed jointly with the World Wide Web. The system has been updated with the new 3D-MAP system as well as the 3D-MAP system and also tested on real-world datasets. The 3D-MAP system is compared to the 2D system and with the new system.

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Deep Learning for Real-Time Traffic Prediction and Clustering

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    Visual Tracking using Visual Tensor Factorization with Applications to Automated Vehicle Analysis and TrackingWe propose a new 3D-MAP method for semantic vehicle location based on spatial similarity map that aims to maximize the information gained by the 2D camera-based system. Based on the spatial similarity map, the system utilizes 3D point-based detection of pedestrian poses and vehicle positions based on semantic similarity maps. The objective of this method is to obtain accurate vehicle location data for both user- and vehicle-centric scenarios. We use a recently published 3D-MAP system developed jointly with the World Wide Web to build a semantic vehicle system using visual tagging framework called 3D-Map, that works well for both users and vehicle-centric scenarios. 3D-MAP system has its own method and the model developed jointly with the World Wide Web. The system has been updated with the new 3D-MAP system as well as the 3D-MAP system and also tested on real-world datasets. The 3D-MAP system is compared to the 2D system and with the new system.


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