Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach
Felix Rempe,
Philipp Franeck,
Klaus Bogenberger
Abstract:This paper presents a dedicated Deep Neural Network (DNN) architecture that reconstructs space-time traffic speeds on freeways given sparse data. The DNN is constructed in such a way, that it learns heterogeneous congestion patterns using a large dataset of sparse speed data, in particular from probe vehicles. Input to the DNN are two equally sized input matrices: one containing raw measurement data, and the other indicates the cells occupied with data. The DNN, comprising multiple stacked convolutional layers… Show more
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