Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach

• URL: http://arxiv.org/abs/2104.09686v1
• Date: Mon, 19 Apr 2021 23:32:12 GMT
• Title: Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach
• Authors: Felix Rempe, Philipp Franeck, Klaus Bogenberger
• Abstract summary: This paper presents a dedicated Deep Neural Network architecture that reconstructs space-time traffic speeds on freeways given sparse data. A large set of empirical Floating-Car Data (FCD) collected on German freeway A9 during two months is utilized. The results show that the DNN is able to apply learned patterns, and reconstructs moving as well as stationary congested traffic with high accuracy.
• Score: 1.5469452301122177
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 with an encoding-decoding structure and feed-forward paths, transforms the input into a full matrix of traffic speeds. The proposed DNN architecture is evaluated with respect to its ability to accurately reconstruct heterogeneous congestion patterns under varying input data sparsity. Therefore, a large set of empirical Floating-Car Data (FCD) collected on German freeway A9 during two months is utilized. In total, 43 congestion distinct scenarios are observed which comprise moving and stationary congestion patterns. A data augmentation technique is applied to generate input-output samples of the data, which makes the DNN shift-invariant as well as capable of managing varying data sparsities. The DNN is trained and subsequently applied to sparse data of an unseen congestion scenario. The results show that the DNN is able to apply learned patterns, and reconstructs moving as well as stationary congested traffic with high accuracy; even given highly sparse input data. Reconstructed speeds are compared qualitatively and quantitatively with the results of several state-of-the-art methods such as the Adaptive Smoothing Method (ASM), the Phase-Based Smoothing Method (PSM) and a standard Convolutional Neural Network (CNN) architecture. As a result, the DNN outperforms the other methods significantly.

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