Citation

Rempe F, Franeck P, Bogenberger K. Transp. Res. C Emerg. Technol. 2022; 134: e103448.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trc.2021.103448

PMID

unavailable

Abstract

This paper studies Deep Convolutional Neural Networks (DCNNs) for the accurate estimation of space-time traffic speeds given sparse data on freeways. Several aspects are highlighted which are crucial for the large-scale application of DCNNs to empirical probe data. (i) A methodology is proposed that allows to effectively train DCNNs on variable-sized space-time domains given empirical data prone to a varying penetration rate. Therefore, space-time domains are decomposed into small unified grids, which are processed separately. Second, in order to cope with varying penetration rates of available probe data, the network input is designed as two input matrices: grid-based speed data and grid occupancies. (ii) Using empirical probe data collected during 43 congestion scenarios on a freeway of 143 km length a shallow encoding-decoding CNN and a deep CNN are trained to reconstruct heterogeneous congestion types, such as moving and stationary congestion. It is demonstrated that for training only data of a single sparse data source is needed but no complete Ground Truth (GT), and still heterogeneous congestion types are reconstructed accurately. (iii) The estimation accuracy of a shallow encoding-decoding CNN and a DCNN, based on the U-net, are compared with traditional methods such as the ASM and PSM. An unseen complex congestion scenario is studied with all approaches and the estimation results are analyzed qualitatively and quantitatively. It is shown, that the DCNN outperforms the other approaches significantly.

Language: en

Keywords

Artificial neural networks; Congestion patterns; Deep learning; Floating car data; Traffic speed estimation