@inproceedings{1f52f62578654a789ca9b527a8baae82,
title = "ESTIMATION OF NETWORK-WIDE TRAFFIC SPEEDS AND FLOWS WITH HETEROGENEOUS DATA",
abstract = "This paper proposes a model-based data-driven approach for simultaneously estimating network-wide traffic speeds and flows. In the proposed model, heterogeneous traffic data from multiple types of traffic detectors (point detectors and automatic vehicle identification detectors) on observed links are used. Fundamental diagrams of link speeds and flows are also considered. The spatial variance and covariance of link speeds and flows are incorporated into the proposed model to estimate the traffic speeds and flows on unobserved links (i.e., the links without traffic detectors) within the entire study network. The proposed model is formulated as a Kullback- Leibler (KL) divergence-based optimization problem that minimizes the KL divergence between the probability density functions of the observed and estimated link densities. A numerical example is presented to demonstrate the applications of the proposed model.",
keywords = "KL divergence, Network-wide traffic speed-flow estimation, fundamental diagrams, spatial variance and covariance",
author = "Shaojie Liu and Tam, {Mei Lam} and Lam, {William H.K.} and Wei Ma and Ho, {H. W.} and Hao Fu",
note = "Publisher Copyright: {\textcopyright} 2022 Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022. All Rights reserved.; 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022 ; Conference date: 12-12-2022 Through 13-12-2022",
year = "2022",
language = "English",
series = "Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022",
pages = "224--230",
editor = "Sisi Jian and Sen Li and Lo, {Hong K.}",
booktitle = "Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022",
}