The Department members published a new paper under the title “Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices” in the international scientific journal MDPI Sustainability, Special Issue “Data-driven Decision Support for Urban Management: Trends and Challenges ”. Authors of the paper are Leo Tišljarić, M.Sc, Tonči Carić, Ph.D., Borna Abramović, Ph.D., and Tomislav Fratrović, Ph.D. The paper is available at Open Access with DOI: https://doi.org/10.3390/su12187278.
This paper presents a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.
This research has been supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS). Data used for this research is collected during project SORDITO, European Regional Development Fund under contract RC.2.2.08-0022.