Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

Leo Tišljarić, Tonči Carić, Borna Abramović and Tomislav Fratrović
Additional contact information
Leo Tišljarić: Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia
Tonči Carić: Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia
Borna Abramović: Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia
Tomislav Fratrović: Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia

Sustainability, 2020, vol. 12, issue 18, 1-16

Abstract: The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a 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.

Keywords: speed transition matrix; traffic state estimation; traffic state classification; center of mass; intelligent transport systems; speed probability distribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/18/7278/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/18/7278/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:18:p:7278-:d:409072

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().