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Pregled bibliografske jedinice broj: 1077649

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


Tišljarić, Leo; Carić, Tonči; Abramović, Borna; Fratrović, Tomislav
Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices // Sustainability, 12 (2020), 18; 7278, 16 doi:10.3390/su12187278 (međunarodna recenzija, članak, znanstveni)


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

Autori
Tišljarić, Leo ; Carić, Tonči ; Abramović, Borna ; Fratrović, Tomislav

Izvornik
Sustainability (2071-1050) 12 (2020), 18; 7278, 16

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
speed transition matrix ; traffic state estimation ; traffic state classification ; center of mass ; intelligent transport systems ; speed probability distribution

Sažetak
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

Izvorni jezik
Engleski

Znanstvena područja
Tehnologija prometa i transport, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )

Ustanove:
Fakultet prometnih znanosti, Zagreb

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.mdpi.com

Citiraj ovu publikaciju:

Tišljarić, Leo; Carić, Tonči; Abramović, Borna; Fratrović, Tomislav
Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices // Sustainability, 12 (2020), 18; 7278, 16 doi:10.3390/su12187278 (međunarodna recenzija, članak, znanstveni)
Tišljarić, L., Carić, T., Abramović, B. & Fratrović, T. (2020) Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices. Sustainability, 12 (18), 7278, 16 doi:10.3390/su12187278.
@article{article, author = {Ti\v{s}ljari\'{c}, Leo and Cari\'{c}, Ton\v{c}i and Abramovi\'{c}, Borna and Fratrovi\'{c}, Tomislav}, year = {2020}, pages = {16}, DOI = {10.3390/su12187278}, chapter = {7278}, keywords = {speed transition matrix, traffic state estimation, traffic state classification, center of mass, intelligent transport systems, speed probability distribution}, journal = {Sustainability}, doi = {10.3390/su12187278}, volume = {12}, number = {18}, issn = {2071-1050}, title = {Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices}, keyword = {speed transition matrix, traffic state estimation, traffic state classification, center of mass, intelligent transport systems, speed probability distribution}, chapternumber = {7278} }
@article{article, author = {Ti\v{s}ljari\'{c}, Leo and Cari\'{c}, Ton\v{c}i and Abramovi\'{c}, Borna and Fratrovi\'{c}, Tomislav}, year = {2020}, pages = {16}, DOI = {10.3390/su12187278}, chapter = {7278}, keywords = {speed transition matrix, traffic state estimation, traffic state classification, center of mass, intelligent transport systems, speed probability distribution}, journal = {Sustainability}, doi = {10.3390/su12187278}, volume = {12}, number = {18}, issn = {2071-1050}, title = {Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices}, keyword = {speed transition matrix, traffic state estimation, traffic state classification, center of mass, intelligent transport systems, speed probability distribution}, chapternumber = {7278} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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