Pregled bibliografske jedinice broj: 1077649
Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices
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)
CROSBI ID: 1077649 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
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
Profili:
Tonči Carić (autor)
Leo Tišljarić (autor)
Tomislav Fratrović (autor)
Borna Abramović (autor)
Citiraj ovu publikaciju:
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