Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices
| Autor: | Tonči Carić, Tomislav Fratrović, Borna Abramović, Leo Tišljarić |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Pollution
Computer science media_common.quotation_subject intelligent transport systems lcsh:TJ807-830 Geography Planning and Development lcsh:Renewable energy sources 0211 other engineering and technologies 02 engineering and technology Management Monitoring Policy and Law computer.software_genre Advanced Traffic Management System 0502 economics and business Cluster analysis Intelligent transportation system lcsh:Environmental sciences 021101 geological & geomatics engineering media_common lcsh:GE1-350 050210 logistics & transportation Renewable Energy Sustainability and the Environment lcsh:Environmental effects of industries and plants 05 social sciences speed transition matrix traffic state classification speed probability distribution lcsh:TD194-195 center of mass traffic state estimation Anomaly detection Data mining Scale (map) computer |
| Zdroj: | Sustainability Volume 12 Issue 18 Sustainability, Vol 12, Iss 7278, p 7278 (2020) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su12187278 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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