- Titel
- Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-859681
- Konferenz
- 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS). Dresden, 30. November - 2. Dezember
- Quellenangabe
- Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022 - 9
Herausgeber: Wang, Meng
Herausgeber: Jaekel, Birgit
Herausgeber: Lehnert, Martin
Herausgeber: Zhou, Runhao
Herausgeber: Li, Zirui
Erscheinungsort: Dresden
Verlag: TUDpress
Erscheinungsjahr: 2023
Titel Schriftenreihe: Verkehrstelematik
Bandnummer Schriftenreihe: 9
Seiten: 105-112
ISBN: 978-3-95908-296-9 - DOI
- https://doi.org/10.25368/2023.104
- Abstract (EN)
- Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki.
- Freie Schlagwörter (DE)
- Clusteranalyse, Stauerkennung, Verkehrsdaten, Reisebedingungen, Wetterdaten
- Freie Schlagwörter (EN)
- cluster analysis, congestion identification, traffic data, travel conditions, weather data
- Klassifikation (DDC)
- 360
- Klassifikation (RVK)
- ZO 4620
- ZO 3100
- Herausgeber (Institution)
- Technische Universität Dresden
- Verlag
- TUDpress, Dresden
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-859681
- Veröffentlichungsdatum Qucosa
- 23.06.2023
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
