2026-08-15T19:37:45Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:16907 2024-03-06T10:58:20Z ddc:004 open_access

Abnormal Situation Simulation and Dynamic Causality Discovery in Urban Traffic Networks (Short Paper) Wang, Yadi Pan, Yicheng Ma, Meng Wang, Ping SUMO simulation dynamic causality discovery congestion propagation Various participants in urban traffic systems intertwine a highly complicated coupling network. An interpretable analysis of underlying correlations is one of the keys to understanding traffic anomalies. Unfortunately, abnormal situation analysis in real scenarios faces severe limitations in negative sample deficiency, data integrity, and verifiability. In view of this, we developed a simulation tool - the Traffic Anomaly Situation Simulator (TASS). Through configurable scripts, TASS simulates real traffic networks by road editing, data collection, and fault injection. Given the generated cases, we designed a dynamic causal discovery algorithm, Dycause-Traffic, to demonstrate the features of causality in traffic anomalies. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Yadi Wang and Yicheng Pan and Meng Ma and Ping Wang 2022 Is Part Of LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.COSIT.2022.22 urn:nbn:de:0030-drops-169077 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.22 eng https://creativecommons.org/licenses/by/4.0/legalcode