Abnormal Situation Simulation and Dynamic Causality Discovery in Urban Traffic Networks (Short Paper)

Authors Yadi Wang, Yicheng Pan, Meng Ma, Ping Wang

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Yadi Wang
  • School of Software and Microelectronics, Peking University, Beijing, China
Yicheng Pan
  • School of Electronics Engineering and Computer Science, Peking University, Beijing, China
Meng Ma
  • National Engineering Research Center for Software Engineering, Peking University, Beijing, China
Ping Wang
  • School of Software and Microelectronics, Peking University, China
  • National Engineering Research Center for Software Engineering, Peking University, Beijing, China

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Yadi Wang, Yicheng Pan, Meng Ma, and Ping Wang. Abnormal Situation Simulation and Dynamic Causality Discovery in Urban Traffic Networks (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 22:1-22:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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.

Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
  • SUMO simulation
  • dynamic causality discovery
  • congestion propagation


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