Document Open Access Logo

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

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

PDF
Thumbnail PDF

File

LIPIcs.COSIT.2022.22.pdf
  • Filesize: 6.08 MB
  • 7 pages

Document Identifiers

Author Details

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

Funding

The National Natural Science Foundation of China (92167104, 62072006) and Science and Technology on Communication Networks Laboratory (6142104200103) support this work.

Cite AsGet BibTex

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)
https://doi.org/10.4230/LIPIcs.COSIT.2022.22

Abstract

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
Keywords
  • SUMO simulation
  • dynamic causality discovery
  • congestion propagation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Michael Behrisch et al. Sumo-simulation of urban mobility: an overview. In Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation. ThinkMind, 2011. Google Scholar
  2. Gleb Beliakov et al. Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data. Applied Soft Computing, 67:910-919, 2018. Google Scholar
  3. Etienne Come et al. Spatio-temporal analysis of dynamic origin-destination data using latent dirichlet allocation: Application to vélib'bike sharing system of paris. In TRB 93rd Annual meeting, page 19p. Transportation Research Board, 2014. Google Scholar
  4. Clive WJ Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, pages 424-438, 1969. Google Scholar
  5. Meng Hu et al. A copula approach to assessing granger causality. NeuroImage, 100:125-134, 2014. Google Scholar
  6. Bin Jiang et al. Topological analysis of urban street networks. Environment and Planning B: Planning and design, 31(1):151-162, 2004. Google Scholar
  7. Zihan Kan et al. Traffic congestion analysis at the turn level using taxis' gps trajectory data. Computers, Environment and Urban Systems, 74:229-243, 2019. Google Scholar
  8. Felix Kling et al. When a city tells a story: urban topic analysis. In Proceedings of the 20th international conference on advances in geographic information systems, pages 482-485, 2012. Google Scholar
  9. Xi Liu et al. Revealing travel patterns and city structure with taxi trip data. Journal of transport Geography, 43:78-90, 2015. Google Scholar
  10. Qiong Lu et al. The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation. Transp. Lett., 12(8):540-549, 2020. Google Scholar
  11. Daniel Malinsky et al. Causal structure learning from multivariate time series in settings with unmeasured confounding. In Proceedings of 2018 ACM SIGKDD workshop on causal discovery, pages 23-47. PMLR, 2018. Google Scholar
  12. Feng Mao et al. Mining spatiotemporal patterns of urban dwellers from taxi trajectory data. Frontiers of Earth Science, 10(2):205-221, 2016. Google Scholar
  13. Meike Nauta et al. Causal discovery with attention-based convolutional neural networks. Machine Learning and Knowledge Extraction, 1(1):312-340, 2019. Google Scholar
  14. Angeliki Papana et al. Detecting causality in non-stationary time series using partial symbolic transfer entropy: Evidence in financial data. Computational economics, 47(3):341-365, 2016. Google Scholar
  15. Felix Rempe et al. Spatio-temporal congestion patterns in urban traffic networks. Transportation Research Procedia, 15:513-524, 2016. Google Scholar
  16. Jakob Runge et al. Detecting and quantifying causal associations in large nonlinear time series datasets. Science advances, 5(11):4996, 2019. Google Scholar
  17. Xiaoying Shi et al. Exploring the evolutionary patterns of urban activity areas based on origin-destination data. IEEE Access, 7:20416-20431, 2019. Google Scholar
  18. Huijun Sun et al. Urban traffic congestion spreading in small world networks. International Journal of Modern Physics B, 19(28):4239-4246, 2005. Google Scholar
  19. Zhang Zhang et al. A general deep learning framework for network reconstruction and dynamics learning. Applied Network Science, 4(1):1-17, 2019. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact