CAUS Publications

2025

  • Jeong, M., Chang, J., & Yoon, Y. (2025). Speak to Simulate:An LLM-Guided Agentic Framework for Traffic Simulation in SUMO, Proceedings of the 8th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (GeoSIM '25)
  • Yoon, D., Jeong, M., Lee, J., Kim, S., & Yoon, Y. (2025). Integrating urban air mobility with highway infrastructure: A strategic approach for vertiport location selection in the Seoul metropolitan area (arXiv preprint No. 2502.00399).
  • Ro, J., Kim. N., & Yoon, Y. (2025). Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 6476–6485.
  • Sohn, S., Kim, N., Hansen, M., & Yoon, Y. (2025). USE-LFA: A data-driven framework for UAM site evaluation using latent factor analysis (arXiv preprint No. 2503.23090v2).
  • Kim, N., & Yoon, Y. (2025). Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT). IEEE Access, 13, 102602–102612.

2024

  • Ro, J., & Yoon, Y. (2024). Fill in the gap: Two-stage prediction modeling framework for understanding human biosensor data. In Proceedings of the 15th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, pp. 787–792.
  • Oh, S., & Yoon, Y. (2024). Urban drone operations: A data-centric and comprehensive assessment of urban airspace with a Pareto-based approach. Transportation Research Part A: Policy and Practice, 182, 104034.
  • Shin, Y., Kim, N., Lee, H., In, S. Y., Hansen, M., & Yoon, Y. (2024). Deep learning framework for vessel trajectory prediction using auxiliary tasks and convolutional networks. Engineering Applications of Artificial Intelligence, 132, 107936.

2023

  • Kim, N., Lee, H., & Yoon, Y. (2023). A heterogeneous attention network model for longitudinal analysis of socioeconomic and racial inequalities in urban regions: Evidence from Chicago, IL. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '23).
  • Shin, Y., Seong, G., Kim, N., Kim, S., & Yoon, Y. (2023). Understanding urban economic status through GNN-based urban representation learning using mobility data. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI (UrbanAI '23), 71–80.
  • Lee, H., Kim, N., Kim, S., & Yoon, Y. (2023). Detecting socioeconomic changes of Chicago regions between 2013 and 2020 using urban region representation learning. Procedia Computer Science, 220, 259–266.
  • Seong, G., Kim, N., & Yoon, Y. (2023). Multi-modal based region representation learning considering mobility data in Seoul. Procedia Computer Science, 220, 251–258.
  • Shin, Y., & Yoon, Y. (2023). PGCN: Progressive Graph Convolutional Networks for Spatial–Temporal Traffic Forecasting. IEEE Transactions on Intelligent Transportation Systems, 25(7), 7633–7644.
  • Shin, Y., & Yoon, Y. (2023). Performance Evaluation of Building Blocks of Spatial-Temporal Deep Learning Models for Traffic Forecasting. IEEE Access, 11, 136478–136495.

2022

  • Oh, S., & Yoon, Y. (2022). Data-driven risk analysis of unmanned aircraft system operations considering spatiotemporal characteristics of population distribution. Transportation Research Interdisciplinary Perspectives, 16, 100732.

2021

  • Kim, N., & Yoon, Y. (2021). Regionalization for urban air mobility application with analyses of 3D urban space and geodemography in San Francisco and New York. Procedia Computer Science, 184, 388–395.
  • Cho, J., & Yoon, Y. (2021). Extracting the topology of urban airspace through graph abstraction. Transportation Research Part C: Emerging Technologies, 127, 103116.
  • Noh, Y., Kim, S., Jang, Y. J., et al. (2021). Modeling individual differences in driver workload inference using physiological data. International Journal of Automotive Technology, 22, 201–212.

2020

  • Shin, Y., & Yoon, Y. (2020). Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting. IEEE Transactions on Intelligent Transportation Systems, 23(3), 2082–2092.
  • Oh, S., Kim, S., & Yoon, Y. (2020). An analysis of runway accident precursors based on latent class model. KSCE Journal of Civil Engineering, 24, 2784–2793.
  • Noh, Y., Kwon, O. H., & Yoon, Y. (2020). Comparative risk factor analyses on bi-level injury severity of taxi and private car crashes in Seoul, South Korea. Traffic Injury Prevention, 21(3), 188–194.
  • Lee, K., Eo, M., Jung, E., Yoon, Y., & Rhee, W. (2020). Short-term traffic prediction with deep neural networks: A survey. IEEE Access, 9, 54739–54756.
  • Lee, S. E., Jung, J.-W., Choi, Y., Yoon, Y.-J., & Hong, J.-W. (2020). Unmanned aerial vehicle impacts on heat-strengthened glass. IEEE Access, 7, 104269–104278.

2019

  • Kim, N., & Yoon, Y. (2019). Cooperative sUAV collision avoidance based on satisficing theory. International Journal of Aeronautical and Space Sciences, 20, 978–986.
  • Kim, S., & Yoon, Y. (2019). On node criticality of the Northeast Asian air route network. Journal of Air Transport Management, 80, 101693.
  • Kim, S., Lee, J., Oh, S., et al. (2019). Assessment of the volcanic hazard of Mt. Paektu explosion to international air traffic using South Korean airspace. Natural Hazards, 96, 647–667.

2018

  • Noh, Y., Kim, M., & Yoon, Y. (2018). Elderly pedestrian safety in a rapidly aging society—Commonality and diversity between the younger-old and older-old. Traffic Injury Prevention, 19(8), 874–879.
  • Cho, J., & Yoon, Y. (2018). How to assess the capacity of urban airspace: A topological approach using keep-in and keep-out geofence. Transportation Research Part C: Emerging Technologies, 92, 137–149.
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