연구동향 분석

Activity based Infectious Modeling on Public Transport Networks = 대중교통을 통한 활동 기반의 감염 확산 모형

구동균 2022년

논문상세정보
인용/피인용
' Activity based Infectious Modeling on Public Transport Networks = 대중교통을 통한 활동 기반의 감염 확산 모형' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Activity-based Model
  • Chaos Theory
  • Deep Learning
  • Encounter Network Analysis
  • SEIR Model
  • transit assignment
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,805 0

0.0%

' Activity based Infectious Modeling on Public Transport Networks = 대중교통을 통한 활동 기반의 감염 확산 모형' 의 참고문헌

  • Wang, N., Chen, Y., & Zhang, L. (2011). Design of multi-agent-based distributed scheduling system for bus rapid transit. Proceedings - 2011 3rd International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2011, 2, 111–114.
    [2011]
  • WHO:World Health Organization(https://www.who.int/)
  • Van den Driessche, P. (2017). Reproduction numbers of infectious disease models. Infectious Disease Modelling, 2(3), 288–303.
    [2017]
  • Travel demand modeling for multi-agent transport simulations : Algorithms and systems .
    Balmer , M. [2007]
  • The perceptron : a probabilistic model for information storage and organization in the brain
  • Sun, L., Axhausen, K. W., Lee, D. H., & Huang, X. (2013). Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences of the United States of America, 110(34), 13774–13779.
    [2013]
  • Sun, L., Axhausen, K. W., Lee, D. H., & Cebrian, M. (2014). Efficient detection of contagious outbreaks in massive metropolitan encounter networks. Scientific Reports 2014 4:1, 4(1), 1–6.
    [2014]
  • Strathman, J. G., & Dueker, K. J. (1990). Understanding trip chaining. Special reports on trip and vehicle attributes, 1-1.
    [1990]
  • Spiess, H., & Florian, M. (1989). Optimal strategies: A new assignment model for transit networks. Transportation Research Part B: Methodological, 23(2), 83–102.
    [1989]
  • Spada, A., Tucci, F. A., Ummarino, A., Ciavarella, P. P., Calà, N., Troiano, V., Caputo, M., Ianzano, R., Corbo, S., de Biase, M., Fascia, N., Forte, C., Gambacorta, G., Maccione, G., Prencipe, G., Tomaiuolo, M., & Tucci, A. (2021). Structural equation modeling to shed light on the controversial role of climate on the spread of SARS-CoV-2. Scientific Reports 2021 11:1, 11(1), 1 –11.
  • Sharkey, K. J. (2011). Deterministic epidemic models on contact networks: Correlations and unbiological terms. Theoretical Population Biology, 79(4), 115 –129.
    [2011]
  • Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., Zimmer, T., Thiel, V., Janke, C., Guggemos, W., Seilmaier, M., Drosten, C., Vollmar, P., Zwirglmaier, K., Zange, S., Wölfel, R., & Hoelscher, M. (2020). Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany. New England Journal of Medicine, 382(10), 970–971.
  • Roddam, A. W. (2001). Mathematical Epidemiology of Infectious Diseases: Model Building. International Journal of Epidemiology, 30(1), 186–186.
    [2001]
  • Research on the movement and overcrowding of passengers according to urban railwqy station ’ s structures
    Cha J. , et al pp.218-224 [2014]
  • Read, J. M., Bridgen, J. R. E., Cummings, D. A. T., Ho, A., & Jewell, C. P. (2020). Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. MedRxiv, 2020.01.23.20018549.
    [2020]
  • Pastor-Satorras, R., Castellano, C., van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925.
    [2015]
  • Nuzzolo, A., & Comi, A. (2018). A Subjective Optimal Strategy for Transit Simulation Models. Journal of Advanced Transportation, 2018.
    [2018]
  • Mo, B., Feng, K., Shen, Y., Tam, C., Li, D., Yin, Y., & Zhao, J. (2021). Modeling epidemic spreading through public transit using time-varying encounter network. Transportation Research Part C: Emerging Technologies, 122, 102893.
  • Midander, K., Pan, J., Wallinder, I. O., & Leygraf, C. (2012). Metal release from stainless steel particles in vitro-influence of particle size. Journal of Environmental Monitoring, 12, 3047–3310.
    [2012]
  • McDonnell, S., & Zellner, M. (2011). Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior. Transport Policy, 18(6), 825–835.
    [2011]
  • Mao, L. (2011). Agent-based simulation for weekend-extension strategies to mitigate influenza outbreaks. BMC Public Health, 11(1), 1–10.
    [2011]
  • Ma, J. (2020). Estimating epidemic exponential growth rate and basic reproduction number. Infectious Disease Modelling, 5, 129–141
    [2020]
  • Li, M. Y., & Muldowney, J. S. (1995). Global stability for the SEIR model in epidemiology. Mathematical Biosciences, 125(2), 155–164.
    [1995]
  • Lefebvre, N. ;, & Balmer, M. (2007). Fast shortest path computation in time-dependent traffic networks. Arbeitsberichte Verkehrs- Und Raumplanung, 439.
    [2007]
  • Lee, W. do, Qian, M., & Schwanen, T. (2021). The association between socioeconomic status and mobility reductions in the early stage of England’s COVID-19 epidemic. Health & Place, 69, 102563.
  • Lee, S.-C., & Rhee, J.-H. (2005). Comparative Analysis of Activity-Based Model and UTMS Model by TRANSIMS’ and EMME/2’s Assigned Link Data. Journal of Transport Research.
    [2005]
  • Lee, J., Yoo, D., Ryu, S., Ham, S., Lee, K., Yeo, M., Min, K., & Yoon, C. (2019). Quantity, Size Distribution, and Characteristics of Cough-generated Aerosol Produced by Patients with an Upper Respiratory Tract Infection. Aerosol and Air Quality Research, 19(4), 840–853.
  • Kwon, S., Yang, Y. S., Yang, H. S., Lee, J., Kang, M. S., Lee, B. S., ... & Song, C. W. (2012). Nasal and pulmonary toxicity of titanium dioxide nanoparticles in rats. Toxicological research, 28(4), 217-224.
    [2012]
  • Ku, D., Yeon, C., Lee, S., Lee, K., Hwang, K., Li, Y. C., & Wong, S. C. (2021). Safe traveling in public transport amid COVID-19. Science Advances, 7(43), 3691–3713.
  • Ku, D., Na, S., Kim, J., & Lee, S. (2021). Real-time taxi demand prediction using recurrent neural network. Proceedings of the Institution of Civil Engineers-Municipal Engineer, 75–87.
  • Ku, D., Na, S., Kim, J., & Lee, S. (2020). Interpretations of Downs–Thomson paradox with median bus lane operations. Research in Transportation Economics, 83, 100909.
    [2020]
  • Ku, D. G., Um, J. S., Byon, Y. J., Kim, J. Y., & Lee, S. J. (2021). Changes in Passengers’ Travel Behavior Due to COVID-19. Sustainability 2021, Vol. 13, Page 7974, 13(14), 7974.
  • Korobeinikov, A., & Maini, P. K. (2004). A Lyapunov function and global properties for SIR and SEIR epidemiological models with nonlinear incidence. Mathematical Biosciences & Engineering, 1(1), 57.
    [2004]
  • Keeling, M. J., & Rohani, P. (2011). Modeling infectious diseases in humans and animals. Princeton university press.
    [2011]
  • Kai, D., Morgunov, A., Goldstein, G.-P., & Rotkirch, A. (2020). Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations. ArXiv Preprint ArXiv.
    [2020]
  • Jung, H., Kim, J., Lee, S., Lee, J., Kim, J., Tsai, P., & Yoon, C. (2014). Comparison of Filtration Efficiency and Pressure Drop in Anti-Yellow Sand Masks, Quarantine Masks, Medical Masks, General Masks, and Handkerchiefs. Aerosol and Air Quality Research, 14(3), 991–1002.
  • Jonnalagadda, N., Freedman, J., Davidson, W. A., & Hunt, J. D. (2001). Development of microsimulation activity-based model for San Francisco: destination and mode choice models. Transportation Research Record, 1777(1), 25-35.
    [2001]
  • Joh, C. H., Arentze, T. A., & Timmermans, H. J. P. (2001). A theory and simulation model of activity-travel rescheduling behavior. In Selected Proceedings of the 9th World Conference on Transport ResearchWorld Conference on Transport Research Society.
    [2001]
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. Jin Ki Eom of. (2008). Estimating travel demand by using a spatial-temporal activity presence-based approach. Koreascience.or.Kr, 163–174.
    [2018]
  • Horni, A., Nagel, K., & Axhausen, K. W. (2016b). The Multi-Agent Transport Simulation MATSim. The Multi-Agent Transport Simulation MATSim, 618.
  • Horni, A. ;, Nagel, K. ;, & Axhausen, K. W. (2016a). Introducing MATSim ETH Library. Ubiquity Press.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
    [1997]
  • Hethcote, H. W., & Yorke, J. A. (2014). Gonorrhea transmission dynamics and control (Vol. 56). Springer.
    [2014]
  • Hensher, D. A., & Button, K. J. (2000). Handbook of transport modelling (No. 1).
    [2000]
  • Hamiltonian Systems and Transformation in Hilbert Space
    Koopman , B. O 17 ( 5 ) , 315 [1931]
  • Gupta, R., Pandey, G., Chaudhary, P., & Pal, S. (2020). SEIR and Regression Model based COVID-19 outbreak predictions in India. ArXiv Preprint ArXiv.
    [2020]
  • Fang, H., Chen, J., & Hu, J. (2006, January). Modelling the SARS epidemic by a lattice-based Monte-Carlo simulation. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 7470-7473). IEEE.
    [2006]
  • Estimating travel demand by using a spatial-temporal activity presence-based approach
    Eom , J. K. 26 ( 5 ) , 163-174 . [2008]
  • Diekmann, O., & Heesterbeek, J. A. P. (2000). Mathematical epidemiology of infectious diseases: model building, analysis and interpretation (Vol. 5). John Wiley & Sons. Fang, H., Chen, J., & Hu, J. (2005). Modelling the SARS epidemic by a lattice-based Monte-Carlo simulation. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 7 VOLS, 7470–7473.
  • Detecting strange attractors in turbulence . Dynamical Systems and Turbulence , Warwick 1980 , 366 ? 381 .
    Takens , F. [1981]
  • Daly, A. (1982). Estimating choice models containing attraction variables. Transportation Research Part B: Methodological, 16(1), 5–15.
    [1982]
  • Comparing different approaches of epidemiological modeling
  • Choi, Minje, et al. “Method for Evaluating Degree of Walkway Breakage Using Deep Learning Algorithms.” KSCE Journal of Civil Engineering (2022): 1-10.
  • Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore Y Piontti, A., Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M. E., Longini, I. M., & Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. SCIENCE, 368.
  • Chakrabarti, D., Wang, Y., Wang, C., Leskovec, J., & Faloutsos, C. (2008). Epidemic Thresholds in Real Networks. ACM Transactions on Information and System Security, 10(4), 1–26.
    [2008]
  • Cats, O. (2013). Multi-agent Transit Operations and Assignment Model. Procedia Computer Science, 19, 809–814.
    [2013]
  • CDC: centers for disease control and prevention(https://www.cdc.gov/)
  • Brunton, S. L., Brunton, B. W., Proctor, J. L., Kaiser, E., & Nathan Kutz, J. (2017). Chaos as an intermittently forced linear system. Nature Communications 2017 8:1, 8(1), 1–9.
    [2017]
  • Brunton, S. L., Brunton, B. W., Proctor, J. L., & Kutz, J. N. (2016). Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control. PLOS ONE, 11(2), e0150171.
    [2016]
  • Bowman, J. L., & Ben-Akiva, M. E. (2001). Activity-based disaggregate travel demand model system with activity schedules. Transportation Research Part A: Policy and Practice, 35(1), 1–28.
    [2001]
  • Bhat, C. R., & Koppelman, F. S. (1999). A retrospective and prospective survey of time-use research. Transportation 26, 119–193.
    [1999]
  • Berger, D. W., Herkenhoff, K. F., Mongey, S., Griffin, K. C., & Post, W. (2020). An SEIR Infectious Disease Model with Testing and Conditional Quarantine. National Bureau of Economic Research. https://doi.org/10.3386/W26901
    [2020]
  • Andrade, J., & Duggan, J. (2020). An evaluation of Hamiltonian Monte Carlo performance to calibrate age-structured compartmental SEIR models to incidence data. Epidemics, 33, 100415.
    [2020]
  • Anderson, R. M., & May, R. M. (1992). Infectious diseases of humans: dynamics and control. Oxford university press. Andrade, J., & Duggan, J. (2020). An evaluation of Hamiltonian Monte Carlo performance to calibrate age-structured compartmental SEIR models to incidence data. Epidemics, 33, 100415.2020.100415
    [1992]
  • Ahn, S., Kim, J., Bekti, A., Cheng, L.-C., Clark, E., Robertson, M., & Salita, R. (2016). Real-time Information System for Spreading Rail Passengers across Train Carriages: Agent-based Simulation Study. Australasian Transport Research Forum.
    [2016]
  • Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. E., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.
    [2018]
  • A Simulated Annealing Algorithm for the Activity Based Travel Demand Modelling
    Jooyoung Kim Universith of Seoul [2014]