Ideal Flow Network
Ideal Flow is a steady state relative flow distribution in a strongly connected network where the flows are conserved
Tutorials on Ideal Flow
Check this tutorial on Ideal Flow Network
- Ideal Flow Network Tutorial with many online interactive programs
- Ideal Flow Network Anaysis using Python
- Video YouTube Tutorial:
- Ideal Flow on Network (contributed by Aubrey Mejia)
- Stochastic Matrix for Ideal Flow (contributed by Aubrey Mejia)
- Ideal Traffic Assignment (contributed by Aubrey Mejia)
- Random Walk on Network (that leads to IFN)
- Ideal Flow Network for Machine Learning
- IFN of Eulerian Network
- Analysis of Road Widening using IFN
- Dynamic Network: Strategy of Adding More Roads
- Kardi Teknomo's Lecture on Ideal Flow Network
- Highlight in the United Nation - Comprehensive Nuclear-Test-Ban Treaty Office (UN CTBTO) Science and Technology Conference 2019 (SnT2019) Vienna, Austria Ideal Flow Network (IFN) for Artificial Intelligence and Machine Learning
Software
Get the following software on Ideal Flow Network
- IFN for Traffic Assignment online interactive programs (in matrix)
- Application of IFN for Music
- Application of IFN for Transport Planning
- Python Core of Ideal Flow Network in GitHub, also in PyPi
- Ideal Flow Matrix EXCEL Add-Ins
- Python IFN Transport in GitHub
Papers about Ideal Flow
Download papers on Ideal Flow Network
Teknomo, K.(2019), Ideal Flow Network in Society 5.0 in Mahdi et al, Optimization in Large Scale Problems - Industry 4.0 and Society 5.0 Applications, Springer, p. 67-69
Teknomo, K., Gardon, R. and Saloma, C. (2019), Ideal Flow Traffic Analysis: A Case Study on a Campus Road Network, Philippine Journal of Science, Volume 148 (1), p. 51-62
Teknomo, K. (2019) Evaluation of a Proposed Road in a Campus Network based on Ideal Flow, Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol 3 No 2. DOI: 10.32734/jocai.v3.i2-620
Teknomo, Kardi (2019) Premagic and Ideal Flow Matrices, Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol 3 No 1. DOI: 10.32734/jocai.v3.i1-621
Teknomo, K. and Gardon, R.W. (2019) Traffic Assignment Based on Parsimonious Data: The Ideal Flow Network, 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 1393-1398.
Montalan, J.R., Estuar,M.R.J., Teknomo,K., Gardon, R.W. (2019) Measles Metapopulation Modeling using Ideal Flow of Transportation Networks, Proceedings of the 2nd International Conference on Software Engineering and Information Management.
Teknomo, K. and Gardon, R.W. (2017) Intersection Analysis Using the Ideal Flow Model, Proceeding of the IEEE 20th International Conference on Intelligent Transportation Systems, Oct 16-19, 2017, Yokohama, Japan
Teknomo, K. (2017) Ideal Relative Flow Distribution on Directed Network, Proceeding of the 12th Eastern Asia Society for Transportation Studies (EASTS), Ho Chi Minh, Vietnam Sept 18-21, 2017.
Gardon, R.W. and Teknomo, K. (2017) Analysis of the Distribution of Traffic Density Using the Ideal Flow Method and the Principle of Maximum Entropy, Proceedings of the 17th Philippine Computing Science Congress, Cebu City, March 2017.
Teknomo, K. (2017) Premagic and Ideal Flow Matrices, Philippine Computing Science Congress, Cebu City, March 2017.
Teknomo, K. (2016) Graphs, Ideal Flow, and the Transportation Network, Invited Speaker on Plenary Session Symposium on Graph Theory and Applications (SGTA2016), Ateneo de Manila, Quezon City, Philippines, January 13-15, 2016
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Teknomo, K. (2015) Ideal Flow Based on Random Walk on Directed Graph, The 9th International collaboration Symposium on Information, Production and Systems (ISIPS 2015) 16-18 Nov 2015, Waseda University, KitaKyushu, Japan.
Books about Ideal Flow
Slides about Ideal Flow
You have contributed a paper, tutorial, presentation or software on Ideal Flow Network and would like to be listed here, please contact me