Ideal Flow Network (IFN) is a Python module and library to compute network efficiency based on the theory of Ideal Flow proposed by Kardi Teknomo and his team. Ideal Flow is a new concept to analyze transportation networks. For traffic assignment using IFN, check the user guide for more details. Example of scenarios can also be downloaded from SampleScenario folder.
The following publications are the foundations of Ideal Flow analysis:
- Teknomo, K., Gardon, R. and Saloma, C. (2019), Ideal Flow Traffic Analysis: A Case Study on a Campus Road Network, Philippine Journal of Science 148 (1): 5162.
- Teknomo, K. (2018) Ideal Flow of Markov Chain, Discrete Mathematics, Algorithms and Applications, doi: 10.1142/S1793830918500738
- 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.
- Teknomo, K. (2017) Premagic and Ideal Flow Matrices. https://arxiv.org/abs/1706.08856
- 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. (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.
Please cite any of those papers if you use or improve this python library.
| Functions | Description |
|---|---|
| A = capacity2adj(C) | convert capacity matrix to adjacency matrix |
| S = capacity2stochastic(C) | convert capacity matrix into stochastic matrix |
| S = adj2stochastic(A) | convert adjacency matrix to stochastic matrix of equal outflow distribution |
| S = idealFlow2stochastic(F) | convert ideal flow matrix into Markov stochastic matrix |
| pi = steadyStateMC(S,kappa) | convert stochastic matrix into steady state Markov vector. kappa is the total of Markov vector. |
| F = idealFlow(S,pi) | return ideal flow matrix based on stochastic matrix and Markov vector |
| F = adj2idealFlow(A,kappa) | convert adjacency matrix into ideal flow matrix of equal distribution of outflow. kappa is the total flow |
| F = capacity2idealFlow(C,kappa) | convert capacity matrix into ideal flow vector, kappa is the total flow |
| sR = sumOfRow(M) | return vector sum of rows of matrix M |
| sC = sumOfCol(M) | return row vector sum of columns of matrix M |
| d = isSquare(M) | return True if M is a square matrix |
| d = isNonNegative(M) | return True of M is a non-negative matrix |
| d = isPositive(M) | return True of M is a positive matrix |
| d = isPremagic(M) | return True if M is premagic matrix |
| d = isIrreducible(M) | return True if M is irreducible matrix |
| d = isIdealFlow(M) | return True if M is an ideal flow matrix |
| h = networkEntropy(S) | return the value of network entropy |
| e = entropyRatio(S) | return network entropy ratio |
Tutorial on Ideal Flow Network is available in Revoledu.com
Check also: https://people.revoledu.com/kardi/research/trajectory/ifn/index.html
(c) 2018 Kardi Teknomo