IdealFlowNetwork 1.0.2

Ideal Flow Network Python Library

# Ideal Flow Network Python Library

This Python module is the core library for the computation of Ideal Flow Network (IFN). The IFN theory was proposed by [Kardi Teknomo](http://people.revoledu.com/kardi/) in 2015 and subsequently developed by his team. Check also: https://people.revoledu.com/kardi/research/trajectory/ifn/index.html

Ideal Flow is a new concept to analyze transportation networks or communication network. For IFN application to traffic assignment check [IFN-Transport](https://github.com/teknomo/ifn-transport) for more details.

# How to Install

> pip install IdealFlowNetwork

Check Also in [Pypi](https://pypi.org/project/IdealFlowNetwork/)

# Scientific Basis 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.](http://people.revoledu.com/kardi/publication/index.html) 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.](http://people.revoledu.com/kardi/publication/index.html) (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.](https://arxiv.org/abs/1706.08856) (2017) Premagic and Ideal Flow Matrices. https://arxiv.org/abs/1706.08856

• Gardon, R.W. and [Teknomo, K.](http://people.revoledu.com/kardi/publication/index.html) (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.](http://people.revoledu.com/kardi/publication/index.html) (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](http://people.revoledu.com/kardi/tutorial/Python/Ideal+Flow.html)

3. 2021 Kardi Teknomo