IdealFlowNetwork 1.0.3

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 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 for more details.

How to Install

pip install IdealFlowNetwork

Check Also in Pypi

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. 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

(c) 2021 Kardi Teknomo