Open AccessMODELING STREAM SPEED IN HETEROGENEOUS TRAFFIC ENVIRONMENT USING ANN‐LESSONS LEARNT
Author(s) -
Debasis Basu,
Bhargab Maitra
Publication year - 2006
Publication title -
transport
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 31
eISSN - 1648-4142
pISSN - 1648-3480
DOI - 10.3846/16484142.2006.9638077
Subject(s) - artificial neural network , process (computing) , computer science , selection (genetic algorithm) , artificial intelligence , logical data model , data mining , machine learning , data modeling , database , operating system
In order to model traffic stream speed resulting from complex interactions among different vehicle types in a heterogeneous/mixed traffic volume, an Artificial Neural Networks (ANN) approach is exploited. Two different categories of ANN model are attempted based on input vectors used. The performance of both categories of ANN model is evaluated using traditional evaluation framework. In addition, relevant logical test is carried out with both categories of ANN model. It is shown that selection of suitable input vectors and carrying out of relevant logical test are the two essential components for ANN model development process.