Open AccessDesign of Traffic Volume Forecasting based on Genetic Algorithm
Author(s) -
Archana Potnurwar,
Shailendra S. Aote,
Vrushali Bongirwar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2512.078219
Subject(s) - traffic flow (computer networking) , genetic algorithm , computer science , traffic congestion , traffic volume , volume (thermodynamics) , path (computing) , regression analysis , plan (archaeology) , data mining , transport engineering , engineering , machine learning , geography , physics , computer security , quantum mechanics , programming language , archaeology
The traffic flow forecasting is very important aspect of traffic predication and congestion. It alleviates the increasing congestion problems that cause drivers to shorten the travelling duration required and prevent financial loss. Increasing congestion is one of the severe problems in big city areas. The aspect of traffic prediction is that it may give drivers to plan their traveling time and traveling path, based on the predictive data information they have. The aim is to design locally weighted regression model by proposing a method, which is a combination of Genetic algorithm and locally weighted regression method. This model helps to achieve optimal prediction performance under various traffic condition parameters. The time series model is used to predict the forecast value for the accurate assumption of the traffic volume generation according to the road capacity. The GA model results show these kind of predictions always be useful for highway road authorities.