Design of Traffic Volume Forecasting based on Genetic Algorithm
Archana Potnurwar1, Shailendra S. Aote2, Vrushali Bongirwar3 

1Dr. Archana Potnurwar, Priyadarshini Institute of Engineering and Technology, Nagpur, Maharastra, India.
2Dr. Shailendra S. Aote, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharastra, India.
3Vrushali Bongirwar, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharastra, India. 

Manuscript received on 01 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 4264-4268 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2512078219/19©BEIESP | DOI: 10.35940/ijrte.B2512.078219
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Abstract: 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.
Keywords: Traffic Forecasting, Traffic Management Algorithms, Genetic Algorithms, Prediction

Scope of the Article:
Algorithm Engineering