Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method: A Comparability Approach with Comparable Data in Multiple Seasons

Aditya R. Raikwar, Rahul R. Sadawarte, Rishikesh G. More, Rutuja S. Gunjal, Parikshit N. Mahalle and Poonam N. Railkar
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Aditya R. Raikwar: Kashibai Navale College of Engineering, Department of Computer Engineering, Pune, India
Rahul R. Sadawarte: Kashibai Navale College of Engineering, Department of Computer Engineering, Pune, India
Rishikesh G. More: Kashibai Navale College of Engineering, Department of Computer Engineering, Pune, India
Rutuja S. Gunjal: Kashibai Navale College of Engineering, Department of Computer Engineering, Pune, India
Parikshit N. Mahalle: Kashibai Navale College of Engineering, Department of Computer Engineering, Pune, India
Poonam N. Railkar: Kashibai Navale College of Engineering, Department of Computer Engineering, Pune, India

International Journal of Synthetic Emotions (IJSE), 2017, vol. 8, issue 2, 38-50

Abstract: The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.

Date: 2017
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