Indian Journal of Science and Technology
Year: 2022, Volume: 15, Issue: 47, Pages: 2605-2611
V Sumalatha1*, Dingari Manohar2, C Jayalakshmi3
1Department of Statistics, OSMANIA University, Hyderabad, India
2Department of Mathematics, ANURAG University, Hyderabad, India
3Department of Mathematics, CVR College of Engineering, Hyderabad, India
Email: [email protected]
Received Date:11 August 2022, Accepted Date:30 October 2022, Published Date:21 December 2022
Objectives: To analyze the road traffic in Hyderabad at a selected junction and fit Hybrid model for forecasting the traffic conditions for the next near future also to compare the proposed model with the existing popular models and identifying the best model for forecasting the Hyderabad road traffic. Methods: This study considers the data on traffic flow at 6.no. junction in Amberpet, Hyderabad, Telangana state, India. The traffic data has been considered for peak hours in the morning for 8 A.M to 12 Noon, for 6 days. Classical Time series models and recent revolution in time series modelling - Artificial Neural Networks have been applied to develop a forecasting model. Then we combined these models to obtain the Hybrid model for forecasting the road traffic at the selected point. SPSS and Zytun softwares have been used for the analysis. The best fit statistics such as RMSE, MAPE and MAE have been used to identify the best model. Findings: Our study indicates the efficacy of the new combinatorial model in acquiring more accurate forecasting as compared to independent models. These results can be considered to monitor traffic signals and explore methods to avoid congestion at that junction. Novelty: Though some research has been done in Hyderabad, no work has been done to develop a model for forecasting the Hyderabad road traffic. Our model can give the best forecasts for the Hyderabad road traffic.
Keywords: Multilayer Perceptron; Hybrid model; Exponential smoothing; Artificial Neural Network; Intelligent Transport System
© 2022 Sumalatha et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)
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