Hybrid Models in Forecasting Short Term Road Traffic

V Sumalatha; Dingari Manohar; C Jayalakshmi

doi:10.17485/IJST/v15i47.1663

Article

Hybrid Models in Forecasting Short Term Road Traffic

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i47.1663

Year: 2022, Volume: 15, Issue: 47, Pages: 2605-2611

Original Article

Hybrid Models in Forecasting Short Term Road Traffic

V Sumalatha^1*, 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

*Corresponding Author
Email: [email protected]

Received Date:11 August 2022, Accepted Date:30 October 2022, Published Date:21 December 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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

References

Yan H, Zhang T, Qi Y, Yu DJJ. Short-term traffic flow prediction based on a hybrid optimization algorithm. Applied Mathematical Modelling. 2022;102:385–404. Available from: https://doi.org/10.1016/j.apm.2021.09.040
Cai L, Chen Q, Cai W, Xu X, Zhou T, Qin J. SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting. IET Intelligent Transport Systems. 2019;13(9):1348–1355. Available from: https://doi.org/10.1049/iet-its.2018.5315
Slimani N, Amghar M, Sbiti N. Deep learning and time series analysis application on traffic flow forecasting. Journal of Theoretical and Applied Information Technology. 2022;100(5). Available from: http://www.jatit.org/volumes/Vol100No5/5Vol100No5.pdf
Mirzahossein H, Gholampour I, Sajadi SR. A hybrid deep and machine learning model for short-term traffic volume forecasting of adjacent intersections. IET Intelligent Transport Systems. 2022. Available from: https://doi.org/10.1049/itr2.12224
Tran QH, Fang YM, Chou TY, Hoang TV, Wang CT, Vu VT, et al. Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach. Sustainability. 2022;14(10):6351. Available from: https://doi.org/10.3390/su14106351
Sumalatha V, Dingari M, Jayalakshmi C. Short term road traffic flow forecasting using multi layer perceptron neural networks. AIP Conference Proceedings. 2020;2246:20070.
Rusull, Abduljabbar H, Dia PW. Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data. Scientifc Reports-natureportfolio. 2021;11:23899.

Copyright

© 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)