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Bi-directional Long Short Term Memory Neural Network for Short-Term Traffic Speed Prediction Using Gravitational Search Algorithm

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Abstract

Traffic speed prediction has implications for urban planning, congestion reduction, and intelligent control systems. To maintain a uniform traffic speed and to avoid issues related to traffic, an accurate traffic speed forecast can help in supplying significant information. The capacity to forecast short-term traffic speed is a fundamental part of both Intelligent Transportation System (ITS) and the Internet of Vehicles (IoV). To achieve better accuracy in predicting short-term traffic speed, we introduced a GSA-Bi-LSTM model by optimizing the Bi-directional Long Short-Term Memory (Bi-LSTM) network prediction framework with Gravitational Search Algorithm (GSA) due to its features of fast convergence, great reliability and significant global search ability of parameters. The utilization of the GSA optimization technique is employed to optimize the hyperparameters of the Bi-LSTM model. By making use of the bidirectional properties of Bi-LSTM layers, the model’s architecture aims to enhance prediction accuracy and effectively capture the intricate patterns present in the input data. From the analysis of the experimental results, it becomes evident that the convenience provided by our proposed GSA-Bi-LSTM model surpasses that of conventional models in terms of evaluation metrics. Additionally, it is also noted that GSA has superior optimization capabilities than Particle Swarm Optimization (PSO) in terms of optimizing the Bi-LSTM approach for traffic speed forecasting.

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Availability of data and material

Data used in this research were collected by authors under the University Grants Commission (UGC), New Delhi, India, funded research project ‘Modelling and simulation of vehicular traffic flow problems.’ If there are relevant research needs, the data can be obtained by sending an e-mail to Kranti Kumar (kranti31lu@gmail.com)

Abbreviations

ITS:

Intelligent Transportation System

GSA:

Gravitational Search Algorithm

LSTM:

Long Short Term Memory

PSO:

Particle Swarm Optimization

ARIMA:

Autoregressive Integrated Moving Average

KNN:

K-Nearest Neighbor

KF:

Kalman Filters

SVM:

Support Vector Machines

ANN:

Artificial Neural Network

WNN:

Wavelet Neural Network

DBN:

Deep Belief Network

RNN:

Recurrent Neural Network

GA:

Genetic Algorithm

ELM:

Extreme Learning Machine

MAE:

Mean Absolute Error

MAPE:

Mean Absolute Percentage Error

RMSE:

Root Mean Squared Error

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Acknowledgements

The first author conveys sincere appreciation to the Council of Scientific and Industrial Research (CSIR), New Delhi, India, for their benevolent financial support, which is provided under file number 09/382(0246)/2019-EMR-I.

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Authors and Affiliations

  1. Department of Mathematics, Maharshi Dayanand University, Rohtak, -124001, India

    Bharti Naheliya & Poonam Redhu

  2. School of Liberal Studies, Dr. B.R. Ambedkar University Delhi, Delhi, -110006, India

    Kranti Kumar

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  1. Bharti Naheliya
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  2. Poonam Redhu
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Contributions

BN played a pivotal role in the analysis and development of the original model, contributing extensively to its conceptualization and formulation and was a major contributor in writing the manuscript. PR provided a detailed review and made significant edits to the manuscript. KK conducted a comprehensive review and meticulously edited the manuscript. All authors read and approved the final manuscript.

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Correspondence to Poonam Redhu.

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Naheliya, B., Redhu, P. & Kumar, K. Bi-directional Long Short Term Memory Neural Network for Short-Term Traffic Speed Prediction Using Gravitational Search Algorithm. Int. J. ITS Res. (2024). https://doi.org/10.1007/s13177-024-00398-0

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