Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Abstract

Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning (DL) is a kind of ML technique which yields effective performance on data classification and prediction tasks. With this motivation, the current study introduces a novel Slime Mould Optimization (SMO) model with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, data preprocessing is performed to normalize the input data in the range of [0, 1] using minmax normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Moreover, the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method. The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model's superior performance in terms of prediction compared to existing techniques.

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…Lan Ngoc-Nguyen et al [131] introduced an SMA to detect and monitor a suspension footbridge's overall damage. The experimental results proved that the SMA was more reliable at detecting the damage location and determining the damage's degree than the CS and GA. Hamza MA et al [132] introduced an SMO model with a bidirectional gated recurrent unit (BiGRU) model to forecast traffic conditions in smart cities. The simulation results proved the proposed model's superiority.…”

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

“…Set the road width as , the interval between any time periods in the period as [ 18 , 19 ], and the state equation of traffic flow at the intersection of smart cities is: Where, is the allowed flow of vehicles in the continuous section , and is the time when the green light flashes in the signal period of SCTI. Establishing the state equation of traffic flow at traffic intersections in smart cities can provide real-time traffic flow information, predict congestion and provide accurate and timely data support for traffic managers.…”