The tremendous growth experienced in the total number of automobiles in recent years, without any additional supporting means of transportation architecture, is one of the major issues in smart city development [1]. Due to countless number of cars on road, urban regions are overcrowded which in turn causes multiple effects such as increased air and noise pollution, reduced fuel proficiency and increased velocity of traffic. This scenario stimulates the intersection traffic by major alterations in pace control structures of metropolitan cities and towns [2]. Since these systems further disturb traffic signal control mechanisms and pollution control strategies, logistics and traffic management have become major issues to deal with. In earlier times, traffic signal controlling tools were used in traffic administration system whereas such tools are used for traffic management these days in smart cities. It plays a major role in ensuring the safety of people who experience traffic [3]. Traffic data accumulations act as a significant input in both management and understanding of the traffic. Traffic counting process is commonly executed these days while conventional city or state governments have certain ways to track the count of traffic such as microwave sensors, cameras, radar weapons, and speed guns [4]. Fig. 1 illustrates the structure of ITS.

For a known period of time, researchers are working on automobile traffic prediction and proposed few models to achieve it. In specialized publications, various solutions have been proposed [5] which function in a fair and accurate manner under regular circumstances i.e., no unplanned incidents on road network. But, on many occasions, these methodologies are unprepared to find out the convoluted congestion propagation patterns. This lack of preparation results in erroneous predictions under severe conditions, although exact predictions are required during these important times [6,7]. Such serious conditions occur through multiple factors such as events, traffic threats, extreme weather conditions, and so on. Even though it is not easy to get rid of the suspicious element entirely from traffic estimation, it is possible to reduce the adverse effect of the blockage by considering exogenous data resources [8–10].

The advancements made in technology and unique ideas for smart cities have encouraged great deals of development in smart cities. The application areas of Artificial Intelligence (AI) such as big data, Deep Learning (DL), Machine Learning (ML), and Internet of Things (IoT) [11], have gained importance in assisting technological evolution of smart cities. Amongst them, ML approaches have endowed numerous applications in different fields such as air pollution monitoring and estimation, city planning, energy demand, consumption estimation, mobility management and monitoring of food supply and production estimation, resource distribution, etc. [12,13].

The current article introduces a novel Slime Mould Optimization (SMO) with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, the data is pre-processed to normalize the input data within a range of [0,1] using min-max normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Further, SMO algorithm is utilized for fine tuning the hypervariables involved in BiGRU design. In order to validate the superior prediction performance of the proposed SMOBGRU-TP model, numerous analyses were conducted and the results established the supremacy of the proposed model.

Vijayalakshmi et al. [14] projected an attention-based multi-stage predictive method named Convolution Neural Network(CNN)-Long Short Term Memory (LSTM). The suggested system used spatial and time-based traffic information extracted with the help of CNN and LSTM systems in order to enhance the accuracy of the model. Attention-based method assists in identifying the nearby traffic information, since the speed is an important parameter to forecast the upcoming values of the flow. In literature [15], the authors presented ML and optimization methods to empower an intelligent ecosystem. For validation purpose, a computation was executed in this study with multi-layer perceptron and Particle Swarm Optimization (PSO) approach.

Wang et al. [16] introduced a multi-task DL approach named ‘Multitask Recurrent Graph Convolution Network (MRGCN)’ which precisely forecasts the traffic data in smart cities. Especially, this study presented a multitasking architecture that consists of four major elements such as a task-specific decoder to forecast the traffic flow, a region flow encoding unit to model region flow dynamics, a transition flow encoding unit to explore transition flow correlation, and a context modelling module for contextual combination of two kinds of traffic flow. Khan et al. [17] aimed at developing a data fusion-related traffic congestion control scheme in smart city using DL method. A hybrid mechanism was utilized in this study based on CNN and LSTM frameworks for region-related traffic flow prediction in smart cities. CNN was employed here for spatial dataset categorization, whereas LSTM was applied for temporal dataset classification.

Kuang et al. [18] presented a traffic signal control method based on reinforcement learning using state reduction. At first, a reinforcement learning method was determined according to the previous traffic flow dataset. In addition to this, the study also presented a dual-objective reward operation that might improve the matching and degree decrease vehicle delay. Neelakandan et al. [19] developed a powerful IoT-based traffic predictive model with OWENN approach and traffic signal control scheme using Intel 80,286 microprocessor for smart cities. The presented technique comprised of five stages such as traffic signal control system, categorization, optimization of traffic IoT values, feature extraction and IoT data collection. At first, IoT traffic dataset is gathered from the information. Next, weather, direction, and traffic dataset are extracted. Followed by, the extracted feature is fed as input to the classification model that classifies the location as either heavy traffic or not.

In current study, a new SMOBGRU-TP model has been proposed to forecast the flow of traffic in smart city environment. The presented SMOBGRU-TP model comprises of data pre-processing initially, during when the input data is normalized within a range of [0,1] using min-max normalization approach. Further, SMO-BiGRU model is employed for effective forecasting of traffic in smart cities.

In this section, the TFP outcomes of the proposed SMOBGRU-TP model were analyzed under several aspects. Tab. 1 and Figs. 4 and 5 provide a detailed overview on TFP performance outcomes achieved by the proposed SMOBGRU-TP model and other existing models under different measures. The experimental results indicate that Radial Basis Function (RBF)+Principal Component Analysis (PCA)+PIM and RBF+PCC+PCA+PIM models reached ineffectual outcomes compared to other methods. At the same time, RBF+PIM and RBF+PCC models accomplished slightly enhanced results. Though AT-TFP model reached a Mean Absolute Percentage Error (MAPE) of 21.364%, Mean Square Error (MSE) of 299.636, and Root Mean Square Error (RMSE) of 17.310, the proposed SMOBGRU-TP model attained the least MAPE of 18.560%, MSE of 256.350%, and RMSE of 16.011.

Tab. 2 and Figs. 6 and 7 provide a detailed overview on predictive outcomes accomplished by the proposed SMOBGRU-TP model and other recent models. The experimental outcomes infer that RNN-LSTM and RNN-GRU models reached ineffectual outcomes with maximal error values. Followed by, the cascaded LSTM, cascaded GRU, and autoencoder methodologies produced slightly lesser error values. Though NN, AE-RBF, and AI-TFP models accomplished reasonable error values, the proposed SMOBGRU-TP model attained an effectual performance with a minimal MAPE of 9.523%, MSE of 142.265, and RMSE of 11.927.

Tab. 3 and Fig. 8 shows the predictive results achieved by SMOBGRU-TP model and other recent models under distinct time durations [27–29]. The experimental results exhibit that the proposed SMOBGRU-TP model accomplished the least MAPE under all-time durations. For example, with a time duration of 5 mins, SMOBGRU-TP model provided the least MAPE of 15.894%, whereas RNN-LSTM, RNN-GRU, cascaded LSTM, cascaded GRU, AE, NN, AE-RBF, and AI-TFP models reached high MAPE values such as 15.894%, 13.023%, 11.533%, 11.148%, 11.113%, 10.924%, 10.898%, and 10.528% respectively. Along with that, with a time duration of 30 mins, the proposed SMOBGRU-TP method provided a low MAPE of 17.820%, whereas RNN-LSTM, RNN-GRU, cascaded LSTM, cascaded GRU, AE, NN, AE-RBF, and AI-TFP techniques reached high MAPE values such as 25.073%, 24.995%, 22.787%, 22.174%, 21.761%, 21.656%, 20.957%, and 17.820% correspondingly.

Tab. 4 and Fig. 9 portrays the predictive output produced by SMOBGRU-TP model and other recent models under distinct time durations. The experimental results exhibit that the proposed SMOBGRU-TP model accomplished a minimal MSE under all-time durations. For instance, with a time duration of 5 mins, the proposed SMOBGRU-TP model provided a low MSE of 142.265, while RNN-LSTM, RNN-GRU, cascaded LSTM, cascaded GRU, AE, NN, AE-RBF, and AI-TFP methodologies reached high MSE values such as 188.626, 182.593, 175.741, 175.382, 165.035, 164.913, 162.353, and 157.846 respectively. Likewise, at 30 mins duration, SMOBGRU-TP system provided the least MSE of 235.966, but RNN-LSTM, RNN-GRU, cascaded LSTM, cascaded GRU, AE, NN, AE-RBF, and AI-TFP techniques reached high MSE values such as 452.353, 449.797, 441.446, 428.327, 414.809, 398.998, 372.006, and 289.229 correspondingly.

Tab. 5 and Fig. 10 showcases the predictive results attained by the proposed SMOBGRU-TP model and other recent models under distinct time durations. The experimental results demonstrate that the proposed SMOBGRU-TP model accomplished the least RMSE at all-time durations. For example, for 5 mins time duration, SMOBGRU-TP system provided the least RMSE of 11.927, while RNN-LSTM, RNN-GRU, cascaded LSTM, cascaded GRU, AE, NN, AE-RBF, and AI-TFP techniques reached high RMSE values such as 13.734, 13.513, 13.257, 13.243, 12.847, 12.842, 12.742, and 12.564 correspondingly. In addition, with a time duration of 30 mins, the proposed SMOBGRU-TP approach reached a low RMSE of 15.361, whereas RNN-LSTM, RNN-GRU, cascaded LSTM, cascaded GRU, AE, NN, AE-RBF, and AI-TFP techniques reached high RMSE values such as 21.269, 21.208, 21.011, 20.453, 19.870, 19.209, 18.493, and 17.007 correspondingly. Based on the comprehensive comparative analyses and the simulation results, the proposed SMOBGRU-TP model proved its superiority to other models, in terms of performance.

In current study, a new SMOBGRU-TP model has been proposed to forecast the flow of traffic in smart city environment. In the initial stage of SMOBGRU-TP model, data pre-processing is performed to normalize the input data within a range of [0,1] using min-max normalization approach. BiGRU model is employed for effective forecasting of the traffic in smart cities. At last, SMO algorithm is utilized to fine tune the hyperparameters involved in BiGRU approach. In order to experimentally validate the superiority of the proposed SMOBGRU-TP models in terms of prediction performance, different analyses were conducted. The simulation outcome confirmed the superior results achieved by SMOBGRU-TP model than the existing techniques. In future, the efficiency of SMOBGRU-TP methodology can be improved with the help of hybrid DL models.