Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique

Anjaneyulu, Mohandu; Kubendiran, Mohan

doi:10.3390/su15010074

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…Abdullah et al [11] designed a bi-directional RNN (BRNN) employing GRU. The method employs a BRNN to fake and predict traffic congestion in the smart areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Alruban,

Mengash,

Eltahir

et al. 2024

IEEE Access

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Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial Hummingbird Optimization Algorithm with Hierarchical Deep Learning for Traffic Management (AHOA-HDLTM) technique in the ITS environment. The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the Hierarchical Extreme Learning Machine (HELM) model can be used. The HELM extracts complex features and patterns, with an additional Artificial Hummingbird Optimization Algorithm (AHOA)-based hyperparameter selection process to enhance predictive outcomes. The simulation result analysis under different traffic data demonstrates the better performance of the AHOA-HDLTM technique over existing models. The hierarchical structure of the HELM model along with AHOA-based hyperparameter tuning helps to accomplish enhanced prediction performance. The AHOA-HDLTM technique presents a robust solution for traffic management in ITS, showcasing enhanced performance in forecasting traffic patterns and congestion. The AHOA-HDLTM technique can be used in various smart cities and urban regions. Its abilities in real-time traffic flow prediction can be helpful in the design of efficient, sustainable, and resilient transportation networks.

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“…Abdullah et al [11] designed a bi-directional RNN (BRNN) employing GRU. The method employs a BRNN to fake and predict traffic congestion in the smart areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Alruban,

Mengash,

Eltahir

et al. 2024

IEEE Access

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“…This part describes the common procedure of GRU. It is an innovative form of Standard RNN [29]. The LSTM contains 3 gates which will not preserve the interior cell layer but are combined into the hidden layer (HL) of the RNN.…”

Section: Crowd Density Classification Using Grumentioning

confidence: 99%

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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Smart Cities refer to urban areas which exploits recent technologies for improving the performance, sustainability, and livability of their infrastructure and services. Crowd Density Analysis (CDA), a vital component of Smart Cities, involves the use of sensors, cameras, and data analytics to monitor and analyze the density and movement of people in public spaces. CDA utilizing DL harnesses the control of neural networks to mechanically and exactly evaluate the density of crowds in numerous settings, mainly in smart cities. DL techniques like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), are trained on vast datasets of crowd videos or images to learn complex designs and features. These models can forecast crowd density levels, recognize crowd anomalies, and offer real-time visions into crowd behavior. This study designs an Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities (AICDA-SSC) technique. The aim of the AICDA-SSC method is to analyze the crowd density and classify it into multiple classes by the use of hyperparameter-tuned DL models. To accomplish this, the AICDA-SSC technique applies contrast enhancement using the CLAHE approach. Besides, the complex and intrinsic features can be derived by the use of the Inception v3 model and its hyperparameters can be chosen by the use of the marine predator's algorithm (MPA). For crowd density detection and classification, the AICDA-SSC technique applies a gated recurrent unit (GRU) model. Finally, a chaotic sooty tern optimizer algorithm (CSTOA) based hyperparameter selection procedure takes place to increase the effectiveness of the GRU system. The experimental evaluation of the AICDA-SSC technique takes place on a crowd-density image dataset. The experimentation values showcase the superior performance of the AICDA-SSC method to the recently developed DL models.

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“…These controls utilize real-time data, coordinating various methods based on short-term traffic flow forecasting to manage demand and mitigate accident risks [35]. For instance, Anjaneyulu and Kubendiran's proposed study presents a hybrid Xception support vector machine (XPSVM) classifier model with a high accuracy rate for short-term traffic congestion prediction [36]. Abdullah et al proposed a bidirectional recurrent neural network (BRNN) using gated recurrent units (GRUs) for simulating and forecasting traffic congestion in smart cities, aiming to improve traffic management efficiency [37].…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Hashmi,

Ud-Din,

Khan

et al. 2024

Infrastructures

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Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

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Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique

Cited by 12 publications

References 24 publications

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

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