Mitigating Traffic Congestion in Smart and Sustainable Cities Using Machine Learning: A Review

Leen, Mikkay Wong Ei; Jafry, Nurul Hanis Aminuddin; Salleh, Narishah Mohamed; Hwang, Ha Jin; Jalil, Nasir Abdul

doi:10.1007/978-3-031-36808-0_21

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…Ei Leen, M. W. et al [22] discussed mitigating visitor congestion in intelligent and sustainable cities. Using device studying is a process that leverages laptop algorithms to enhance the efficiency and throughput of urban site visitor networks.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Ismaeel,

Mary,

Chelliah

et al. 2023

Sustainability

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Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

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Section: Related Workmentioning

confidence: 99%

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Ismaeel,

Mary,

Chelliah

et al. 2023

Sustainability

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“…Accurate forecasting contributes to traffic flow volume control, traffic management, and optimization, as predicting traffic congestion using ML is considered more accurate than traditional methods, which contributes significantly to improving traffic flow, especially at peak times. However, to fully utilize the potential of machine learning in traffic management, issues such as data reliability and model interpretability must be resolved [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion

Arabiat,

Altayeb

2024

IJEECS

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Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.

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“…Rapid urbanization in various countries has caused a growing number of challenges related to traffic congestion and road accidents [1,2]. To address these issues, considerable attention has been paid to smart cities and ITS [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

BOppCL: Blockchain-Enabled Opportunistic Federated Learning Applied in Intelligent Transportation Systems

Li,

Wang,

Zhu

et al. 2023

Electronics

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In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches.

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Mitigating Traffic Congestion in Smart and Sustainable Cities Using Machine Learning: A Review

Cited by 8 publications

References 34 publications

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion

BOppCL: Blockchain-Enabled Opportunistic Federated Learning Applied in Intelligent Transportation Systems

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