Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities

Silva, Matteus Vargas Simão da; Bittencourt, Luiz F.; Rivera, Adín Ramirez

doi:10.5753/courb.2020.12361

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…In the study by Imteaj and Amini 32 , focused on data collection from smartphones' built-in sensors in smart city infrastructure for transportation, health, and emergency services. Silva et al 33 proposed FL for real-time traffic forecasting using roadside units and suggested exploring different models. Lonare et al 34 presented an aggregated approach to predict vehicle traffic, utilizing decentralized local data securely shared with the server and training with spatial parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Munawar,

Piantanakulchai

2024

Sci Rep

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The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared ($$R^2$$ R 2 ). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available.

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Section: Literature Reviewmentioning

confidence: 99%

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Munawar,

Piantanakulchai

2024

Sci Rep

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“…RELATED WORKS The widespread use of Internet of Things (IoT) sensors and devices, in conjunction with artificial intelligence, has resulted in the creation of "smart environments [13]." However, these solutions have high latency conditions and more information transmission from a network standpoint.…”

Section: IImentioning

confidence: 99%

Utilizing Federated Learning for Enhanced Real-Time Traffic Prediction in Smart Urban Environments

Kumari,

Ulmas,

et al. 2024

IJACSA

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Federated Learning (FL), a crucial advancement in smart city technology, combines real-time traffic predictions with the potential to enhance urban mobility. This paper suggests a novel approach to real-time traffic prediction in smart cities: a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) architecture. The investigation started with the systematic collection and preprocessing of a lowresolution dataset (1.6 GB) derived from real-time Closed Circuit Television (CCTV) traffic camera images at significant intersections in Guntur and Vijayawada. The dataset has been cleaned up utilizing min-max normalization to facilitate use. The primary contribution of this study is the hybrid architecture that it develops by fusing RNN to detect temporal dynamics with CNN for geographic extraction of characteristics. While the RNN's recurrent interactions preserve hidden states for sequential processing, the CNN efficiently retrieves high-level spatial information from static traffic images. Weight adjustments and backpropagation are used in the training of the proposed hybrid model in order to enhance real-time predictions that aid in traffic management. Notably, the implementation is done with Python software. The model reaches a testing accuracy of 99.8% by the 100th epoch, demonstrating excellent performance in the results and discussion section. The Mean Absolute Error (MAE) results, which show a 4.5% improvement over existing methods like Long S hort Term Memory (LS TM), S upport Vector Machine (S VM), S parse Auto Encoder (S AE), and Gated Recurrent Unit (GRU), illustrate the efficacy of the model. This demonstrates how well complex patterns may be represented by the model, yielding precise real-time traffic predictions in crowded metropolitan settings. A new era of more precise and effective real-time traffic forecasts is about to begin, thanks to the hybrid CNN-RNN architecture, which is validated by the combined strengths of FL, CNN, and RNN as well as the overall outcomes.

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“…Distinct locations and distinct time in the same location have an effect on the predicted value, this highly dynamic task scenario frequently needs to combine more edge-end information, and for congested vehicles to assign a data set (due to the fact that traffic congestion state and traffic flow are strongly correlated). Furthermore, the location of the camera, speed, and even weather conditions can be included in the input data set for better estimation of traffic behavior [76]. Recently, GNNs have been applied to traffic flow prediction and estimation scenarios, where coordinate information associated with nodes is used to assist in routing to avoid information diffusion [77].…”

Section: B Traffic Status Identificationmentioning

confidence: 99%

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu

Yin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities

Cited by 12 publications

References 19 publications

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Utilizing Federated Learning for Enhanced Real-Time Traffic Prediction in Smart Urban Environments

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

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