Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Abstract

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

Neurotechnologies Applied to Society's Perception of Cyber-Physical Systems (CPS) in Smart Cities

“…This involves the use of advanced sensors to collect real-time data on traffic, air quality, weather, waste management, and other urban aspects. Integration of this data into CPS systems for making Hsu et al, 2023;Salih et al, 2023;Gera et al, 2023;Linardatos et al, 2023;Song et al, 2023;Alshbatat, 2023;Holubcik et al, 2023;Goyal et al, 2023;Ismaeel et al, 2023;Moolikagedara et al, 2023 CPS-enabled environmental monitoring and air quality management in smart cities CPS-enabled transportation systems for autonomous vehicles and connected vehicles…”

“…They can also provide recommendations, such as avoiding outdoor activities or using public transportation. Regarding intelligent traffic management, the development of traffic control systems that dynamically adjust based on current conditions is crucial, using optimization algorithms and real-time data analysis to enhance traffic efficiency (Ismaeel et al, 2023). A novel approach involves the classification of traffic patterns based on deep recurrent neural networks, which can effectively capture the dynamic and sequential characteristics of traffic patterns (Malik et al, 2023).…”

Analysis of Urban Residents’ Travelling Characteristics and Hotspots Based on Taxi Trajectory Data

“…Likewise, Ayad et al leveraged advanced deep recurrent neural network techniques, which were trained on extensive resident travel data, to effectively capture the evolving and sequential characteristics of urban traffic flow. This effort resulted in the creation of a highly accurate urban traffic model, achieving an impressive accuracy rate of up to 95% [7]. (3) Trajectory data mining and personalized travel data collection: Traditionally, when mining trajectory data for transportation analysis, researchers relied on data from questionnaires and fixed-route buses, which failed to efficiently capture personalized travel information of residents.…”

Enhancing Brain Stroke Detection: A Novel Deep Neural Network with Weighted Binary Cross Entropy Training

“…This leads to increased diagnostic accuracy and makes stroke diagnosis more rapid and accurate. Innovations in deep learning immediately improve clinical decision-making and offer a chance for early intervention that might potentially prevent long-term consequences and even save lives [9,10].…”