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Anti-Attack Intrusion Detection Model Based on MPNN and Traffic Spatiotemporal Characteristics
Considering the robustness and accuracy of conventional intrusion detection models are easily influenced by adversarial attacks, this work proposes...
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ISTGCN: Integrated spatio-temporal modeling for traffic prediction using traffic graph convolution network
To effectively estimate traffic patterns, spatial-temporal information must consider the complex spatial connections on road networks and...
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Traffic signal optimization framework using interpretable machine learning technique under heterogeneous-autonomy traffic environment
Recent advancements in the industrial revolution and artificial intelligence have aided in the development of novel approaches that have considerable...
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Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression
In recent decades, there has been substantial population growth, leading to a higher volume of vehicles on the roadways. This has contributed to...
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Smart traffic control: machine learning for dynamic road traffic management in urban environments
Roadside and outside environmental elements contribute to the road traffic setting's highly dynamic and turbulent nature. The human factor, primarily...
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Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters
Timely and accurate large-scale traffic prediction has gained increasing importance for traffic management. However, it is a challenging task due to...
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A Deep Learning Approach for Classifying Network Connected IoT Devices Using Communication Traffic Characteristics
The Internet of Things can be considered a technological revolution and has successfully merged the physical world with the digital world. However,...
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An Overview Based on the Overall Architecture of Traffic Forecasting
With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is...
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Probabilistic spatio-temporal graph convolutional network for traffic forecasting
Forecasting traffic flow is crucial for Intelligent Traffic Systems (ITS), traffic control, and traffic management systems. Complex spatial and...
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Internet Traffic Prediction Model
AbstractMany modern machine learning tools are inefficient due to the pronounced nonlinearity of traffic changes and nonstationarity. For this, the...
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Traffic prediction for diverse edge IoT data using graph network
More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart...
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Multi-weighted graph 3D convolution network for traffic prediction
Predicting future traffic state (e.g., traffic speed, volume, travel time, etc.) accurately is highly desirable for traffic management and control....
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Behavior Characteristics of Crosswalk in Traffic Thinking
Nowadays, with the rapid development of society, people travel more and more convenient, more and more cars on urban roads, followed by the increase...
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Spatiotemporal synchronous dynamic graph attention network for traffic flow forecasting
Traffic flow forecasting (TFF) is crucial for effective urban planning and traffic management. Most modeling approaches in TFF ignore the dynamic...
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Real-time traffic, accident, and potholes detection by deep learning techniques: a modern approach for traffic management
The practical applications of social media have raised the bar for real-time event detection all over the globe. It has been deemed useful for...
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DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network
A traffic network possesses all the basic characteristics of networks, as well as its own distinct features, which have research significance. In...
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Period Extraction for Traffic Flow Prediction
Due to the particularity of “Tourist chartered Buses, Liner Buses and Dangerous Goods Transport Vehicles” (“TLD Vehicles”), traffic accidents will...
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WKNN-FDCNN method for big data driven traffic flow prediction in ITS
Traffic prediction is a vital paradigm in intelligent transport system (ITS) due to the increase in traffic flow. The big data traffic flow...
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Scaling law of real traffic jams under varying travel demand
The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban...
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EcoLight+: a novel multi-modal data fusion for enhanced eco-friendly traffic signal control driven by urban traffic noise prediction
Urban traffic congestion is of utmost importance for modern societies due to population and economic growth. Thus, it contributes to environmental...
