Latest Articles for Traffic Management

Evaluating large language models for enhancing road safety in self-driving cars.

A study on how Park-and-Ride can reduce congestion and emissions.

A novel approach using hypergraphs improves predictions of agent trajectories in various environments.

A data-driven approach to enhance traffic flow and safety at city intersections.

A new framework enhances traffic speed predictions using contextual data.

SceneMotion improves predictions for self-driving cars by considering agent interactions.

A look at optimizing High Occupancy Toll lanes to reduce congestion.

This article examines how vehicles cooperate to improve traffic flow.

A new model predicts traffic flow without relying heavily on sensor data.

SOD-YOLOv8 improves detection of small objects in traffic scenes using advanced techniques.

Innovative methods aim to improve parking management in busy cities.

A framework for classifying diverse driving patterns to improve road safety.

PM-DMNet enhances traffic prediction accuracy and efficiency using advanced techniques.

Research highlights traffic patterns and congestion near UNCC using iterative methods.

A new method improves traffic sensor location efficiency using graph cuts.

A new speed controller for automated vehicles improves traffic management.

Explore the role of tropical matrices in understanding dynamic systems and their stability.

Exploring how switched systems operate and their importance in various applications.

A new GNN method offers faster evaluation of traffic policies for cities.

A new model improves traffic predictions following crashes for better management.

A new system enhances traffic monitoring and management in cities.

Studying how autonomous vehicles can improve traffic flow and reduce congestion.

This study presents a traffic flow model that accounts for connected and autonomous vehicles.

A new approach enhances traffic light management, reducing congestion and improving flow.

Discover how V2X communication transforms road safety and traffic management.

Examining the complexities of scalar conservation laws influenced by abrupt changes.

A new dataset aims to improve traffic signal management using real images and diverse scenarios.

A model focuses on vehicle space to improve traffic management.

STGormer enhances traffic predictions by considering time and space.

A new method for routing jobs efficiently while ensuring system stability.

A look into effective management of HOT lanes to reduce congestion.

A comprehensive look at understanding traffic patterns through complex data analysis.

This study compares different YOLOv5 models in identifying various vehicle types under various conditions.

A study on improving HOT lane efficiency and congestion management.

A new method enhances road traffic predictions by integrating regional knowledge.

A new method enhances traffic forecasting accuracy through data decomposition and neural networks.

A new training method enhances 3D object detection using synthetic and real-world data.

Exploring how collaboration can improve outcomes in congestion games.

TempoKGAT improves prediction accuracy in dynamic relationships using time-sensitive methods.

A study on enhancing navigational recommendations for better urban driving experiences.