Latest Articles for Transportation Systems

A look at how Ideal Flow Networks simplify and optimize various systems.

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

Learn how graph signal processing analyzes data in various fields.

Clarifying operational limits ensures safety for self-driving cars.

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

This study evaluates changes in network connections and their impact on optimal paths.

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

New AI method improves travel data collection for better city planning.

A look at equity in urban transport access amid rising challenges.

A fresh approach to better predict human movements in urban areas.

Learn about new techniques for efficient biharmonic distance estimation in networks.

Integrating future events enhances accuracy in energy forecasting methods.

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

Understanding key concepts for monitoring connections in oriented graphs.

A method for tracking evolving connections in networks for better decision-making.

A study on adjusting centrality in networks for various applications.

A new approach to help self-driving cars make safer decisions on the road.

MemFusionMap improves real-time HD mapping using past frame information.

LSSDM offers an effective solution for filling gaps in multivariate time series data.

An overview of temporal forests and their significance in tracking changing connections.

Cities must adapt transportation systems to handle increased flooding from climate change.

A deep dive into the world of electronic maps and their applications.

A new method ensures safe vehicle platooning despite delays.

Cities use cameras and Digital Twin tech to maintain cleanliness in public areas.

A clear look at how self-driving cars operate and ensure safety.

A new algorithm minimizes regret while adhering to complex rules in reinforcement learning.

Learn how distance sensitivity oracles improve route planning in networks.

Discover how vehicles interact in traffic networks and achieve balance.

A new approach to studying mixed graphs using integrated adjacency matrices.

Learn how cities forecast movement patterns through advanced predictive methods.

Learn how to efficiently update Katz centrality scores in changing networks.

A study reveals how cultural differences shape pedestrian crossing habits.

A better method for traffic estimation with limited data in urban areas.

New methods improve Graph Neural Networks for better insights.

An analysis of bike sharing usage in six major cities over a month.