Meng Wang

Professor

Technische Universität Dresden

Germany

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Professor Meng Wang is a faculty member at Technische Universität Dresden, Germany. His research areas include traffic signal optimization, vehicle trajectory control, and privacy-preserving technologies in transportation systems. Recent publications highlight his work on traffic flow models, cooperative automated vehicles, and deep learning approaches for urban traffic prediction.

Meng Wang

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Articles (28)

Real-time privacy-preserving coordination for cross-carrier truck platooning

Stochastic traffic signal optimization and robust vehicle trajectory control in uncertain mixed traffic flow

Constrained traffic signal control under competing public transport priority requests via safe reinforcement learning

A first-order link-based flow model with variable speed limits and capacity drops for freeway networks

Joint optimisation of bus trajectories and signal priority for multi-line transit corridors

A link-based flow model with turn-level queue transmission and time-varying free-flow speed for urban road networks

Hierarchical Predictive Control of Network Traffic Signals Using Link Transmission Model With Queue Dynamics

A platoon formation algorithm for intersections with blue phase control in mixed traffic

High-precision vehicle trajectory data from an intersection in Shanghai: A unique dataset for microscopic traffic flow studies collected by drone and GNSS receiver

A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)

An Introduction to the Chair of Traffic Process Automation [Its Research Lab]

On data-induced CO2 emissions of vehicle automation: An overlooked emission source

A behaviourally underpinned approach for two-dimensional vehicular trajectory reconstruction with constrained optimal control

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach

Measuring Sociality in Driving Interaction

Sequencing-Enabled Hierarchical Cooperative CAV On-Ramp Merging Control With Enhanced Stability and Feasibility

An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden

A flock-like two-dimensional cooperative vehicle formation model based on potential functions

Memory-Anticipation Strategy to Compensate for Communication and Actuation Delays for Strings-Stable Platooning

Microscopic Traffic Modeling Inside Intersections: Interactions Between Drivers

Unprotected Left-Turn Behavior Model Capturing Path Variations at Intersections

A physics-informed reinforcement learning-based strategy for local and coordinated ramp metering

Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

Online Optimization of Gear Shift and Velocity for Eco-Driving Using Adaptive Dynamic Programming

Hierarchical Optimal Maneuver Planning and Trajectory Control at On-Ramps With Multiple Mainstream Lanes

Probabilistic Risk Metric for Highway Driving Leveraging Multi-Modal Trajectory Predictions

A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections

Collaborators (7)

Daniel Görges

Xinwei Wang

Riender Happee

Bart van Arem

Yang Zhou

Claudio Roncoli

Travis Waller

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