6 papers
Can the Waymo Open Motion Dataset Support Realistic Behavioral Modeling? A Validation Study with Naturalistic Trajectories
Yanlin Zhang, Sungyong Chung, Nachuan Li +4
The Waymo Open Motion Dataset (WOMD) has become a popular resource for data-driven modeling of autonomous vehicles (AVs) behavior. However, its validity for behavioral analysis rem…
Traffic Shaping and Hysteresis Mitigation Using Deep Reinforcement Learning in a Connected Driving Environment
Rami Ammourah, Alireza Talebpour
A multi-agent deep reinforcement learning-based framework for traffic shaping. The proposed framework offers a key advantage over existing congestion management strategies which is…
Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network
Mohammadreza Khajeh-Hosseini, Alireza Talebpour
This study proposes a deep learning methodology to predict the propagation of traffic shockwaves. The input to the deep neural network is time-space diagram of the study segment, a…
Assessing Strong String Stability of Constant Spacing Policy under Speed Limit Fluctuations
Sina Arefizadeh, Aria Hasanzadezonuzy, Alireza Talebpour +1
The speed limit changes frequently throughout the transportation network, due to either safety (e.g., change in geometry) or congestion management (e.g., speed harmonization system…
Collaborative Platooning of Automated Vehicles Using Variable Time-Gaps
Aria HasanzadeZonuzy, Sina Arefizadeh, Alireza Talebpour +2
Connected automated vehicles (CAVs) could potentially be coordinated to safely attain the maximum traffic flow on roadways under dynamic traffic patterns, such as those engendered…
Platooning in the Presence of a Speed Drop: A Generalized Control Model
Sina Arefizadeh, Alireza Talebpour, Igor Zelenko
The positive impacts of platooning on travel time reliability, congestion, emissions, and energy consumption have been shown for homogeneous roadway segments. However, speed limit…