6 citations · 9 across the 10 of their papers we have counts for
10 papers
Ground Plane Projection for Improved Traffic Analytics at Intersections
Sajjad Pakdamansavoji, Kumar Vaibhav Jha, Baher Abdulhai +1
Accurate turning movement counts at intersections are important for signal control, traffic management and urban planning. Computer vision systems for automatic turning movement co…
Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
Xiaocan Li, Xiaoyu Wang, Ilia Smirnov +2
Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading…
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
Xiaoyu Wang, Ayal Taitler, Scott Sanner +1
Efficient traffic signal control is essential for managing urban transportation, minimizing congestion, and improving safety and sustainability. Reinforcement Learning (RL) has eme…
Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control
Xiaocan Li, Xiaoyu Wang, Ilia Smirnov +2
Perimeter control (PC) prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous PC allows all access points to a protected region to have identical p…
SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees
Tianyu Shi, Ilia Smirnov, Omar ElSamadisy +1
Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers,…
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
Ta Jiun Ting, Xiaocan Li, Scott Sanner +1
Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have b…
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Xiaocan Li, Ray Coden Mercurius, Ayal Taitler +4
Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical v…
A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control
Xiaoyu Wang, Scott Sanner, Baher Abdulhai
Recent years have witnessed substantial growth in adaptive traffic signal control (ATSC) methodologies that improve transportation network efficiency, especially in branches levera…
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb +3
Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy. Model-based approa…
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model
Tianyu Shi, Yifei Ai, Omar ElSamadisy +1
In the coming years and decades, autonomous vehicles (AVs) will become increasingly prevalent, offering new opportunities for safer and more convenient travel and potentially smart…