88 citations · 305 across the 31 of their papers we have counts for
8 papers · 1 filter
Sample-Efficiency of Kolmogorov-Arnold Networks
Kevin Riehl, Shaimaa K. El-Baklish, Fan Wu +1
Deep reinforcement learning has achieved substantial performance gains over classical control approaches. Yet, a central challenge to learning in real-world applications is acquiri…
Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation
Linghang Sun, Qishen Zhou, Michail A. Makridis +1
The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values for an entire urban network…
Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Data
Qishen Zhou, Yifan Zhang, Michail A. Makridis +3
Inferring network-wide traffic states from sparse observations with high accuracy and trustworthy uncertainty quantification is essential for intelligent transportation systems, ye…
Great GATsBi: Hybrid, Multimodal, Trajectory Forecasting for Bicycles using Anticipation Mechanism
Kevin Riehl, Shaimaa K. El-Baklish, Anastasios Kouvelas +1
Accurate prediction of road user movement is increasingly required by many applications ranging from advanced driver assistance systems to autonomous driving, and especially crucia…
Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach
Qishen Zhou, Yifan Zhang, Michail A. Makridis +3
Network-wide Traffic State Estimation (TSE), which aims to infer a complete image of network traffic states with sparsely deployed sensors, plays a vital role in intelligent transp…
MoGERNN: An Inductive Traffic Predictor for Unobserved Locations
Qishen Zhou, Yifan Zhang, Michail A. Makridis +3
Given a partially observed road network, how can we predict the traffic state of interested unobserved locations? Traffic prediction is crucial for advanced traffic management syst…