88 citations · 305 across the 31 of their papers we have counts for
33 papers
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…
Revisiting mesoscopic traffic flow simulation in SUMO: Limitations, analysis, and an alternative
Ying-Chuan Ni, Alina Akopian, Anastasios Kouvelas +1
Mesoscopic traffic flow models combines the merits of both macroscopic and microscopic models by capturing individual vehicle behavior in great detail and remaining the computation…
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…
ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review
Kevin Riehl, Andres L. Marin, Nikofors Zacharof +6
Scientific peer review increasingly struggles to assess reproducibility at the scale and complexity of modern research output. Evaluating reproducibility requires reconstructing ex…
sumoITScontrol: Traffic Controller Collection for SUMO Traffic Simulations
Kevin Riehl, Anastasios Kouvelas, Michail A. Makridis
Reliable benchmarking is essential for progress in intelligent traffic control research. While microscopic traffic simulators such as SUMO enable detailed modelling of individual v…