arXiv subjects

Shaimaa K. El-Baklish

Publications and source records attributed to Shaimaa K. El-Baklish.

4 recordsLinked to original sources

arXiv2026

Sample-Efficiency of Kolmogorov-Arnold Networks

Deep reinforcement learning has achieved substantial performance gains over classical control approaches. Yet, a central challenge to learning in real-world applications is acquiring costly samples. Kolmogorov-Arnold Networks are a recently proposed architecture that can learn physical relationships in control problems effectively, with significantly higher parameter efficiency and interpretability when compared to Multi-Layer-Perceptron architectures. In this work, we systematically study sample-efficiency using computational experiments, covering the Feynman dataset and the Gymnasium RL benchmark. The results show that similar performance can be achieved with 40% fewer samples using the Kolmogorov-Arnold architecture, and that relative performance improvements up to 50% occur during the training process. The observed gains are robust to varying levels of noise in rewards. These results highlight the potential of the Kolmogorov-Arnold architectures for more sample-efficient reinforcement learning. Code: https://github.com/DerKevinRiehl/neurips26_kan_training

cs.LG↗

arXiv2026

Distributive Perimetral Queue Balancing Mechanisms: Towards Equitable Urban Traffic Gating and Fair Perimeter Control

Perimeter control is an effective urban traffic management strategy that regulates inflow to congested urban regions using aggregate network dynamics. While existing approaches primarily optimize system-level efficiency, such as total travel time or network throughput, they often overlook equity considerations, leading to uneven delay distributions across entry points. This work integrates fairness objectives into perimeter control design through explicit queue balancing mechanisms.A large-scale, microscopic case study of the Financial District in the San Francisco urban network is used to evaluate both performance and implementation challenges. The results demonstrate conventional perimeter control not only reduces total and internal delays but can also improve fairness metrics (Harsanyian, Rawlsian, Utilitarian, Egalitarian). Building on this observation, queue balancing strategies match conventional performance while yielding measurable fairness improvements, especially in heterogeneous demand scenarios, where congestion is unevenly distributed across entry points. The proposed framework contributes toward equitable control design for emerging intelligent transportation systems and higher user acceptance for those.

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arXiv2025

Great GATsBi: Hybrid, Multimodal, Trajectory Forecasting for Bicycles using Anticipation Mechanism

Accurate prediction of road user movement is increasingly required by many applications ranging from advanced driver assistance systems to autonomous driving, and especially crucial for road safety. Even though most traffic accident fatalities account to bicycles, they have received little attention, as previous work focused mainly on pedestrians and motorized vehicles. In this work, we present the Great GATsBi, a domain-knowledge-based, hybrid, multimodal trajectory prediction framework for bicycles. The model incorporates both physics-based modeling (inspired by motorized vehicles) and social-based modeling (inspired by pedestrian movements) to explicitly account for the dual nature of bicycle movement. The social interactions are modeled with a graph attention network, and include decayed historical, but also anticipated, future trajectory data of a bicycles neighborhood, following recent insights from psychological and social studies. The results indicate that the proposed ensemble of physics models -- performing well in the short-term predictions -- and social models -- performing well in the long-term predictions -- exceeds state-of-the-art performance. We also conducted a controlled mass-cycling experiment to demonstrate the framework's performance when forecasting bicycle trajectories and modeling social interactions with road users.

cs.LG↗

arXiv2025

Driving Towards Stability and Efficiency: A Variable Time Gap Strategy for Adaptive Cruise Control

Automated vehicle technologies offer a promising avenue for enhancing traffic efficiency, safety, and energy consumption. Among these, Adaptive Cruise Control (ACC) systems stand out as a prevalent form of automation on today's roads, with their time gap settings holding paramount importance. While decreasing the average time headway tends to enhance traffic capacity, it simultaneously raises concerns regarding safety and string stability. This study introduces a novel variable time gap feedback control policy aimed at striking a balance between maintaining a minimum time gap setting under equilibrium car-following conditions, thereby improving traffic capacity, while ensuring string stability to mitigate disturbances away from the equilibrium flow. Leveraging nonlinear $H_\infty$ control technique, the strategy employs a variable time gap component as the manipulated control signal, complemented by a constant time gap component that predominates during car-following equilibrium. The effectiveness of the proposed scheme is evaluated against its constant time-gap counterpart calibrated using field platoon data from the OpenACC dataset. Through numerical and traffic simulations, our findings illustrate that the proposed algorithm effectively dampens perturbations within vehicle platoons, leading to a more efficient and safer mixed traffic flow.

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