Zobrazeno 1 - 10
of 2 909
pro vyhledávání: '"Øzbay, A."'
Autor:
Ozbay, Eren
This paper offers a comprehensive analysis of collaborative bandit algorithms and provides a thorough comparison of their performance. Collaborative bandits aim to improve the performance of contextual bandits by introducing relationships between arm
Externí odkaz:
http://arxiv.org/abs/2410.12086
Autor:
Li, Tao, Bian, Zilin, Lei, Haozhe, Zuo, Fan, Yang, Ya-Ting, Zhu, Quanyan, Li, Zhenning, Ozbay, Kaan
In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper
Externí odkaz:
http://arxiv.org/abs/2408.02208
To enable widespread use of Integrated Sensing and Communication (ISAC) in future communication systems, an important requirement is the ease of integration. A possible way to achieve this is to use existing communication reference signals for sensin
Externí odkaz:
http://arxiv.org/abs/2407.13478
Autor:
Li, Tao, Bian, Zilin, Lei, Haozhe, Zuo, Fan, Yang, Ya-Ting, Zhu, Quanyan, Li, Zhenning, Chen, Zhibin, Ozbay, Kaan
Traditional mobility management strategies emphasize macro-level mobility oversight from traffic-sensing infrastructures, often overlooking safety risks that directly affect road users. To address this, we propose a Digital Twin-based Driver Risk-Awa
Externí odkaz:
http://arxiv.org/abs/2407.15025
While deep learning has shown success in predicting traffic states, most methods treat it as a general prediction task without considering transportation aspects. Recently, graph neural networks have proven effective for this task, but few incorporat
Externí odkaz:
http://arxiv.org/abs/2406.13057
Although traffic prediction has been receiving considerable attention with a number of successes in the context of intelligent transportation systems, the prediction of traffic states over a complex transportation network that contains different road
Externí odkaz:
http://arxiv.org/abs/2406.13038
The rapid growth in terms of the availability of transportation data provides great potential for the introduction of emerging data-driven methodologies into transportation-related research and development efforts. However, advanced data-driven model
Externí odkaz:
http://arxiv.org/abs/2406.15452
Collecting traffic data is crucial for transportation systems and urban planning, and is often more desirable through easy-to-deploy but power-constrained devices, due to the unavailability or high cost of power and network infrastructure. The limite
Externí odkaz:
http://arxiv.org/abs/2401.14504
Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed base on optimization methods. In this paper, we propose a novel physics-informed, lear
Externí odkaz:
http://arxiv.org/abs/2307.06267
Connected vehicles (CVs) can provide numerous new data via vehicle-to-vehicle or vehicle-to-infrastructure communication. These data can in turn be used to facilitate real-time traffic state estimation. In this paper, we focus on ramp queue length es
Externí odkaz:
http://arxiv.org/abs/2305.17921