Zobrazeno 1 - 10
of 2 100
pro vyhledávání: '"Koutsopoulos A"'
Autor:
Koutsopoulos, Iordanis
We study the problem of entanglement distribution in terms of maximizing a utility function that captures the total fidelity of end-to-end entanglements in a two-link linear quantum network with a source, a repeater, and a destination. The nodes have
Externí odkaz:
http://arxiv.org/abs/2407.09171
Autor:
Zhuang, Dingyi, Wang, Qingyi, Zheng, Yunhan, Guo, Xiaotong, Wang, Shenhao, Koutsopoulos, Haris N, Zhao, Jinhua
Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers. A typical example is the prediction of communities' travel mode share by acc
Externí odkaz:
http://arxiv.org/abs/2405.14079
We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident occurrences.
Externí odkaz:
http://arxiv.org/abs/2311.00164
High-resolution location ("heartbeat") data of transit fleet vehicles is a relatively new data source for many transit agencies. On its surface, the heartbeat data can provide a wealth of information about all operational details of a recorded transi
Externí odkaz:
http://arxiv.org/abs/2305.15545
Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably exists in travel demand prediction. To fill this gap, this study propo
Externí odkaz:
http://arxiv.org/abs/2303.04040
Understanding passengers' path choice behavior in urban rail systems is a prerequisite for effective operations and planning. This paper attempts bridging the gap by proposing a probabilistic approach to infer passengers' path choice behavior in urba
Externí odkaz:
http://arxiv.org/abs/2301.03808
Commuting is an important part of daily life. With the gradual recovery from COVID-19 and more people returning to work from the office, the transmission of COVID-19 during commuting becomes a concern. Recent emerging mobility services (such as ride-
Externí odkaz:
http://arxiv.org/abs/2301.02594
This paper proposes a stochastic framework to evaluate the performance of public transit systems under short random service suspensions. We aim to derive closed-form formulations of the mean and variance of the queue length and waiting time. A bulk-s
Externí odkaz:
http://arxiv.org/abs/2301.00918
This study proposes a mixed-integer programming formulation to model the individual-based path (IPR) recommendation problem during public transit service disruptions with the objective of minimizing system travel time and respecting passengers' path
Externí odkaz:
http://arxiv.org/abs/2301.00916
Shared mobility on demand (MoD) services are receiving increased attention as many high volume ride-hailing companies are offering shared services (e.g. UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous vehicles (AVs) promises
Externí odkaz:
http://arxiv.org/abs/2211.16656