Zobrazeno 1 - 10
of 565
pro vyhledávání: '"Jin, Depeng"'
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper, we propose a reinforcement learning method tailored to solve large-sc
Externí odkaz:
http://arxiv.org/abs/2409.01588
Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. However, existing mobility trajectory generation methods still require real-
Externí odkaz:
http://arxiv.org/abs/2407.16729
With the development of artificial intelligence techniques, transportation system optimization is evolving from traditional methods relying on expert experience to simulation and learning-based decision optimization methods. Learning-based optimizati
Externí odkaz:
http://arxiv.org/abs/2406.10661
In the research of Intelligent Transportation Systems (ITS), traffic simulation is a key procedure for the evaluation of new methods and optimization of strategies. However, existing traffic simulation systems face two challenges. First, how to balan
Externí odkaz:
http://arxiv.org/abs/2405.12520
Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread in the network. Existing approaches such as suspending users or broadca
Externí odkaz:
http://arxiv.org/abs/2403.09217
Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demograp
Externí odkaz:
http://arxiv.org/abs/2403.09197
Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fo
Externí odkaz:
http://arxiv.org/abs/2402.17161
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel generative pre-training framework, GPD, for spatio-temporal few-shot le
Externí odkaz:
http://arxiv.org/abs/2402.11922
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthroughs in pretrained natural language models that enable one model to handl
Externí odkaz:
http://arxiv.org/abs/2402.11838
The growing complexity of next-generation networks exacerbates the modeling and algorithmic flaws of conventional network optimization methodology. In this paper, we propose a mobile network digital twin (MNDT) architecture for 6G networks. To addres
Externí odkaz:
http://arxiv.org/abs/2311.12273