arXiv Machine Learning By Xiao Han, Pinbo Wang, Yuanshao Zhu, Guojiang Shen, Xiangjie Kong

MobiWave: Dispatch-Oriented Graph Wavelets and Drift-Guided Selective Optimization for Autonomous Fleet Rebalancing

Read the original on arXiv Machine Learning →

arXiv:2607. 24365v1 Announce Type: new Abstract: Autonomous fleets enable mobility platforms to coordinate idle vehicles directly, making fleet-wide rebalancing possible.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
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Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast

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Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions

Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets.

arXiv Machine Learning
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GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning

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