arXiv:2606. 25709v1 Announce Type: new Abstract: Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees.
arXiv:2608. 00402v1 Announce Type: new Abstract: Estimated Time of Arrival (ETA) prediction is a core component of intelligent transportation systems.
arXiv:2606. 28274v1 Announce Type: cross Abstract: Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks.
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:2607. 19974v1 Announce Type: cross Abstract: The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance.
arXiv:2607. 26467v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch.
arXiv:2607. 16930v1 Announce Type: cross Abstract: Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration.
arXiv:2604. 16084v2 Announce Type: replace-cross Abstract: Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management.
arXiv:2606. 19821v1 Announce Type: new Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks.
arXiv:2405. 17468v3 Announce Type: replace-cross Abstract: Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations.
arXiv:2607. 24056v1 Announce Type: cross Abstract: Network-wide traffic volume estimation typically relies on propagating measurements from fixed sensors, making performance highly dependent on sensor density and limiting deployment in sparsely instrumented networks.
arXiv:2606. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).
arXiv:2608. 04075v1 Announce Type: cross Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time.