arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.
arXiv:2608.30512v1 Announce Type: cross Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersecti...
arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.
arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.
arXiv:2604.02927v3 Announce Type: replace Abstract: Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within...
arXiv:2608. 13799v1 Announce Type: new Abstract: This paper presents an event-driven learning and benchmarking framework for the Dynamic Multi-Depot Vehicle Routing Problem with progressively revealed requests and evolving vehicle states.