arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.
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.
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.
STHMoE is a Spatio‑Temporal Hypergraph‑Enhanced Mixture of Experts framework designed for urban traffic forecasting. It separates traffic dynamics into frequency‑, time‑, spatial‑, and higher‑order representations, each handled by a prompt‑guided expert built on a partially frozen large language model. The higher‑order expert uses an adaptive hypergraph module to learn evolving spatial structures, while an entropy‑aware router balances expert usage and fuses outputs, achieving competitive results on ten real‑world traffic benchmarks.
arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, ho...
The paper introduces STDSH-MARL, a multi-agent deep reinforcement learning framework that uses a dual-stage hypergraph attention mechanism to capture spatio-temporal dependencies in corridor traffic signal control. It employs a hybrid discrete action space to jointly set signal phase configurations and green durations, allowing more adaptive timing. Experiments on a corridor network show that STDSH-MARL outperforms state‑of‑the‑art baselines, notably reducing tram waiting times while balancing overall network efficiency, tram priority, and bus service quality.
arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.
The paper introduces a gated graph attention network (GAT) to better explain spatial information flow in short‑term traffic forecasting models. By adding a gate that learns how much of a sensor’s updated state comes from its neighbors versus itself, the authors can progressively withdraw neighbor information and observe its impact on accuracy. Applied to the ST‑MetaNet architecture on data from 498 loop detectors in England, the gated GAT shows that neighbor influence is higher for busy sensors, improves accuracy slightly with mild regularisation, and reveals that the two GAT layers are largely redundant.