arXiv Machine Learning
An Empirical Survey and Benchmark of Learned Distance Indexes for Road Networks
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arXiv AI
1d ago
COL-Trees: Efficient Hierarchical Object Search in Spatial Networks
The paper introduces COL‑Tree, a compact landmark‑based tree structure designed to accelerate hierarchical object search in spatial networks. It supports diverse query types—such as aggregate k‑nearest neighbor (AkNN), k‑farthest neighbor (kFN), and others—by enabling efficient graph traversal with a more accurate heuristic than Euclidean‑based methods. Experiments on real‑world and synthetic road, terrain, and physics mesh networks show that COL‑Tree outperforms existing approaches by up to three orders of magnitude, with only modest preprocessing overhead.
arXiv Machine Learning
Aug 4
Using Lower-Bound Representations for Trajectory Similarity Learning
arXiv:2608. 01039v1 Announce Type: cross Abstract: Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures.
arXiv Machine Learning
Jun 9
The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers
arXiv:2606. 07587v1 Announce Type: new Abstract: LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by dynamically selecting a model for each query.
arXiv Machine Learning
Jul 1
Capturing Context-Aware Route Choice Semantics for Trajectory Representation 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.
Hugging Face Trending Papers
Jul 29
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
Jun 4
RIDE: An Open Dataset and Benchmark for Train Delay Prediction
arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.