mlsanityData to 14 Oct 2026

Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach

arXiv 2408.17258 ↗First posted 30 Aug 2024cs.LGcited by 6

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Abstract (arXiv)

The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning techniques, have emerged to handle these complexities in urban delivery demand management problems. One particularly pressing issue that has yet to be sufficiently addressed is the joint estimation and prediction of city-wide delivery demand, as well as the generalization of the model to new cities. To this end, we formulate this problem as a transferable graph-based spatiotemporal learning task. First, an individual-collective message-passing neural network model is formalized to capture the interaction between demand patterns of associated regions. Second, by exploiting recent advances in large language models (LLMs), we extract general geospatial knowledge encodings from the unstructured locational data using the embedding generated by LLMs. Last, to encourage the cross-city generalization of the model, we integrate the encoding into the demand predictor in a transferable way. Comprehensive empirical evaluation results on two real-world delivery datasets, including eight cities in China and the US, demonstrate that our model significantly outperforms state-of-the-art baselines in accuracy, efficiency, and transferability.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 60 of its 102 references that are in the map. First posted on arXiv 30 Aug 2024.

Builds on

60 of 102 references.

References · 60

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50 most cited of 60 All 60 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  2. 2025Urban Computing in the Era of Large Language Modelscited by 2
  3. 2025Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Studycited by 1
  4. 2026Virtual Traffic Police: Large Language Model-Augmented Traffic Signal Control for Unforeseen Incidentscited by 1
  5. 2026Earth Science Foundation Models: From Perception to Reasoning and Discoverycited by 0
  6. 2026Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demandcited by 0