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

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

URL: http://arxiv.org/abs/2408.17258v1
Date: Fri, 30 Aug 2024 12:56:17 GMT
Title: Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach
Authors: Tong Nie, Junlin He, Yuewen Mei, Guoyang Qin, Guilong Li, Jian Sun, Wei Ma,
Abstract summary: E-commerce and urbanization has significantly intensified delivery operations in urban areas. Data-driven predictive methods, especially those utilizing machine learning techniques, have emerged to handle these problems. This paper formulates this problem as a graph-based graph learning learning task.
Score: 40.357070798871675
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 problem that has not yet been sufficiently studied is the joint estimation and prediction of city-wide delivery demand. To this end, we formulate this problem as a graph-based spatiotemporal learning task. First, a 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, we extract general geospatial knowledge encodings from the unstructured locational data and integrate them into the demand predictor. Last, to encourage the cross-city transferability of the model, an inductive training scheme is developed in an end-to-end routine. Extensive empirical 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 these challenging tasks.

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