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RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction

Published: 13 November 2024 Publication History

Abstract

In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus ensuring employee productivity and customer satisfaction. Currently, few related works focus on couriers’ level delivery timely rate prediction, and there are complex spatial correlations between couriers and road districts in the express scenario, which makes traditional real-time prediction approaches hard to utilize. To deal with this, we propose a deep spatial-temporal neural network, RCCNet to model spatial-temporal correlations. Specifically, we adopt Node2vec, which can encode the road network-based graph directly to capture spatial correlations between road districts. Further, we calculate couriers’ historical time-series similarity to build a graph and employ graph convolutional networks to capture the correlation between couriers. We also leverage historical sequential information with long short-term memory networks. We conduct experiments with real-world express datasets. Compared with other competitive baseline methods widely used in industry, the experiment results demonstrate its superior performance over multiple baselines.

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  1. RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction

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        cover image ACM Transactions on Intelligent Systems and Technology
        ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 6
        December 2024
        727 pages
        EISSN:2157-6912
        DOI:10.1145/3613712
        • Editor:
        • Huan Liu
        Issue’s Table of Contents

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 13 November 2024
        Online AM: 29 August 2024
        Accepted: 12 August 2024
        Revised: 19 June 2024
        Received: 27 April 2023
        Published in TIST Volume 15, Issue 6

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        Author Tags

        1. Express delivery
        2. delivery timely rate
        3. spatial-temporal prediction

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        • National Nature Science Foundation of China

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