Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration Framework

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Intelligent Systems and Technology Pub Date : 2024-06-13 DOI:10.1145/3663484
Fudan Yu, Guozhen Zhang, Haotian Wang, Depeng Jin, Yong Li
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Abstract

Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers’ fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time, and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec.
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利用基于数字孪生的迭代校准框架恢复细粒度快递投递行为
了解快递员完成日常工作的详细过程有助于分析、理解和优化工作流程,因此,恢复快递员的精细工作流程正成为改进快递系统的基本问题之一。虽然可以收集到粗粒度的快递员轨迹和运单投递时间数据,但由于存在时空偏差的噪声数据、缺乏快递员细粒度行为的基本真实数据以及行为之间的复杂关联性,这一问题仍具有挑战性。现有研究通常只关注过程的单一维度,如推断投递时间,而且只能得出低时空分辨率的结果,无法很好地解决这一问题。为了弥补这一差距,我们提出了一种基于数字孪生迭代校准系统(DTRec),用于细粒度快递工作流程恢复。首先,我们提出了一种时空偏差校正算法,系统地改进了现有的运单地址和轨迹停留点校正方法。其次,为了模拟行为之间的复杂关联和固有的物理约束,我们提出了一种基于代理的模型来构建快递员的数字孪生。第三,为了进一步提高恢复性能,我们设计了一个基于数字孪生的迭代校准框架,利用数字孪生的推导结果与真实世界数据恢复结果之间的不一致性来改进基于代理的模型和恢复结果。实验表明,在真实世界数据集上,DTRec 的细粒度准确度比最先进的基线高出 10.8%。该系统已在剑龙物流的工业实践中部署,应用前景广阔。代码可在 https://github.com/tsinghua-fib-lab/Courier-DTRec 上获取。
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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