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Technol."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>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\u2019 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\u2019 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.<\/jats:p>","DOI":"10.1145\/3690649","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T16:32:17Z","timestamp":1724949137000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2339-2698","authenticated-orcid":false,"given":"Jinhui","family":"Yi","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China and JD Logistics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9626-5676","authenticated-orcid":false,"given":"Huan","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9783-6389","authenticated-orcid":false,"given":"Haotian","family":"Wang","sequence":"additional","affiliation":[{"name":"JD Logistics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9734-6056","authenticated-orcid":false,"given":"Jian","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1109\/TITS.2022.3168865","article-title":"Sequence-to-sequence recurrent graph convolutional networks for traffic estimation and prediction using connected probe vehicle data","volume":"24","author":"Abdelraouf Amr","year":"2022","unstructured":"Amr Abdelraouf, Mohamed Abdel-Aty, and Nada Mahmoud. 2022. 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