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Technol."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers\u2019 package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and overdue-risk evaluation, by leveraging the predicted routes to improve those downstream tasks. In the package pick-up scene, the decision-making of a courier is affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time, and courier\u2019s current location). Furthermore, couriers have different decision preferences on various factors (e.g., time factor, distance factor, and balance of both), based on their own perception of the environments and work experience. In this article, we propose a novel model, named DeepRoute+, to predict couriers\u2019 future package pick-up routes according to the couriers\u2019 decision experience and preference learned from the historical behaviors. Specifically, DeepRoute+ consists of three layers: (1) The representation layer produces experience- and preference-aware representations for the unpicked-up packages, in which a decision preference module can dynamically adjust the importance of factors that affects the courier\u2019s decision under the current situation. (2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. (3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our model.<\/jats:p>","DOI":"10.1145\/3481006","type":"journal-article","created":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T15:07:50Z","timestamp":1641395270000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["DeepRoute+: Modeling Couriers\u2019 Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6130-126X","authenticated-orcid":false,"given":"Haomin","family":"Wen","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China and Beijing KeyLaboratory of Traffic Data Analysis and Mining, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youfang","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China and Beijing KeyLaboratory of Traffic Data Analysis and Mining, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaiyu","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China and Beijing KeyLaboratory of Traffic Data Analysis and Mining, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengnan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, China and Beijing KeyLaboratory of Traffic Data Analysis and Mining, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Department, Cainiao Network, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixia","family":"Wu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Department, Cainiao Network, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Song","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Department, Cainiao Network, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Xu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Department, Cainiao Network, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2012.05.028"},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the Workshop on Negative Dependence in Machine Learning","author":"Bello Irwan","year":"2019","unstructured":"Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi. 2019. 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