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Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this article, we aim to detect whether a takeaway rider meets an accident on a certain time period based on trajectories of food delivery and riders\u2019 contextual information. The food delivery data has a heterogeneous information structure and carries contextual information such as weather and delivery history, and trajectory data are collected as a spatial-temporal sequence. In this article, we propose a\n            <jats:bold>TakeAway<\/jats:bold>\n            <jats:bold>Rider<\/jats:bold>\n            <jats:bold>Accident<\/jats:bold>\n            detection fusion network\n            <jats:bold>TARA-Net<\/jats:bold>\n            to jointly model these heterogeneous and spatial-temporal sequence data. We utilize the residual network to extract basic contextual information features and take advantage of a transformer encoder to capture trajectory features. These embedding features are concatenated into a pyramidal feed-forward neural network. We jointly train the above three components to combine the benefits of spatial-temporal trajectory data and sparse basic contextual data for early detecting traffic accidents. Furthermore, although traffic accidents rarely happen in food delivery, we propose a sampling mechanism to alleviate the imbalance of samples when training the model. We evaluate the model on a transportation mode classification dataset Geolife and a real-world\n            <jats:italic>Ele.me<\/jats:italic>\n            dataset with over 3 million riders. The experimental results show that the proposed model is superior to the state-of-the-art.\n          <\/jats:p>","DOI":"10.1145\/3457218","type":"journal-article","created":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T19:41:05Z","timestamp":1639251665000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["TARA-Net: A Fusion Network for Detecting Takeaway Rider Accidents"],"prefix":"10.1145","volume":"12","author":[{"given":"Yifan","family":"He","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Lei","family":"Fu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Anhui","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Southern Queensland, Queesland, Australia"}]},{"given":"Ting","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2021,12,11]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E. 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