{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:09:58Z","timestamp":1775912998683,"version":"3.50.1"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:p>Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in traffic management, urban computing, public safety, and so on. Recently, the availability of massive trajectory data and the success of deep learning motivate a plethora of deep traffic prediction studies. However, the existing neural-network-based approaches tend to ignore the correlations between multiple types of moving objects located in the same spatio-temporal traffic area, which is suboptimal for traffic prediction analytics.<\/jats:p>\n          <jats:p>\n            In this paper, we propose a multi-source deep traffic prediction framework over spatio-temporal trajectory data, termed as\n            <jats:bold>MDTP.<\/jats:bold>\n            The framework includes two phases: spatio-temporal feature modeling and multi-source bridging. We present an enhanced graph convolutional network (GCN) model combined with long short-term memory network (LSTM) to capture the spatial dependencies and temporal dynamics of traffic in the feature modeling phase. In the multi-source bridging phase, we propose two methods, Sum and Concat, to connect the learned features from different trajectory data sources. Extensive experiments on two real-life datasets show that MDTP i) has superior efficiency, compared with classical time-series methods, machine learning methods, and state-of-the-art neural-network-based approaches; ii) offers a significant performance improvement over the single-source traffic prediction approach; and iii) performs traffic predictions in seconds even on tens of millions of trajectory data. we develop\n            <jats:bold>\n              MDTP\n              <jats:sup>+<\/jats:sup>\n            <\/jats:bold>\n            , a user-friendly interactive system to demonstrate traffic prediction analysis.\n          <\/jats:p>","DOI":"10.14778\/3457390.3457394","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T22:48:38Z","timestamp":1634856518000},"page":"1289-1297","source":"Crossref","is-referenced-by-count":50,"title":["MDTP"],"prefix":"10.14778","volume":"14","author":[{"given":"Ziquan","family":"Fang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, Chinaa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Pan","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuntao","family":"Du","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304225"},{"key":"e_1_2_1_2_1","first-page":"447","article-title":"Prediction based traffic management in a metropolitan area","volume":"7","author":"Chavhan Suresh","year":"2020","journal-title":"TTE"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_4_1","volume-title":"Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values. arXiv preprint arXiv:2005.11627","author":"Cui Zhiyong","year":"2020"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3332290"},{"key":"e_1_2_1_6_1","unstructured":"Jilin Hu Chenjuan Guo Bin Yang and Christian S Jensen. 2019. 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