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Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,11,30]]},"abstract":"<jats:p>In urban transportation systems, transfer stations refer to hubs connecting a variety of bus and subway lines and, thus, are the most important nodes in transportation networks. The pervasive availability of large-scale travel traces of passengers, collected from automated fare collection (AFC) systems, has provided unprecedented opportunities for understanding citywide transfer patterns, which can benefit smart transportation, such as smart route recommendation to avoid crowded lines, and dynamic bus scheduling to enhance transportation efficiency. To this end, in this article, we provide a systematic study of the measurement, patterns, and modeling of spatiotemporal dynamics of passenger transfers. Along this line, we develop a data-driven analytical system for modeling the transfer volumes of each transfer station. More specifically, we first identify and quantify the discriminative patterns of spatiotemporal dynamics of passenger transfers by utilizing heterogeneous sources of transfer related data for each station. Also, we develop a multi-task spatiotemporal learning model for predicting the transfer volumes of a specific station at a specific time period. Moreover, we further leverage the predictive model of passenger transfers to provide crowdedness-aware route recommendations. Finally, we conduct the extensive evaluations with a variety of real-world data. Experimental results demonstrate the effectiveness of our proposed modeling method and its applications for smart transportation.<\/jats:p>","DOI":"10.1145\/3232229","type":"journal-article","created":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T13:05:30Z","timestamp":1541768730000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["SmartTransfer"],"prefix":"10.1145","volume":"9","author":[{"given":"Bowen","family":"Du","sequence":"first","affiliation":[{"name":"Beihang University, Haidian District, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifeng","family":"Cui","sequence":"additional","affiliation":[{"name":"Beihang University, Haidian District, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Missouri-Rolla, Rolla, MO, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runxing","family":"Zhong","sequence":"additional","affiliation":[{"name":"Beihang University, Haidian District, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers University, Newark, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11,8]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"41","article-title":"Multi-task feature learning","volume":"19","author":"Argyriou Andreas","year":"2007","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5040-8"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Rich Caruana. 1998. 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