{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T04:30:32Z","timestamp":1691555432764},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,6]]},"abstract":"We study route planning that utilizes historical trajectories to predict a realistic route from a source to a destination on a road network at given departure time. Route planning is a fundamental task in many location-based services. It is challenging to capture latent patterns implied by complex trajectory data for accurate route planning. Recent studies mainly resort to deep learning techniques that incur immense computational costs, especially on massive data, while their effectiveness are complicated to interpret.<\/jats:p>\n \n This paper proposes DRPK, an effective and efficient route planning method that achieves state-of-the-art performance via a series of novel algorithmic designs. In brief, observing that a route planning query (RPQ) with closer source and destination is easier to be accurately predicted, we fulfill a promising idea in DRPK to first detect the key segment of an RPQ by a classification model KSD, in order to split the RPQ into shorter RPQs, and then handle the shorter RPQs by a destination-driven route planning procedure DRP. Both KSD and DRP modules rely on a directed association (DA) indicator, which captures the dependencies between road segments from historical trajectories in a surprisingly intuitive but effective way. Leveraging the DA indicator, we develop a set of well-thought-out key segment concepts that holistically consider historical trajectories and RPQs. KSD is powered by effective encoders to detect high-quality key segments, without inspecting all segments in a road network for efficiency. We conduct extensive experiments on 5 large-scale datasets. DRPK consistently achieves the highest effectiveness, often with a significant margin over existing methods, while being much faster to train. Moreover, DRPK is efficient to handle thousands of online RPQs in a second,\n e.g.<\/jats:italic>\n , 2768 RPQs per second on a PT dataset,\n i.e.<\/jats:italic>\n , 0.36 milliseconds per RPQ.\n <\/jats:p>","DOI":"10.14778\/3603581.3603591","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T19:06:48Z","timestamp":1691521608000},"page":"2512-2524","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Effective and Efficient Route Planning Using Historical Trajectories on Road Networks"],"prefix":"10.14778","volume":"16","author":[{"given":"Wei","family":"Tian","sequence":"first","affiliation":[{"name":"Hong Kong Polytechnic University"}]},{"given":"Jieming","family":"Shi","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University"}]},{"given":"Siqiang","family":"Luo","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Xiamen University"}]},{"given":"Xike","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"}]},{"given":"Yuanhang","family":"Zou","sequence":"additional","affiliation":[{"name":"Tencent Co. 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