A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit <a rel="external noopener" href="http://research.microsoft.com:80/pubs/164590/KDD12-PopularRoutes.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="ACM Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12</a>
The advances in location-acquisition technologies have led to a myriad of spatial trajectories. These trajectories are usually generated at a low or an irregular frequency due to applications' characteristics or energy saving, leaving the routes between two consecutive points of a single trajectory uncertain (called an uncertain trajectory). In this paper, we present a Route Inference framework based on Collective Knowledge (abbreviated as RICK) to construct the popular routes from uncertain<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2339530.2339562">doi:10.1145/2339530.2339562</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/WeiZP12.html">dblp:conf/kdd/WeiZP12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cn6skuqbbng5je6sclzquxzbze">fatcat:cn6skuqbbng5je6sclzquxzbze</a> </span>
more »... jectories. Explicitly, given a location sequence and a time span, the RICK is able to construct the top-k routes which sequentially pass through the locations within the specified time span, by aggregating such uncertain trajectories in a mutual reinforcement way (i.e., uncertain + uncertain → certain). Our work can benefit trip planning, traffic management, and animal movement studies. The RICK comprises two components: routable graph construction and route inference. First, we explore the spatial and temporal characteristics of uncertain trajectories and construct a routable graph by collaborative learning among the uncertain trajectories. Second, in light of the routable graph, we propose a routing algorithm to construct the top-k routes according to a userspecified query. We have conducted extensive experiments on two real datasets, consisting of Foursquare check-in datasets and taxi trajectories. The results show that RICK is both effective and efficient.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160309080819/http://research.microsoft.com:80/pubs/164590/KDD12-PopularRoutes.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/73/37/7337163c3303391aeb227bbc04bca585a68d9d52.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2339530.2339562"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>