@inproceedings{wei2012constructing,
author = {Wei, Ling-Yin and Zheng, Yu and Peng, Wen-Chih},
title = {Constructing Popular Routes from Uncertain Trajectories},
booktitle = {Proceedings of the 18th SIGKDD conference on Knowledge Discovery and Data Mining},
year = {2012},
month = {August},
abstract = {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 trajectories. 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.

The data can be found here.
Please cite this paper when using the dataset.},
url = {http://approjects.co.za/?big=en-us/research/publication/constructing-popular-routes-from-uncertain-trajectories/},
edition = {Proceedings of the 18th SIGKDD conference on Knowledge Discovery and Data Mining},
}