24 research outputs found
Inferring destination from mobility data
Destination prediction in a moving vehicle has several applications such as alternative route recommendations even in cases where the driver has not entered their destination into the system. In this paper a hierarchical approach to destination prediction is presented. A Discrete Time Markov Chain model is used to make an initial prediction of a general region the vehicle might be travelling to. Following that a more complex Bayesian Inference Model is used to make a fine grained prediction within that destination region. The model is tested on a dataset of 442 taxis operating in Porto, Portugal. Experiments are run on two maps. One is a smaller map concentrating specificially on trips within the Porto city centre and surrounding areas. The second map covers a much larger area going as far as Lisbon. We achieve predictions for Porto with average distance error of less than 0.6 km from early on in the trip and less than 1.6 km dropping to less than 1 km for the wider area
Spatio-Temporal Techniques for User Identification by means of GPS Mobility Data
One of the greatest concerns related to the popularity of GPS-enabled devices
and applications is the increasing availability of the personal location
information generated by them and shared with application and service
providers. Moreover, people tend to have regular routines and be characterized
by a set of "significant places", thus making it possible to identify a user
from his/her mobility data.
In this paper we present a series of techniques for identifying individuals
from their GPS movements. More specifically, we study the uniqueness of GPS
information for three popular datasets, and we provide a detailed analysis of
the discriminatory power of speed, direction and distance of travel. Most
importantly, we present a simple yet effective technique for the identification
of users from location information that are not included in the original
dataset used for training, thus raising important privacy concerns for the
management of location datasets.Comment: 11 pages, 8 figure
Privacy preserving path recommendation for moving user on location based service
With the increasing adoption of location based services, privacy is becoming a major concern. To hide the identity and location of a request on location based service, most methods consider a set of users in a reasonable region so as to confuse their requests. When there are not enough users, the cloaking region needs expanding to a larger area or the response needs delay. Either way degrades the quality-of-service. In this paper, we tackle the privacy problem in a predication way by recommending a privacy-preserving path for a requester. We consider the popular navigation application, where users may continuously query different location based servers during their movements. Based on a set of metrics on privacy, distance and the quality of services that a LBS requester often desires, a secure path is computed for each request according to user's preference, and can be dynamically adjusted when the situation is changed. A set of experiments are performed to verify our method and the relationship between parameters are discussed in details. We also discuss how to apply our method into practical applications. © 2013 IEEE.published_or_final_versio