Learning Points and Routes to Recommend Trajectories

The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transition patterns between POIs that enable us to recommend probable routes. In addition, a probabilistic model is proposed to combine the results of POI ranking and the POI to POI transitions. We propose a new F$_1$ score on pairs of POIs that capture the order of visits. Empirical results show that our approach improves on recent methods, and demonstrate that combining points and routes enables better trajectory recommendations

Scalable Mining of Common Routes in Mobile Communication Network Traffic Data

A probabilistic method for inferring common routes from mobile communication network traffic data is presented. Besides providing mobility information, valuable in a multitude of application areas, the method has the dual purpose of enabling efficient coarse-graining as well as anonymisation by mapping individual sequences onto common routes. The approach is to represent spatial trajectories by Cell ID sequences that are grouped into routes using locality-sensitive hashing and graph clustering. The method is demonstrated to be scalable, and to accurately group sequences using an evaluation set of GPS tagged data

Discovering private trajectories using background information

Trajectories are spatio-temporal traces of moving objects which contain valuable information to be harvested by spatio-temporal data mining techniques. Applications like city traffic planning, identification of evacuation routes, trend detection, and many more can benefit from trajectory mining. However, the trajectories of individuals often contain private and sensitive information, so anyone who possess trajectory data must take special care when disclosing this data. Removing identifiers from trajectories before the release is not effective against linkage type attacks, and rich sources of background information make it even worse. An alternative is to apply transformation techniques to map the given set of trajectories into another set where the distances are preserved. This way, the actual trajectories are not released, but the distance information can still be used for data mining techniques such as clustering. In this paper, we show that an unknown private trajectory can be reconstructed using the available background information together with the mutual distances released for data mining purposes. The background knowledge is in the form of known trajectories and extra information such as the speed limit. We provide analytical results which bound the number of the known trajectories needed to reconstruct private trajectories. Experiments performed on real trajectory data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds

Privacy risks in trajectory data publishing: reconstructing private trajectories from continuous properties

Location and time information about individuals can be captured through GPS devices, GSM phones, RFID tag readers, and by other similar means. Such data can be pre-processed to obtain trajectories which are sequences of spatio-temporal data points belonging to a moving object. Recently, advanced data mining techniques have been developed for extracting patterns from moving object trajectories to enable applications such as city traffic planning, identification of evacuation routes, trend detection, and many more. However, when special care is not taken, trajectories of individuals may also pose serious privacy risks even after they are de-identified or mapped into other forms. In this paper, we show that an unknown private trajectory can be reconstructed from knowledge of its properties released for data mining, which at first glance may not seem to pose any privacy threats. In particular, we propose a technique to demonstrate how private trajectories can be re-constructed from knowledge of their distances to a bounded set of known trajectories. Experiments performed on real data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds

Quantum trajectories and their statistics for remotely entangled quantum bits

We experimentally and theoretically investigate the quantum trajectories of jointly monitored transmon qubits embedded in spatially separated microwave cavities. Using nearly quantum-noise limited superconducting amplifiers and an optimized setup to reduce signal loss between cavities, we can efficiently track measurement-induced entanglement generation as a continuous process for single realizations of the experiment. The quantum trajectories of transmon qubits naturally split into low and high entanglement classes corresponding to half-parity collapse. The distribution of concurrence is found at any given time and we explore the dynamics of entanglement creation in the state space. The distribution exhibits a sharp cut-off in the high concurrence limit, defining a maximal concurrence boundary. The most likely paths of the qubits' trajectories are also investigated, resulting in three probable paths, gradually projecting the system to two even subspaces and an odd subspace. We also investigate the most likely time for the individual trajectories to reach their most entangled state, and find that there are two solutions for the local maximum, corresponding to the low and high entanglement routes. The theoretical predictions show excellent agreement with the experimental entangled qubit trajectory data.Comment: 11 pages and 4 figure

Generating optimal aircraft trajectories with respect to weather conditions

International audienceTwo major projects have been initiated to improve air traffic management by enabling 4D trajectory planning, whereby the aircraft plan their trajectory both in position and in time. In this paper, we are interested in a Free Flight variant of the concept, whereby airspace users are allowed maximum freedom when selecting routes: aircraft are no longer restricted to fly along airways; rather, they are allowed to fly along optimal routes, from origin to destination, following optimal altitudes, using favourable winds and avoiding hazards. Such optimal routes are good for the environment, for the airlines, and for passengers. The goal of our research is to generate trajectories which minimize congestion and travel time of each aircraft in a way that is fair and efficient. We first optimize the route of a single aircraft relying on an algorithm called Ordered Upwind. And then, with a multi-agent system, we modify trajectories in order to minimize the congestion and to stay as close as possible to the optimal trajectories

Evaluation of long-distance orientation in birds on the basis of migration routes recorded by radar and satellite tracking

Predicted flight trajectories differ depending on which orientation cues are used by migrating birds. Results from radar and satellite tracking of migrating birds can be used to test which of the predicted trajectories shows the best fit with observed flight routes, supporting the use of the associated orientation mechanism. Radar studies of bird migration at the Northeast Passage and the Northwest Passage support the occurrence of migration along sun-compass routes in these polar regions. In contrast, satellite tracking of Brent geese (Branta bernicla) migrating from Iceland across Greenland and from Northwest Europe to Siberia show routes that conform most closely with geographic loxodromes, but which are also profoundly influenced by large-scale topography. These evaluations are discussed in relation to the adaptive values of different routes in different parts of the world. Sun compass routes are favourable mainly for east-west migration at high latitudes. For east-west migration at mid and high latitudes magnetic loxodromes are more favourable than geographic loxodromes in certain regions while the reverse holds in other regions. The geometry of migration routes, as recorded by radar and satellite tracking, may be important for understanding the evolution of the complexity of birds' orientation systems, and for providing clues about the orientation mechanisms guiding the birds on their global journeys