Incremental Frequent Route Based Trajectory Prediction

Abstract

Recent technological trends enable modern traffic prediction and management systems in which the analysis and prediction of movements of objects is essential. To this extent the present paper proposes IncCCFR-a novel, incremental approach for managing, mining, and predicting the incrementally evolving trajectories of moving objects. In addition to reduced mining and storage costs, a key advantage of the incremental approach is its ability to combine multiple temporally relevant mining results from the past to capture temporal and periodic regularities in movement. The approach and its variants are empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction.

A Comparative Study of Frequent Pattern Mining with Trajectory Data

“…Methods such as the grid-based [ 19 , 25 , 26 ] or clustering-based method [ 27 , 28 ] may be employed to segment the trajectories in spatial aspects in preparation for further studies. For example, a work by Tsoukatos and Gunopulos in 2001 used the grid-based method, where ordered sequences of rectangular regions are used to define sequential patterns [ 25 ].…”

Robust methods for fusing heterogeneous spatiotemporal data

“…For instance, Li et al (2012) developed an approach to discover periodic behaviors in animal movements using Fourier Transform and autocorrelation; however, their approach does not handle the application of more variable movements that occur in sporadic temporal windows, which limits the types of patterns they can discover. Bachmann et al (2013) use density or frequency analysis for the purpose of trajectory prediction, while others employ historical trend information for anomaly detection (Fanaswala and Krishnamurthy. 2013).…”

Mobility Collector

“…There are several applications of positional data in the transportation science environment, such as (1) studying the movement of groups of users within a city or a larger area to verify whether the spatial syntax of that place is relevant for the current transportation network status (Ratti 2004), (2) predicting movement of masses or individuals (Ashbrook and Starner 2002;Bachmann, Borgelt, and Gidófalvi 2013;Gidófalvi et al 2011;Gidófalvi and Dong 2012) and (3) automatically inferring a user's transportation mode (Zheng et al 2008). However, the applications depend on the amount of information that can be extracted from a data-set and the information content is directly linked to the data collection process.…”