3 research outputs found
Co-Clustering Network-Constrained Trajectory Data
Recently, clustering moving object trajectories kept gaining interest from
both the data mining and machine learning communities. This problem, however,
was studied mainly and extensively in the setting where moving objects can move
freely on the euclidean space. In this paper, we study the problem of
clustering trajectories of vehicles whose movement is restricted by the
underlying road network. We model relations between these trajectories and road
segments as a bipartite graph and we try to cluster its vertices. We
demonstrate our approaches on synthetic data and show how it could be useful in
inferring knowledge about the flow dynamics and the behavior of the drivers
using the road network
TPM: Supporting pattern matching queries for road-network trajectory data
With the advent of ubiquitous computing, we can easily collect large scale trajectory data from moving vehicles. This paper presents TPM (Trajectory Pattern Miner), a software aimed at pattern matching queries for road-network trajectory data, which complements existing efforts focusing on (a) a spatio-temporal window query for location-based service or (b) Euclidean space with no restriction. To overcome limitations of prior research, TPM supports three types of pattern matching queries – whole, subpattern, and reverse subpattern matching for road-network trajectories. We demonstrate application scenarios for each type of pattern matching queries using large-scale real-life trajectory data