TraV: an Interactive Trajectory Exploration System for Massive Data Sets

EasyChair Preprint 1409

Abstract

The proliferation of modern GPS-enabled devices like smartphones have led to significant research interest in large-scale trajectory exploration, which aims to identify all nearby trajectories of a given input trajectory. Trajectory exploration is beneficial, for example, in identifying incorrect road network information or in assisting users when traveling in unfamiliar geographical regions as it can reveal the popularity of certain routes/trajectories. In this study, we develop an interactive trajectory exploration system, named TraV. TraV allows users to easily plot and explore trajectories using an interactive Graphical User Interface (GUI) containing a map of the geographical region. Under the hood, TraV applies the Hidden Markov Model to (optionally) calibrate the user input trajectory and then makes use of the massively parallel execution capabilities of modern hardware to quickly identify nearby trajectories to the input provided by the user. In order to ensure a seamless user experience, TraV adopts a progressive execution model that contrast to the conventional “querybefore-process” model. Demonstration participants will gain first-hand experience with TraV and its ability to calibrate user input and analyze billions of trajectories obtained from Grab taxi drivers (normalized) in Singapore.

Keyphrases: GUI, query processing, trajectory

@booklet{EasyChair:1409,
  author    = {Jieliang Ang and Tianyuan Fu and Johns Paul and Shuhao Zhang and Bingsheng He and Teddy Sison David Wenceslao and Sienyi Tan},
  title     = {TraV: an Interactive Trajectory Exploration System for Massive Data Sets},
  howpublished = {EasyChair Preprint 1409},
  year      = {EasyChair, 2019}}