GeoLife Trajectories (© Microsoft)

Theoretical (Analytical):

Practical (Implementation):

Literature Work:

Overview

Often times, trajectories such as walking humans or driving cars are snapped to a road network for simplification and aggregation purposes. Public transportation also operates along a predefined network. The contextualization of this movement data can be used to expose and analyze temporal patterns in traffic flows.

 

Problem Statement

The frequency of occurence is a common attribute to visualize on a road network. However, other data attributes such as the speed, delays, or gas consumption are also available for a deeper analysis.
Hence, visualizing multiple temporally dependent attributes along a road network to draw insights from urban movement data remains a challenging, but equally promising task.

Tasks

  • Evaluate and compare existing approaches for road network visualizations
  • Develop a novel interactive, visual analysis technique to visualize and explore multiple data attribubes along a road network
  • Compare your technique to other existing approaches

Requirements

  • Interest in spatiotemporal data analysis
  • Basic knowledge about information visualization, data mining and geographic information systems
  • Good programming skills in: JavaScript/TypeScript, D3.js, Python

 

Scope/Duration/Start

  • Scope: Bachelor / Master
  • 3 Months Project, 3 / 6 Months Thesis
  • Start: immediately

Contact

References

  • A Survey of Traffic Data Visualization. W. Chen et al.. IEEE Transactions on Intelligent Transportation Systems, 16 (2015). DOI: 10.1109/TITS.2015.2436897
  • T-Watcher: A New Visual Analytic System for Effective Traffic Surveillance. J. Pu et al.. IEEE 14th International Conference on Mobile Data Management (2013). DOI: 10.1109/MDM.2013.23
  • A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data. W. Chen, Z. Huang, F. Wu, M. Zhu, H. Guan, & R. Maciejewski. IEEE Trans Vis Comput Graph. (2018). DOI: 10.1109/TVCG.2017.2758362
  • Interactive Visualization of Traffic Dynamics Based on Trajectory Data.G. A. M. Gomes, E. Santos and C. A. Vidal. 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (2017). DOI: 10.1007/s10462-019-09736-1