Can Yang

Ph.D. student in Geoinformatics

KTH, Royal Institute of Technology in Sweden

Email: cyang [at] kth.se

Address: Teknikringen 10, 11428 Stockholm

About me

I am a Ph.D. student in the Division of Geoinformatics, Department of Urban Planning and Environment at KTH, Royal Institute of Technology in Sweden. My research interest spans the following fields:

  • Trajectory pattern mining and recognition
  • Machine learning and deep learning
  • Map matching
  • Visual analytics

My current work is concentrated on extraction, analysis and visualization of mobility patterns from large collections of trajectories.

Before the Ph.D. study, I obtained bachelor degree in Civil Engineering from Beijing Jiaotong University in 2012 and master degree in Transportation and Geoinformation from KTH in 2014. (Curriculum Vitae)

Open source algorithm

  • Fast map matching : a high performance algorithm for matching GPS point to road network.

Publications

Journal articles

Conference papers

  • Silvino Cumbane, Can Yang and Gyözö Gidofalvi. A framework for traffic prediction integrated with deep learning. In the 8th Symposium of the European Association for Research in Transportation, Budapest, 2019 [pdf]

  • Can Yang and Gyözö Gidofalvi. Trajectory quality assessment based on movement feature stability. In International Symposium on Location-Based Big Data, Tokyo, 2019 [pdf]

  • Can Yang and Gyözö Gidofalvi. Classification of regional dominant movement patterns in trajectories with a convolutional neural network. In GIScience Workshop on Spatial big data and machine learning, Australia, 2018 [pdf]

  • Can Yang and Gyözö Gidofalvi. Comparative mining and clustering of temporal route visit profiles. In GIScience Workshop on Analysis of Movement Data (AMD2018), Australia, 2018 [pdf]

  • Can Yang and Gyözö Gidofalvi. Interactive Visual Exploration of Most Likely Movements. In Visually-supported Computational Movement Analysis (VCMA) workshop in AGILE conference, 2016 [pdf] [ppt]

  • Can Yang, Xiaoliang Ma, Yifang Ban. Demonstration of intelligent transport applications using freight transport GPS data. In Transportation Research Board (TRB) 95th Annual Meeting, 2016 [pdf]

Internship

  • 2018.06-2018.08 : Internship in DiDi, the leading mobile transportation company in China. I worked on trajectory quality assessment and outlier detection, where billions of points were processed in a distributed computing environment using Spark.

Reviewer of journals

Teaching

Screenshots of programs

  • GPSAnimation designed with OpenGL (support millions of observations)