SETA

SETA is a H2020 project (http://setamobility.eu) that runs from 2016-2018 in which we will design and build a​ ubiquitous data and service ecosystem for better metropolitan mobility. SETA creates technologies and methodologies set to change the way mobility is organised, monitored and planned in large metropolitan areas. The solutions are based on large, complex dynamic data from millions of citizens, thousands of connected cars, thousands of city sensors and hundreds of distributed databases. The consortium involves partners from 5 countries, UK, Italy, Spain, Poland and The Netherland. DiTTLAB is involved in SETA with two PhD students (Ding Luo, Panchamy Krishnakumari) under supervision of prof.dr. Hans van Lint and dr. Oded Cats.

DiTTLAB Partners: TU Delft

Publications:

  • Luo, D., Cats, O. & van Lint, H (2017). Constructing Transit Origin-Destination Matrices with Spatial Clustering. Transportation Research Record: Journal of the Transportation Research Board, (2652), 39-49. [More] [Digital version]
  • Krishnakumari, P., Nguyen, T., Heydenrijk-Ottens, L., Hai, L. V. & van Lint, H. (2017). Traffic Congestion Pattern Classification Using Multiclass Active Shape Models. Transportation Research Record: Journal of the Transportation Research Board, (2645). [More]
  • Lopez, C., Krishnakumari, P., Leclercq, L., Chiabaut, N. & van Lint, H. (2017). Spatio-temporal Partitioning of Transportation Network Using Travel Time Data. Transportation Research Record: Journal of the Transportation Research Board, (2623), 98-107. [More] [Digital version]
  • Luo, D., Cats, O. & van Lint, H (2017). Analysis of network-wide transit passenger flows based on principal component analysis. In 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pages 744-749. [More] [Digital version]
  • Lopez, C., Leclercq, L., Krishnakumari, P., Chiabaut, N. & van Lint, H. (2017). Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps. Scientific Reports, 7(1), 14029. [More] [Digital version]
  • Luo, D., Bonnetain, L., Cats, O. & van Lint, H (2018). Constructing Spatiotemporal Load Profiles of Transit Vehicles with Multiple Data Sources. In Transportation Research Board 97th Annual Meeting (TRB), Washington, D.C., USA. [More]