SETA: PT data analytics

SETA is a European Horizon 2020 project aiming to change the way mobility is organized, monitored and planned in large metropolitan areas. Using complex data from large numbers of citizens, cars, city sensors and distributed databases, SETA will study and model mobility with a precision and granularity that is impossible with today’s technologies. The results will be used to inform decision makers on how to improve town planning and infrastructure, as well as individual citizens on how to plan their journeys in a more efficient and sustainable way.

SETA is funded by the European Union and runs from 2016 to 2018. The consortium involves partners from industry and academia in the UK, Italy, Spain, Poland and the Netherlands.


For more information please visit: http://www.setamobility.eu

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Publications

2019

Krishnakumari P., Cats O. and van Lint H. (2019). Heuristic Coarsening for Generating Multiscale Transport Networks. IEEE Transactions on Intelligent Transport Systems, in press.

Krishnakumari P., van Lint H., Djukic T. and Cats O. (2019). A Data Driven Method for OD Matrix Estimation. Transportation Research Part C, in press.

Luo D., Cats O. and van Lint H. (2019). Can Passenger Flow Distribution be Estimated Solely based on Network Properties in Public Transport Systems? Transportation. In press.

Luo D., Cats O. and van Lint H. (2019). “Enhanced Complex Network Representation of Public Transport for Accessibility Assessment based on General Transit Feed Specification Data”. TransitData2019, Paris. July 2019.

Yap M., Luo D., Cats O., van Oort N. and Hoogendoorn S. (2019). Where Shall We Sync? Clustering Passenger Flows to Identify Urban Public Transport Hubs and Their Key Synchronization Priorities. Transportation Research Part C, 98, 433-448.

2018

Krishnakumari P., Perotti A., Pinto V., Cats O. and van Lint V. (2018). Understanding Network Traffic States using Transfer Learning. The 21st International IEEE conference on Intelligent Transportation Systems (ITSC), Hawaii , 4-7 November.

Luo D., Bonnetain L., Cats O. and van Lint H. (2018). Constructing Spatiotemporal Load Profiles of Transit Vehicles with Multiple Data Sources. Transportation Research Record, in press.

Luo D., Bonnetain L., Cats O. and van Lint H. (2018). Constructing Spatiotemporal Load Profiles of Transit Vehicles with Multiple Data Sources. Proceedings of the 97th Transportation Research Board Annual Meeting, Washington DC.

Yap M., Luo D. and Cats O. (2018). Using Passenger Flows to Determine Key Interchange Connections. Conference on Advanced Systems in Public Transport (CASPT), Brisbane, Australia. Proceedings of the Conference on Advanced Systems in Public Transport and TransitData (CASPT), Brisbane, Australia.

2017

Luo D., Cats O. and van Lint H. (2017). Analysis of Network-wide Transit Passenger Flows based on Principal Component Analysis. The 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS2017), Naples, 744-749.

Luo D., Cats O. and van Lint H. (2017). Constructing Transit Origin-Destination Matrices using Spatial ClusteringTransportation Research Record, 2652, 39-49.

Luo D., Cats O. and van Lint H. (2017). Constructing Transit Origin-Destination Matrices using Spatial Clustering. Proceedings of the 96th Transportation Research Board Annual Meeting, Washington DC.

Smart Public Transport Lab

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Contact:
 Phone: +31 (0)6 15908644
 E-mail: smartptlab@tudelft.nl

Address:
 P.O. Box 5048
 2600 GA Delft

Visiting address:
 Building 23
 
Stevinweg 1
 2628 CN Delft