Dataset for Scaling Traffic Behaviour: Traffic Analysis Zones Clustering Using Traffic Motivation Data – Estimating the Optimal Cluster Quantity
Description
The purpose of this dataset is to enable the replication of the research results presented in the article:
Paszkowski, J. S., Murawski, J., & Gao, J. (2025). Scaling Traffic Behaviour: Traffic Analysis Zones Clustering Using Traffic Motivation Data – Estimating the Optimal Cluster Quantity. 2025 9th International Conference on Models and Technologies for Intelligent Transportation Systems, 1–6. https://doi.org/10.1109/MT-ITS68460.2025.11223546
The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).
Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.
E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).
- The Chinese part of the project is funded by National Natural Science Foundation of China.
- The Swedish part of the project is funded by Swedish Energy Agency.
- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).
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Additional details
Related works
- Is published in
- Conference paper: 10.1109/MT-ITS68460.2025.11223546 (DOI)
Funding
Dates
- Available
-
2026-06-02