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Research Article | Open Access

Swarm intelligence freeway–urban trajectories (SWIFTraj) Dataset – Part I: Dataset description and applications

Yu Han1, Xinkai Ji2, Chen Qian2, Le Zhang3( ), Pan Liu2
Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511455, China
School of Transportation, Southeast University, Nanjing 210089, China
School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
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Abstract

This study presents a detailed description and characterization of swarm intelligence freeway–urban trajectories (SWIFTraj), a new open-source vehicle trajectory dataset constructed from videos recorded by a swarm of 16 drones equipped with 5.4 K-resolution cameras. The dataset distinguishes itself from existing open-source trajectory datasets in several aspects. First, it provides long-distance continuous trajectories of up to 4.5 km on a freeway, enabling in-depth investigation of traffic phenomena and their spatial and temporal evolution. Second, the data collection site covers an integrated network comprising a long freeway corridor and parts of its connected urban network, facilitating traffic analysis and modeling from a network perspective. The potential applications of the dataset for transportation research—including traffic flow analysis, modeling, and control at multiple scales, as well as topics related to autonomous driving—are thoroughly discussed. Finally, SWIFTraj is released as a freely accessible open-source dataset to support and accelerate future research in the transportation community.

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Communications in Transportation Research
Article number: 9640035

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Cite this article:
Han Y, Ji X, Qian C, et al. Swarm intelligence freeway–urban trajectories (SWIFTraj) Dataset – Part I: Dataset description and applications. Communications in Transportation Research, 2026, 6(3): 9640035. https://doi.org/10.26599/COMMTR.2026.9640035

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Received: 24 February 2026
Revised: 26 April 2026
Accepted: 10 June 2026
Published: 30 September 2026
© The Author(s) 2026.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0 http://creativecommons.org/licenses/by/4.0/).