{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:27:15Z","timestamp":1771068435301,"version":"3.50.1"},"reference-count":13,"publisher":"TIB Open Publishing","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/de\/deed.en"}],"funder":[{"DOI":"10.13039\/100008383","name":"Bundesministerium f\u00fcr Verkehr und Digitale Infrastruktur","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008383","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002946","name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002946","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SUMO Conf Proc"],"abstract":"<jats:p>Trajectory data are great data to work with, since they are the most natural data for traffic. However, they provide considerable challenges when tried to put into a micro-simulation framework such as SUMO. This work here gives an example what had to be done to arrive at a simulation that is driven by these data. Succeeding in this, microscopic tools can be much better tested against real data.<\/jats:p>","DOI":"10.52825\/scp.v6i.2633","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T15:46:39Z","timestamp":1752594399000},"page":"25-32","source":"Crossref","is-referenced-by-count":1,"title":["SUMO Simulation of DLR's Research Intersection"],"prefix":"10.52825","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3620-2715","authenticated-orcid":false,"given":"Yun-Pang","family":"Fl\u00f6tter\u00f6d","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9097-8026","authenticated-orcid":false,"given":"Peter","family":"Wagner","sequence":"additional","affiliation":[]}],"member":"30396","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"64126","unstructured":"J. Treiterer, and J. Myers, \"The hysteresis phenomenon in traffic flow\", Transportation and traffic theory, vol. 6, pp. 13\u201338, 1974."},{"key":"64127","doi-asserted-by":"crossref","unstructured":"B. Coifman, L. Li, and W. Xiao, \"Resurrecting the Lost Vehicle Trajectories of Treiterer and Myers with New Insights into a Controversial Hysteresis\", Transportation Research Record, vol. 2672, no. 20, pp. 25-38, 2018. DOI: 10.1177\/0361198118786473.","DOI":"10.1177\/0361198118786473"},{"key":"64128","unstructured":"V. Alexiadis, J. Colyar, J. Halkias, R. Hranac, and G. McHale, \"The Next Generation Simulation Program\", ITE Journal, vol. 74, no. 8, pp. 22 - 26, 2004."},{"key":"64129","doi-asserted-by":"crossref","unstructured":"E. Barmpounakis, and N. Geroliminis, \"On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment\", Transportation Research Part C: Emerging Technologies, vol. 111, pp. 50-71, 2020. DOI: 10.1016\/j.trc.2019.11.023.","DOI":"10.1016\/j.trc.2019.11.023"},{"key":"64130","doi-asserted-by":"crossref","unstructured":"D. Gloudemans, Y. Wang, J. Ji et al., \"I-24 MOTION: An instrument for freeway traffic science\", Transportation Research Part C: Emerging Technologies, vol. 155, p. 104311, 2023. DOI: 10.1016\/j.trc.2023.104311.","DOI":"10.1016\/j.trc.2023.104311"},{"key":"64131","doi-asserted-by":"crossref","unstructured":"A. Kutsch, M. Margreiter, and K. Bogenberger, \"TUMDOT-MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones\", Data Science for Transportation, vol. 6, no. 2, p. 15, 2024. DOI: 10.1007\/s42421-024-00101-5.","DOI":"10.1007\/s42421-024-00101-5"},{"key":"64132","doi-asserted-by":"crossref","unstructured":"M. Berghaus, S. Lamberty, J. Ehlers, E. Kall\u00f3, and M. Oeser, \"Vehicle trajectory dataset from drone videos including off-ramp and congested traffic \u2013 Analysis of data quality, traffic flow, and accident risk\", Communications in Transportation Research, vol. 4, p. 100133, 2024. DOI: 10.1016\/j.commtr.2024.100133.","DOI":"10.1016\/j.commtr.2024.100133"},{"key":"64133","unstructured":"C. Schicktanz, L. Klitzke, K. Gimm et al., \"DLR Urban Traffic dataset (DLR UT)\", version 1.2.0, Zenodo, 2025. DOI: 10.5281\/zenodo.14773161."},{"key":"64134","unstructured":"P. Alvarez Lopez, O. A. Banse Bueno, M. P. S. Barthauer et al., \"Simulation of Urban Mobility (SUMO) (Version 1.22.0)\", Feb. 2025. [Online]. Available: https:\/\/elib.dlr.de\/212503\/."},{"key":"64135","doi-asserted-by":"crossref","unstructured":"P. A. Lopez, M. Behrisch, L. Bieker-Walz et al., \"Microscopic Traffic Simulation using SUMO\", in The 21st IEEE International Conference on Intelligent Transportation Systems, IEEE, 2018. [Online]. Available: https:\/\/elib.dlr.de\/124092\/.","DOI":"10.1109\/ITSC.2018.8569938"},{"key":"64136","unstructured":"Institute of Transportation Systems of DLR, \"\u2018Research Intersection\u2019 \u2013 a hub for data collection in the field\", 2025. Accessed: Apr. 1, 2025. [Online]. Available: https:\/\/www.dlr.de\/en\/ts\/research-transfer\/research-infrastructure\/test-areas\/acquisition-technology\/research-intersection."},{"key":"64137","unstructured":"VVM, \"VVM - Verification Validation Methods\", 2023. Accessed: Mar. 7, 2025. [Online]. Available: https:\/\/www.vvm-projekt.de\/en\/project."},{"key":"64138","unstructured":"KoFeMo, \"Kombinierte Untersuchung von Feinstaub und Mobilit\u00e4t \u2013 KoFeMo\", 2024. Accessed: Mar. 7, 2025. [Online]. 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