The DOI matches its Crossref record.
Microscopic Traffic Simulation using SUMO
Abstract
Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 26
- 10.1287/trsc.1100.0367 · DOI
- flötteröd, 2006, Sumo-cadyts calibration with limited data quality, Proceedings of the SUMO User Conference-Towards Simulation for Autonomous Mobility, 155
- flötteröd, 2016, Experiment study on the evacuation of bomb alert with sumo, Proceedings of the SUMO User Conference-Traffic Mobility and Logistics, 39
- flötteröd, 2011, Route choice calibration from multi-point vehicle stream measurements, 2nd International Conference on Models and Technologies for Intelligent Transportation Systems
- horni, 2016, Multi-Agent Transport Simulation MATSim, 10.5334/baw · DOI · Paper page
- krajzewicz, 2012, Recent development and applications of SUMO-Simulation of Urban MObility, International Journal on Advances in Systems and Measurements, 5, 128
- krajzewicz, 2014, Second generation of pollutant emission models for sumo. In: SUMO2014-Second SUMO User Conference, Reports of the DLR-Institute of Transportation Systems
- krauss, 1998, Microscopic Modeling of Traffic Flow Investigation of Collision Free Vehicle Dynamics
- manual, 2010, Hcm2010
- rummel, 2017, Replication of the hbs autobahn with sumo, SUMO 2017-Towards Simulation for Autonomous Mobility, 171
- daamen, 2014, Traffic Simulation and Data Validation Methods and Applications, 10.1201/b17440 · doi:10.1201/b17440
- 10.3141/1876-07 · DOI
- erdmann, 2012, Online-kalibrierung einer mikroskopischen verkehrssimulation, Proceedings of VIMOS 2012
- 2018, Technical University of Munich Fidens-Trust between Cooperative Systems in Application to VANETs web site
- 2018, FHWA Traffic Analysis Tools
- fellendorf, 1994, Vissim: A microscopic simulation tool to evaluate actuated signal control including bus priority, 64th Meeting of Institute of Transportation Engineers, 1
- brilon, 2001, Handbuch für die bemessung von straBenverkehrsan-lagen, Schlussbericht zum Forschungsauftrag FE, 2, r96a
- bieker, 2014, Traffic simulation for all: a real world traffic scenario from the city of bologna, SUMO 2014
- flötteröd, 2008, Traffic State Estimation with Multi-agent Simulations
- 10.1109/VNC.2014.7013309 · DOI · Paper page
- 2018, Demand/Activity-based Demand Generation web site
- 10.1109/TMC.2010.133 · DOI · Paper page
- 10.1016/j.physa.2014.09.061 · doi:10.1016/j.physa.2014.09.061
- todosiev, 1963, The action point model of the driver-vehicle system
- 10.1145/1400713.1400740 · DOI · Paper page
- varschen, 2006, Mikroskopische modellierung der personenverkehrsnachfrage auf basis von zeitverwen-dungstagebuchern, Stadt Region Land-Heft 81-Tagungsband AMUS 2006, 81, 63
Cited by
Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342
Dense reinforcement learning for safety validation of autonomous vehicles
Feng, S., Sun, H., Yan, X., Zhu, H., Zou, Z., Shen, S., & Liu, H. X. (2023) · Nature · doi:10.1038/s41586-023-05732-2 · Cited by 587
A digital twin smart city for citizen feedback
White, G., Zink, A., Codecá, L., & Clarke, S. (2021) · Cities · doi:10.1016/j.cities.2020.103064 · Cited by 548
Large language models empowered agent-based modeling and simulation: a survey and perspectives
Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03611-3 · Cited by 294
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
Li, Q., Peng, Z., Feng, L., Zhang, Q., Xue, Z., & Zhou, B. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3190471 · Cited by 246
Metadata
- Authors
- Lopez, P. A., Wiessner, E., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y.-P., Hilbrich, R., Lucken, L., Rummel, J., & Wagner, P.
- Venue
- 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 2575-2582
- OpenAlex
- W2903709398
- Cited by
- 3,297
- References
- 0
- FWCI
- 284.42
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Microscopic Traffic Simulation using SUMO. https://app.cochat.ai/papers/10.1109/itsc.2018.8569938
- The paper
- Lopez, P. A., Wiessner, E., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y.-P., Hilbrich, R., Lucken, L., Rummel, J., & Wagner, P. (2018). Microscopic Traffic Simulation using SUMO. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). https://doi.org/10.1109/itsc.2018.8569938
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.