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Simulating and Validating the Traffic of Blackwall Tunnel Using TfL Jam Cam Data and Simulation of Urban Mobility (SUMO) (Short Paper)
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12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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Acknowledgements
I would like to thank Transport for London for providing journey time data through its Open Data programme. I would also thank Dr Sarah Wise, my PhD supervisor, for her outstanding support during this research.
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Chukun Gao. Simulating and Validating the Traffic of Blackwall Tunnel Using TfL Jam Cam Data and Simulation of Urban Mobility (SUMO) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 34:1-34:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.34
https://doi.org/10.4230/LIPIcs.GIScience.2023.34
Abstract
Blackwall Tunnel is one of the most congested roadways in London. By simulating the tunnel and the connecting roads, information can be obtained about the traffic conditions and bottlenecks. In this paper, a model will be created using the Simulation of Urban Mobility (SUMO) tool and traffic flow data gathered from Transport for London (TfL) traffic cameras. The result from the simulation will be compared to the journey time data of Blackwall Tunnel in order to determine the accuracy of simulation.
Subject Classification
ACM Subject Classification
- Information systems → Traffic analysis
Keywords
- Traffic simulation
- Validation
- SUMO
- Blackwall Tunnel
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References
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Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz. Sumo - simulation of urban mobility, 2011.
- Lara Codeca, Raphael Frank, Sebastien Faye, and Thomas Engel. Luxembourg SUMO traffic (LuST) scenario: Traffic demand evaluation. IEEE Intelligent Transportation Systems Magazine, 9(2):52-63, 2017. Conference Name: IEEE Intelligent Transportation Systems Magazine. URL: https://doi.org/10.1109/MITS.2017.2666585.
-
Transport for London. Syndication developer guidelines – transport for london data service, 2012.
-
Transport for London. River crossings: Silvertown tunnel – supporting technical documentation, 2014.
- Huan Min Gan, Senaka Fernando, and Miguel Molina-Solana. Scalable object detection pipeline for traffic cameras: Application to tfl JamCams. Expert Systems with Applications, 182:115154, 2021. URL: https://doi.org/10.1016/j.eswa.2021.115154.
- Hongsheng He, Zhenzhou Shao, and Jindong Tan. Recognition of car makes and models from a single traffic-camera image. IEEE Transactions on Intelligent Transportation Systems, 16(6):3182-3192, 2015. Conference Name: IEEE Transactions on Intelligent Transportation Systems. URL: https://doi.org/10.1109/TITS.2015.2437998.
-
Jasper Kell, Gordon Ridley, and GLC. Blackwall tunnel duplication. Proceedings of the Institution of Civil Engineers, 35(2):253-274, 1966.
- Maria Kontorinaki, Anastasia Spiliopoulou, Claudio Roncoli, and Markos Papageorgiou. First-order traffic flow models incorporating capacity drop: Overview and real-data validation. Transportation Research Part B: Methodological, 106:52-75, 2017. URL: https://doi.org/10.1016/j.trb.2017.10.014.
- Changle Li, Wenwei Yue, Guoqiang Mao, and Zhigang Xu. Congestion propagation based bottleneck identification in urban road networks. IEEE Transactions on Vehicular Technology, 69(5):4827-4841, 2020. URL: https://doi.org/10.1109/TVT.2020.2973404.
- Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wiessner. Microscopic traffic simulation using SUMO. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2575-2582, 2018. ISSN: 2153-0017. URL: https://doi.org/10.1109/ITSC.2018.8569938.
- Tomer Toledo and Haris N. Koutsopoulos. Statistical validation of traffic simulation models. Transportation Research Record, 1876(1):142-150, 2004. Publisher: SAGE Publications Inc. URL: https://doi.org/10.3141/1876-15.
- Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. URL: https://doi.org/10.48550/arXiv.2207.02696.
- Yingjie Xia, Xingmin Shi, Guanghua Song, Qiaolei Geng, and Yuncai Liu. Towards improving quality of video-based vehicle counting method for traffic flow estimation. Signal Processing, 120:672-681, 2016. URL: https://doi.org/10.1016/j.sigpro.2014.10.035.