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Traffic Simulation with Incomplete Data: the Case of Brussels

Published: 21 November 2023 Publication History

Abstract

Intelligent transport systems are intended to support the decision-making process for reducing traffic congestion, traffic accidents, and air pollution, these having an impact on citizens' well-being. Decision-making tasks are complex because of the unpredictable dynamics of urban traffic. To tackle this complexity, simulation tools enable evaluating in silico the impact of policies on urban infrastructures. Accurate and continuous information about traffic is necessary to define simulation models that reflect the dynamics of the real traffic. However, this is not always possible, as data from the physical environment are collected generally by sensors that undergo maintenance or unpredictable temporary failures, resulting in sparse data sets that cannot be used to create accurate simulation models.
We propose an approach for going from sparse data to traffic simulation models. We use the HybridIoT technique to estimate missing traffic data, used to create traffic simulation models, and SUMO, an open-source traffic simulation tool, to simulate traffic. We integrate data provided from two traffic services that operate in the city of Brussels (Belgium). We also simulate the lack of different percentages of data (from 10% to 90%), and evaluate the accuracy of traffic models created using the estimated data.
The outcome of this study is twofold: (i) the definition of a novel traffic simulation model for the city of Brussels by integrating sparse data sets, and (ii) the evaluation of the impact of missing data in the accuracy of traffic simulation models.

References

[1]
J. Argota Sánchez-Vaquerizo. Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona's Traffic with Empirical Data. ISPRS International Journal of Geo-Information, 11(1):24, Dec. 2021. ISSN 2220-9964.
[2]
M. Batty. Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5):817--820, 2018.
[3]
M. Behrisch and P. Hartwig. A comparison of SUMO's count based and countless demand generation tools. SUMO Conference Proceedings, 2:125--131, June 2022. ISSN 2750-4425.
[4]
M. Behrisch, M. Bieker, J. Erdmann, and D. Krajzewicz. Sumo - simulation of urban mobility: An overview. In S. . U. of Oslo Aida Omerovic, R. I. R. T. P. D. A. Simoni, and R. I. R. T. P. G. Bobashev, editors, SIMUL 2011. ThinkMind, October 2011.
[5]
L. Bieker, D. Krajzewicz, A. P. Morra, C. Michelacci, and F. Cartolano. Traffic Simulation for All: A Real World Traffic Scenario from the City of Bologna. In M. Behrisch and M. Weber, editors, Modeling Mobility with Open Data, pages 47--60. Springer, 2015. ISBN 978-3-319-15023-9 978-3-319-15024-6.
[6]
K. Bochenina, A. Taleiko, and L. Ruotsalainen. Simulation-Based Origin-Destination Matrix Reduction: A Case Study of Helsinki City Area. SUMO Conference Proceedings, 4:1--13, June 2023. ISSN 2750-4425.
[7]
L. Codeca, R. Frank, S. Faye, and T. Engel. Luxembourg SUMO Traffic (LuST) Scenario: Traffic Demand Evaluation. IEEE Intelligent Transportation Systems Magazine, 9(2):52--63, 2017. ISSN 1939-1390.
[8]
C. Dalaff, R. Ebendt, J. Erdmann, G. Gurczik, and L. C. Touko Tcheumadjeu. Benchmarking sumo generated traffic simulation results based on geh statistic. In SUMO2013 - The first SUMO User Conference, May 2013.
[9]
A. De Iasio, A. Furno, L. Goglia, and E. Zimeo. A Microservices Platform for Monitoring and Analysis of IoT Traffic Data in Smart Cities. In 2019 IEEE International Conference on Big Data (Big Data), pages 5223--5232, Los Angeles, CA, USA, Dec. 2019. IEEE. ISBN 978-1-72810-858-2.
[10]
M. Fadda, M. Anedda, R. Girau, G. Pau, and D. D. Giusto. A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari. IEEE Internet of Things Journal, 10(3):2373--2390, Feb. 2023. ISSN 2327-4662, 2372--2541.
[11]
R. C. Fan, X. Yang, and J. D. Fay. Using location data to determine traffic information, US patent 6 594 576.
[12]
D. A. Guastella, V. Camps, and M.-P. Gleizes. A cooperative multi-agent system for crowd sensing based estimation in smart cities. IEEE Access, 8:183051--183070, 2020. ISSN 2169-3536.
[13]
I. Ištoka Otković, T. Tollazzi, M. šraml, and D. Varevac. Calibration of the Microsimulation Traffic Model Using Different Neural Network Applications. Future Transportation, 3(1):150--168, Feb. 2023. ISSN 2673-7590.
[14]
A. Keler, W. Sun, and J.-D. Schmöcker. Generating and Calibrating a Microscopic Traffic Flow Simulation Network of Kyoto: First Insights from Simulating Private and Public Transport. SUMO Conference Proceedings, 4:189--195, June 2023. ISSN 2750-4425.
[15]
S. C. Lobo, S. Neumeier, E. M. G. Fernandez, and C. Facchi. Intas - the ingolstadt traffic scenario for sumo, 2020.
[16]
S. Majumdar, M. M. Subhani, B. Roullier, A. Anjum, and R. Zhu. Congestion prediction for smart sustainable cities using IoT and machine learning approaches. Sustainable Cities and Society, 64:102500, Jan. 2021. ISSN 22106707.
[17]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825--2830, 2011.
[18]
M. Rapelli, C. Casetti, and G. Gagliardi. Vehicular Traffic Simulation in the City of Turin From Raw Data. IEEE Transactions on Mobile Computing, 21(12):4656--4666, Dec. 2022. ISSN 1536-1233, 1558--0660, 2161--9875.
[19]
J. Schweizer, C. Poliziani, F. Rupi, D. Morgano, and M. Magi. Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data. ISPRS International Journal of Geo-Information, 10(3):165, Mar. 2021. ISSN 2220-9964.
[20]
T. Tang, C. Li, H. Huang, and H. Shang. A new fundamental diagram theory with the individual difference of the driver's perception ability. Nonlinear Dynamics, 67(3):2255--2265, Feb. 2012. ISSN 0924-090X, 1573-269X.
[21]
L. Wilkinson. Revising the Pareto Chart. The American Statistician, 60(4):332--334, 2006. ISSN 00031305. URL http://www.jstor.org/stable/27643812. Publisher: [American Statistical Association, Taylor & Francis, Ltd.].

Cited By

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  • (2025)Calibration of vehicular traffic simulation models by local optimizationTransportation10.1007/s11116-025-10593-xOnline publication date: 17-Feb-2025
  • (2024)Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic SimulationVehicles10.3390/vehicles60200356:2(747-764)Online publication date: 28-Apr-2024

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      cover image ACM Conferences
      EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives 2023
      November 2023
      50 pages
      ISBN:9798400703478
      DOI:10.1145/3615885
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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      Published: 21 November 2023

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      Author Tags

      1. road traffic simulation
      2. missing data imputation
      3. multi-agent system

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      View all
      • (2025)Calibration of vehicular traffic simulation models by local optimizationTransportation10.1007/s11116-025-10593-xOnline publication date: 17-Feb-2025
      • (2024)Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic SimulationVehicles10.3390/vehicles60200356:2(747-764)Online publication date: 28-Apr-2024

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