Leveraging SUMO for Real-World Traffic Optimization: A Comprehensive Approach

Authors

DOI:

https://doi.org/10.52825/scp.v5i.1120

Keywords:

SUMO, ATSPMs, Calibration, Traffic Management, Traffic Optimization

Abstract

This paper illuminates the utilization of SUMO as a powerful tool for addressing real-world traffic management issues. There is a gap in testing and validating solutions to in-field conditions due to the high cost and complexity of urban and suburban road networks. The validation step is often skipped, which can lead to a higher risk in implementing sophisticated solutions that exist in our multimodal transportation environment. This challenge is addressed by introducing simulations as a crucial preliminary step before real-world application. Accurate simulations require detailed data on intersection geometries, vehicle distribution, and driver behavior to accurately mirror real-world conditions. To meet these criteria, detailed sensor data on trajectories, types of road users, and their locations are extensively employed. This data forms the foundation for calibrated traffic simulations by NoTraffic™ . In conclusion, an in-depth demonstration of the method used to address a real-world traffic problem with SUMO is provided, emphasizing SUMO’s effectiveness in building confidence for deploying solutions in the field.

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References

A. Stevanovic and M. Zlatkovic, “Evaluation of insync adaptive traffic signal control in microsimulation environment,” in 92nd Annual Meeting of the Transportation Research Board, Washington DC, 2013.

K. Udomsilp, T. Arayakarnkul, S. Watarakitpaisarn, P. Komolkiti, J. Rudjanakanoknad, and C. Aswakul. Traffic data analysis on sathorn road with synchro optimization and traffic simulation. DOI: https://doi.org/10.4186/ej.2017.21.6.57, 2017.

J Shadewald and C. Prem. "Quantifying access management benefits using traffic simulation," in Proceeds of the Ninth TRB Conference on the Application of Transportation Planning Methods, 2003, pp. 187-196.

J. Felez, J. Maroto, J. M. Cabanellas, and J. M. Mera. A full-scale simulation model to reproduce urban traffic in real conditions in driving simulators. DOI: https://doi.org/10.1177/0037549713483557, 2013.

P. Alvarez Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wie_ner. Microscopic traffic simulation using sumo. Available online: https://elib.dlr.de/127994/, 2018.

P. Koonce and L. Rodegerdts. Traffic signal timing manual. Available online: https://nacto.org/docs/usdg/signal_timing_manual_fhwa.pdf, 2008.

H. Ceylan and M. G.H. Bell. Traffic signal timing optimisation based on genetic algorithm approach, including drivers' routing. DOI: https://doi.org/10.1016/S0191-2615(03)00015-8, 2004.

T. Balasha and T. Toledo. Simulation-based optimization of actuated traffic signalplans. Available online: https://transp-or.epfl.ch/heart/2014/abstracts/051.pdf, 2015.

B. Wang, G. G. Schultz, G. S. Macfarlane, Dennis L Eggett, and Matthew C Davis. A methodology to detect traffic data anomalies in automated traffic signal performance measures. DOI: https://doi.org/10.3390/futuretransp3040064, 2023.

B. Wang, G. G. Schultz, G. S. Macfarlane, and S. McCuen. Evaluating signal systems using automated traffic signal performance measures. DOI: https://doi.org/10.3390/future-transp2030036, 2022.

A. P. Akgungor and A. G. R. Bullen. Analytical delay models for signalized intersections. Available online: https://nacto.org/docs/usdg/analytical_delay_models_for_signalized_intersections_akgungor.pdf, 1999.

Federal Highway Administration. Automated traffic signal performance measures. Available online: https://ops.fhwa.dot.gov/publications/fhwahop20002/fhwahop20002.pdf, 2020.

C. Bewermeyer, R. Berndt, S. Schellenberg, R. German, and D. Eckhoff. Poster: cosmetic-towards reliable osm to sumo network conversion. Available online: https://www.david-eckhoff.net/pdf/bewermeyer2015cosmetic.pdf, 2015.

M. Schrader, Q. Wang, and J. Bittle. Extension and validation of nema-style dual-ring controller in sumo. DOI: https://doi.org/10.52825/scp.v3i.115, 2022.

B. Higgs, Montasir Abbas, and Alejandra Medina. Analysis of the Wiedemann car following model over different speeds using naturalistic data. Available online: https://onlinepubs.trb.org/onlinepubs/conferences/2011/RSS/3/Higgs,B.pdf, 2011.

B. Mahmood and J. Kianfar. Driver behavior models for heavy vehicles and passenger cars at a work zone. DOI: https://doi.org/10.3390/su11216007, 2019.

B. Ciuffo, V. Punzo, M. Montanino, et al. The calibration of traffic simulation models: Report on the assessment of different goodness of fit measures and optimization algorithms multitude project–cost action tu0903. DOI: https://doi.org/10.2788/7975, 2012.

B.Bryan Higgs, M. Abbas, and A. Medina. Analysis of the Wiedemann car following model over different speeds using naturalistic data. Available online: https://onlinepubs.trb.org/onlinepubs/conferences/2011/RSS/3/Higgs,B.pdf, 2011.

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Published

2024-07-17

How to Cite

Dobrilko, O., & Bublil, A. (2024). Leveraging SUMO for Real-World Traffic Optimization: A Comprehensive Approach. SUMO Conference Proceedings, 5, 179–194. https://doi.org/10.52825/scp.v5i.1120

Conference Proceedings Volume

Section

Conference papers
Received 2024-02-22
Accepted 2024-04-03
Published 2024-07-17