Integration Traffic Signal Control From Synchro to SUMO
DOI:
https://doi.org/10.52825/scp.v5i.1112Keywords:
Synchro, SUMO, Traffic Signal Control, Traffic SimulationAbstract
This study investigates the feasibility and challenges of transferring traffic signal control schemes from the macroscopic signal timing optimization tool Synchro to the microscopic traffic simulator SUMO, focusing on Downtown Seattle as a case study. The research assesses the process of sharing and importing traffic signal timing plans, a crucial aspect of transportation simulations, between these two platforms. We conduct a detailed analysis of the traffic signal characteristics and data formats unique to each simulator and identify elements suitable for conversion. Subsequently, a four-stage framework is developed for semi-automatic integration of traffic signal control between the two. Our results indicate a successful conversion rate of approximately 85% of signalized intersections from Synchro to SUMO. This research not only illustrates the challenges and solutions in converting signal control across different platforms but also paves the way for future studies aimed at improving the interoperability of various traffic simulation tools.
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N. N. Nor Azlan and M. Md Rohani, “Overview Of Application Of Traffic Simulation Model,” MATEC Web of Conferences, vol. 150, p. 03006, 2018, doi: https://doi.org/10.1051/matecconf/201815003006.
P. A. Lopez et al., “Microscopic Traffic Simulation using SUMO,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Nov. 2018, pp. 2575–2582. doi: https://doi.org/10.1109/ITSC.2018.8569938.
X. Zhou and J. Taylor, “DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration,” Cogent Engineering, vol. 1, no. 1, p. 961345, Dec. 2014, doi: https://doi.org/10.1080/23311916.2014.961345.
“PTV Visum.” Accessed: Apr. 24, 2024. [Online]. Available: https://www.ptvgroup.com/en-us/products/ptv-visum
“PTV Vissim.” Accessed: Apr. 24, 2019. [Online]. Available: http://vision-traffic.ptvgroup.com/en-us/products/ptv-vissim/
“MATsim - SUMO Documentation.” Accessed: Jul. 27, 2021. [Online]. Available: https://sumo.dlr.de/docs/Networks/Import/MATsim.html
“Unity Real-Time Development Platform | 3D, 2D, VR & AR Engine,” Unity. Accessed: Feb. 10, 2024. [Online]. Available: https://unity.com
J. Ban, O. Angah, Y. Zhang, Q. Guo, Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART), and University of Washington, “A Multiscale Simulation Platform for Connected and Automated Transportation Systems,” Dec. 2022. Accessed: Feb. 01, 2024. [Online]. Available: https://rosap.ntl.bts.gov/view/dot/67308
S. Coogan and M. Thitsa, “Coordinated Anti-congestion Control Algorithm for Diverging Diamond Interchanges,” FHWA-GA-21-1913, Apr. 2021.
“netconvert - SUMO Documentation.” Accessed: Jul. 27, 2021. [Online]. Available: https://sumo.dlr.de/docs/netconvert.html
“Vissim - SUMO Documentation.” Accessed: Jul. 27, 2021. [Online]. Available: https://sumo.dlr.de/docs/Networks/Import/Vissim.html
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,” Engineering Journal, vol. 21, no. 6, pp. 57–67, Oct. 2017, doi: https://doi.org/10.4186/ej.2017.21.6.57.
S. K. Singh, P. Komolkiti, and C. Aswakul, “Impact Analysis of Start-Up Lost Time at Major Intersections on Sathorn Road Using a Synchro Optimization and a Microscopic SUMO Traffic Simulation,” IEEE Access, vol. 6, pp. 6327–6340, 2018, doi: https://doi.org/10.1109/ACCESS.2017.2739240.
Trafficware, LLC., “Synchro Studio 10 User Guide.” Oct. 26, 2017. [Online]. Available: https://www.trafficware.com/synchro-studio.html
“SUMO Road Networks - SUMO Documentation.” Accessed: Jan. 23, 2022. [Online]. Available: https://sumo.dlr.de/docs/Networks/SUMO_Road_Networks.html
“Documentation - SUMO Documentation.” Accessed: Jul. 28, 2021. [Online]. Available: https://sumo.dlr.de/docs/index.html
“Traffic Lights - SUMO Documentation.” Accessed: Jan. 23, 2022. [Online]. Available: https://sumo.dlr.de/docs/Simulation/Traffic_Lights.html
M. Schrader, Q. Wang, and J. Bittle, “Extension and Validation of NEMA-Style Dual-Ring Controller in SUMO,” SUMO Conference Proceedings, vol. 3, pp. 1–13, Sep. 2022, doi: https://doi.org/10.52825/scp.v3i.115.
M. Halbach and J. Erdmann, “High fidelity modelling of traffic light control with XML logic representation,” SUMO Conference Proceedings, vol. 3, pp. 45–68, Sep. 2022, doi: https://doi.org/10.52825/scp.v3i.114.
A. Bundy and L. Wallen, “Breadth-First Search,” in Catalogue of Artificial Intelligence Tools, A. Bundy and L. Wallen, Eds., in Symbolic Computation. , Berlin, Heidelberg: Springer, 1984, pp. 13–13. doi: https://doi.org/10.1007/978-3-642-96868-6_25.
“netedit - SUMO Documentation.” Accessed: Feb. 14, 2024. [Online]. Available: https://sumo.dlr.de/docs/Netedit/index.html
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Copyright (c) 2024 Yiran Zhang, Mingjian Fu, Xuegang (Jeff) Ban
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Accepted 2024-04-03
Published 2024-07-17
Funding data
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National Science Foundation
Grant numbers CNS-2034615