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Calibration of microscopic traffic simulation of urban road network including mini-roundabouts and unsignalized intersection using open-source simulation tool

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.
Rocznik
Tom
Strony
305--318
Opis fizyczny
Bibliogr. 31 poz.
Twórcy
  • Faculty of Engineering and Natural Sciences, Maltepe University, Istanbul, Turkey
autor
  • Faculty of Civil Engineering, Yıldız Technical University, Istanbul, Turkey
Bibliografia
  • 1. Jayasinghe Thenuwan, Thillaiampalam Sivakumar, Amal S. Kumarage. 2021. ,,Calibration of SUMO microscopic simulator for Sri Lankan traffic conditions”. In: Proceedings of the Eastern Asia Society for Transportation Studies”:12-15. Tokyo, Japan.
  • 2. Sashank Yadavilli, Nitin A. Navali, Arjuna Bhanuprakash, B. Anil Kumar, Lelitha Vanajakshi. 2020. ,,Calibration of SUMO for Indian Heterogeneous Traffic Conditions”. In: Recent Advances in Traffic Engineering”: 199-214. ISBN: 978-981-15-3742-4.
  • 3. Yu Miao, Wei (David) Fan. 2017. ,,Calibration of microscopic traffic simulation models using metaheuristic algorithms”. International Journal of Transportation Science and Technology 6 (1): 63-77. ISSN: 2046-0430. DOI: https://doi.org/10.1016/j.ijtst.2017.05.001.
  • 4. Lidbe Abhay, Alexander Hainen, Steven Jones. 2017. ,,Comparative study of simulated annealing, tabu search, and the genetic algorithm for calibration of the microsimulation model”. Simulation 93(1): 21-33. DOI: https://doi.org/10.1177/0037549716683028.
  • 5. Yatmar Hajriyanti, Muhammad Isran Ramli, Dantje Runtulalo, Muhammad Rahmat Muslim. 2022. ,,Optimizing signal control on signalized intersection using micro-traffic simulation approach: Case study Haji Bau-Cendrawasih-arif rate intersection in Makassar city”. AIP Conference Proceedings 2543(1). ISSN: 1551-7616. DOI: https://doi.org/10.1063/5.0094918.
  • 6. Kulakarni Rakesh, Akhilesh Chepuri, Shriniwas Arkatkar, Gaurang J. Joshi. 2020. ,,Estimation of saturation flow at signalized intersections under heterogeneous traffic conditions”. In: Transportation Research: Proceedings of CTRG 2017”: 591-605. Springer, Singapore. ISBN: 978-981-32-9042-6.
  • 7. Maheshwary Palak, Kinjal Bhattacharyya, Bhargab Maitra, Manfred Boltze. 2020. ,,A methodology for calibration of traffic micro-simulator for urban heterogeneous traffic operations”. Journal of traffic and transportation engineering (English Edition) 7(4): 507-519. ISSN: 2095-7564. DOI: https://doi.org/10.1016/j.jtte.2018.06.007.
  • 8. Orazio Giuffrè, Granà Anna, Tumminello Maria Luisa, Sferlazza Antonino. 2018. ,,Calibrating a microscopic traffic simulation model for roundabouts using genetic algorithms”. Journal of Intelligent & Fuzzy Systems 35(2): 1791-1806. DOI: 10.3233/JIFS-169714.
  • 9. Mathew Tom V., Padmakumar Radhakrishnan. 2010. ,,Calibration of microsimulation models for nonlane-based heterogeneous traffic at signalized intersections”. Journal of Urban Planning and Development 136(1):59-66. DOI: https://doi.org/10.1061/(ASCE)0733-9488(2010)136:1(59).
  • 10. Fang Xuan, Tamás Tettamanti, Arthur Couto Piazzi. 2020. ,,Online calibration of microscopic road traffic simulator”. In: 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)”: 275-280. IEEE. 23-25 January 2020. Herlany, Slovakia. ISBN: 978-1-7281-3149-8.
  • 11. Arathi A.R, M. Harikrishna, Mithun Mohan. 2023. ,,Simulation-based performance evaluation of skewed uncontrolled intersections”. International Journal of Intelligent Transportation Systems Research 21: 1-12.
  • DOI: https://doi.org/10.1007/s13177-023-00360-6.
  • 12. Cobos Carlos, Cristian Erazo, Julio Luna, Martha Mendoza, Carlos Gaviria, Cristian Arteaga, Alexander Paz. 2016. ,,Multi-objective memetic algorithm based on NSGA-II and simulated annealing for calibrating CORSIM micro-simulation models of vehicular traffic flow”. In: Advances in Artificial Intelligence: 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016”: 468-476. Springer,Cham.
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  • 13. Sun Jian, Zhizhou Wu, Xiaoguang Yang. 2005. ,,Calibration of VISSIM for Shanghai Expressway weaving sections using simulated annealing algorithm”. In: Computing in Civil Engineering (2005): 1-8.
  • 14. Gamboa-Venegas Carlos, Steffan Gómez-Campos, Esteban Meneses. 2021. ,,Calibration of traffic simulations using simulated annealing and GPS navigation records”. In: Annual International Conference on Information Management and Big Data: 17-33. Springer,Cham. 1-13 December 2021. ISBN: 978-3-031-04447-2.
  • 15. Paz Alexander, Victor Molano, Carlos Gaviria. 2012. ,,Calibration of CORSIM models considering all model parameters simultaneously”. In: 15th International IEEE Conference on Intelligent Transportation Systems: 1417-1422. IEEE. 16-19 September 2012. Anchorage, AK, USA. ISBN 978-1-4673-3063-3.
  • 16. Lee Jung-Beom, Kaan Ozbay. 2009. ,,New calibration methodology for microscopic traffic simulation using enhanced simultaneous perturbation stochastic approximation approach”. Transportation Research Record 2124(1): 233-240. DOI: https://doi.org/10.3141/2124-23.
  • 17. Sha Di, Jingqin Gao, Di Yang, Fan Zuo, Kaan Ozbay. 2023. ,,Calibrating stochastic traffic simulation models for safety and operational measures based on vehicle conflict distributions obtained from aerial and traffic camera videos”. Accident Analysis & Prevention 179. DOI: https://doi.org/10.1016/j.aap.2022.106878.
  • 18. Karimi Mohammad, Ciprian Alecsandru. 2019. ,,Two‐fold calibration approach for microscopic traffic simulation models”. IET Intelligent Transport Systems 13(10): 1507-1517. DOI: https://doi.org/10.1049/iet-its.2018.5369.
  • 19. Paul M., V. Charan, V. Soni, I. Ghosh. 2017. ,,Calibration methodology of microsimulation model for unsignalized intersection under heterogeneous traffic conditions”. In: ASCE India Conference 2017: 618-627. American Society of Civil Engineers. 12-14 December 2017. New Delhi, India. ISBN: 9780784482025.
  • 20. Shahrokhi Shahraki Hamed, Ciprian Alecsandru, Reza Maghsoudi, Luis Amador. 2018. ,,An efficient soft computing-based calibration method for microscopic simulation models”. Journal of Transportation Safety & Security 10(4): 367-386. DOI: https://doi.org/10.1080/19439962.2017.1292337.
  • 21. Dutta M, M.A. Ahmed. 2019. ,,Calibration of VISSIM models at three-legged unsignalized intersections under mixed traffic conditions”. Advances in transportation studies 48(2019): 31-46. DOI: 10.4399/9788255254723.
  • 22. Bari Chintaman, Ajay Gangwal, Ziauddin Rahimi, L. Srikanth, Bijendra Singh, Ashish Dhamaniya. 2023. ,,Emission modeling at toll plaza under mixed traffic condition using simulation”. Environmental Monitoring and Assessment 195: 803. DOI: https://doi.org/10.1007/s10661-023-11409-0.
  • 23. Šurdonja Sanja, Sergije Babić, Aleksandra Deluka–Tibljaš, Marijana Cuculić. 2012. ,,Mini-roundabouts in urban areas”. In 2nd Conference on Road and Rail Infrastructure: 997-1003. 7-9 May 2012. Dubrovnik, Crotia. ISBN: 978-953-6272-50-1.
  • 24. The Federal Highway Administration (FHWA). 2010. Mini-Roundabouts. U.S. Department of Transportation.
  • 25. Pratelli Antonio, Marino Lupi, Chiara Pratelli, Alessandro Farina. 2019. Mini-roundabouts for improving urban accessibility. In: Modelling of the Interaction of the Different Vehicles and Various Transport Modes: 333-382. Edited by Aleksander Sładkowski. Switzerland: Springer, Cham. ISBN: 978-3-030-11512-8.
  • 26. Lopez Pablo Alvarez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, Evamarie Wiessner. 2018. ,,Microscopic traffic simulation using sumo”. In: 2018 21st international conference on intelligent transportation systems (ITSC): 2575-2582. IEEE. 04-07 November 2018. Maui, HI, USA. ISBN: 978-1-7281-0323-5.
  • 27. Kim Minjung, Max Schrader, Hwan-Sik Yoon, Joshua Bittle. 2023. ,,Optimal traffic signal control using priority metric based on real-time measured traffic information”. Sustainability 15(9): 7637. DOI: https://doi.org/10.3390/su15097637.
  • 28. Patil Mayur, Punit Tulpule, Shawn Midlam-Mohler. 2023. ,,An approach to model a traffic environment by addressing sparsity in vehicle count data”. SAE Technical Paper. DOI: https://doi.org/10.4271/2023-01-0854.
  • 29. Song Jie, Yi Wu, Zhe-Xin Xu, Xiao Lin. 2014. ,,Research on car-following model based on SUMO”. In: The 7th IEEE/International Conference on Advanced Infocomm Technology: 47-55. IEEE. 14-16 November 2014. Fuzhou, China. ISBN: 978-1-4799-5455-1.
  • 30. Ge Qiao, Monica Menendez. 2014. ,,An efficient sensitivity analysis approach for computationally expensive microscopic traffic simulation models”. International Journal of Transportation 2(2): 49-64. DOI: http://dx.doi.org/10.14257/ijt.2014.2.2.04.
  • 31. Wunderlich Karl, Meenakshy Vasudevan, Peiwei Wang. 2019. TAT Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software 2019 Update to the 2004 Version. Washington: U.S. Department of Transportation Federal Highway Administration Office of Operations.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cfaed9b8-ce9b-4e58-814c-065a0af1c026
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