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Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation

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  • Gunnar Flötteröd
  • Yu Chen
  • Kai Nagel

Abstract

This article reports on the calibration and analysis of a fully disaggregate (agent-based) transport simulation for the metropolitan area of Zurich. The agent-based simulation goes beyond traditional transport models in that it equilibrates not only route choice but all-day travel behavior, including departure time choice and mode choice. Previous work has shown that the application of a novel calibration technique that adjusts all choice dimensions at once from traffic counts yields cross-validation results that are competitive with any state-of-the-art four-step model. While the previous study aims at a methodological illustration of the calibration method, this work focuses on the real-world scenario, and it elaborates on the usefulness of the obtained results for further demand analysis purposes. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Gunnar Flötteröd & Yu Chen & Kai Nagel, 2012. "Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation," Networks and Spatial Economics, Springer, vol. 12(4), pages 481-502, December.
    • Handle: RePEc:kap:netspa:v:12:y:2012:i:4:p:481-502
    DOI: 10.1007/s11067-011-9164-9
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    References listed on IDEAS

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    1. Bal Fondake, 2008. "Eureka," Working Papers id:1700, eSocialSciences.
    2. David Charypar & Kai Nagel, 2005. "Generating complete all-day activity plans with genetic algorithms," Transportation, Springer, vol. 32(4), pages 369-397, July.
    3. Aura Reggiani & Peter Nijkamp (ed.), 2009. "Complexity and Spatial Networks," Advances in Spatial Science, Springer, number 978-3-642-01554-0, december.
    4. Dominik Grether & Yu Chen & Marcel Rieser & Kai Nagel, 2009. "Effects of a Simple Mode Choice Model in a Large-Scale Agent-Based Transport Simulation," Advances in Spatial Science, in: Aura Reggiani & Peter Nijkamp (ed.), Complexity and Spatial Networks, chapter 0, pages 167-186, Springer.
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    1. Tie-Qiao Tang & Yun-Peng Wang & Xiao-Bao Yang & Hai-Jun Huang, 2014. "A Multilane Traffic Flow Model Accounting for Lane Width, Lane-Changing and the Number of Lanes," Networks and Spatial Economics, Springer, vol. 14(3), pages 465-483, December.
    2. Kim, Jinhee & Rasouli, Soora & Timmermans, Harry, 2014. "Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 71-85.
    3. Irena Ištoka Otković & Barbara Karleuša & Aleksandra Deluka-Tibljaš & Sanja Šurdonja & Mario Marušić, 2021. "Combining Traffic Microsimulation Modeling and Multi-Criteria Analysis for Sustainable Spatial-Traffic Planning," Land, MDPI, vol. 10(7), pages 1-26, June.
    4. Ben-Dor, Golan & Ogulenko, Aleksey & Klein, Ido & Ben-Elia, Eran & Benenson, Itzhak, 2024. "Simulation-based policy evaluation of monetary car driving disincentives in Jerusalem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    5. Cantelmo, Guido & Qurashi, Moeid & Prakash, A. Arun & Antoniou, Constantinos & Viti, Francesco, 2020. "Incorporating trip chaining within online demand estimation," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 171-187.
    6. Meead Saberi & Taha H. Rashidi & Milad Ghasri & Kenneth Ewe, 2018. "A Complex Network Methodology for Travel Demand Model Evaluation and Validation," Networks and Spatial Economics, Springer, vol. 18(4), pages 1051-1073, December.
    7. Jiri Horak & Jan Tesla & David Fojtik & Vit Vozenilek, 2019. "Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    8. Chengxiang Zhuge & Mike Bithell & Chunfu Shao & Xia Li & Jian Gao, 2021. "An improvement in MATSim computing time for large-scale travel behaviour microsimulation," Transportation, Springer, vol. 48(1), pages 193-214, February.
    9. Seyitali İlyas & Bahadır Ersoy Ulusoy & Sevil Köfteci & Yalçın Albayrak, 2024. "Association of Vehicle Count Data Obtained Via Image Processing Techniques Compared with Microsimulation Program Analysis Results," Networks and Spatial Economics, Springer, vol. 24(3), pages 655-680, September.
    10. Cantelmo, Guido & Viti, Francesco & Cipriani, Ernesto & Nigro, Marialisa, 2018. "A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 303-320.
    11. Dali Wei & Changwei Yuan & Hongchao Liu & Dayong Wu & Wesley Kumfer, 2017. "The Impact of Service Refusal to the Supply–Demand Equilibrium in the Taxicab Market," Networks and Spatial Economics, Springer, vol. 17(1), pages 225-253, March.
    12. Katrien Ramaekers & Sofie Reumers & Geert Wets & Mario Cools, 2013. "Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data," Networks and Spatial Economics, Springer, vol. 13(3), pages 351-372, September.
    13. Chengxiang Zhuge & Chunfu Shao, 2018. "Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles," Networks and Spatial Economics, Springer, vol. 18(4), pages 875-908, December.
    14. Satish V. Ukkusuri & Samiul Hasan & Binh Luong & Kien Doan & Xianyuan Zhan & Pamela Murray-Tuite & Weihao Yin, 2017. "A-RESCUE: An Agent based Regional Evacuation Simulator Coupled with User Enriched Behavior," Networks and Spatial Economics, Springer, vol. 17(1), pages 197-223, March.

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