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Development and calibration of the Anisotropic Mesoscopic Simulation model for uninterrupted flow facilities

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  • Chiu, Yi-Chang
  • Zhou, Liang
  • Song, Houbing

Abstract

This paper presents the development, analysis, and calibration of the Anisotropic Mesoscopic Simulation (AMS) model for uninterrupted flow facilities, such as freeways. The proposed AMS model is a vehicle-based mesoscopic traffic simulation approach that explicitly considers the anisotropic property of traffic flow into the vehicle state update at each simulation step. The advantage of AMS is its ability to address a variety of uninterrupted flow conditions in a relatively simple, unified and computationally efficient manner. The discussions focus on the key modeling concepts, the analytical properties and numerical analysis, and the calibration process and results. The addressed analytical properties are the overtaking conditions, acceleration and deceleration rate bounds, and shockwaves. The numerical analysis includes both freeway segments as well as merging junctions. Considerable efforts were devoted to employ the Next-Generation Simulation (NGSIM) program datasets to calibrate the AMS model parameters. The high traffic fidelity and satisfactory computational efficiency make AMS a promising simulation approach for large-scale regional dynamic traffic simulation and assignment.

Suggested Citation

  • Chiu, Yi-Chang & Zhou, Liang & Song, Houbing, 2010. "Development and calibration of the Anisotropic Mesoscopic Simulation model for uninterrupted flow facilities," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 152-174, January.
    • Handle: RePEc:eee:transb:v:44:y:2010:i:1:p:152-174
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    1. Daganzo, Carlos F., 1995. "The cell transmission model, part II: Network traffic," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 79-93, April.
    2. C. Gawron, 1998. "An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 393-407.
    3. David Branston, 1976. "Models of Single Lane Time Headway Distributions," Transportation Science, INFORMS, vol. 10(2), pages 125-148, May.
    4. Daganzo, Carlos F., 1995. "Requiem for second-order fluid approximations of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 277-286, August.
    5. Daganzo, Carlos F., 1994. "The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 269-287, August.
    6. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part I: General theory," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 281-287, August.
    7. D. J. Buckley, 1968. "A Semi-Poisson Model of Traffic Flow," Transportation Science, INFORMS, vol. 2(2), pages 107-133, May.
    8. Nelson, Paul & Sopasakis, Alexandros, 1998. "The prigogine-herman kinetic model predicts widely scattered traffic flow data at high concentrations," Transportation Research Part B: Methodological, Elsevier, vol. 32(8), pages 589-604, November.
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    Cited by:

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    2. Sheu, Jiuh-Biing & Wu, Hsi-Jen, 2015. "Driver perception uncertainty in perceived relative speed and reaction time in car following – A quantum optical flow perspective," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 257-274.
    3. Qu, Xiaobo & Zhang, Jin & Wang, Shuaian, 2017. "On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 256-271.
    4. S H Melouk & B B Keskin & C Armbrester & M Anderson, 2011. "A simulation optimization-based decision support tool for mitigating traffic congestion," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1971-1982, November.
    5. Yong Lin, 2023. "Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    6. Xiqun (Michael) Chen & Xiang He & Chenfeng Xiong & Zheng Zhu & Lei Zhang, 2019. "A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management," Transportation Science, INFORMS, vol. 53(2), pages 545-565, March.

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