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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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    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.
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    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.
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    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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