2020
DOI: 10.1016/j.trc.2020.102650
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Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation

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Cited by 11 publications
(11 citation statements)
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“…To keep consistency, the calibration parameters are denoted as θ ¼ ðθ h h 2 HÞ, with which the superscripts 0, L, and U refer to a priori value, the lower bounds, and the upper bounds, respectively. The demand calibration problem can be formulated as the following optimization problem (Osorio, 2019;Chen et al, 2020).…”
Section: Br-ata Od Demand Calibration Problemmentioning
confidence: 99%
See 2 more Smart Citations
“…To keep consistency, the calibration parameters are denoted as θ ¼ ðθ h h 2 HÞ, with which the superscripts 0, L, and U refer to a priori value, the lower bounds, and the upper bounds, respectively. The demand calibration problem can be formulated as the following optimization problem (Osorio, 2019;Chen et al, 2020).…”
Section: Br-ata Od Demand Calibration Problemmentioning
confidence: 99%
“…One common technique for solving calibration problems is minimizing the distance, such as sum-of-squares distance, Manhattan distance, and Euclidean distance (Chen et al, 2020;Yang et al, 2001), between the estimated values and observed values. The least squares method (LSM) and its variants (Chiu et al, 2010;Shafiei et al, 2018;Chen et al, 2020) are widely used. The implicit network loading or ATP choice makes the constraints in the calibration problems nonlinear and the ATA models intractable.…”
Section: Spsa Algorithm For Calibrating Br-atamentioning
confidence: 99%
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“…Calibration and validation are key aspects that must be addressed for these models to be reliable and widely applied, especially for the activity-based models. However, few studies have investigated the development and application of robust techniques for calibrating the demand-side parameters of activity-based models (Chen et al 2020).…”
Section: Activity-based Modelingmentioning
confidence: 99%
“…In calibrating the ABM for the prototype cities, we adjust the following quantities to match the mode and activity shares for their respective typologies: alternative-specific constants, specific variable coefficients and scale parameters (which control the correlations in the nested logit structure). The calibration approach is formally described in Chen et al (2019). The activity validation data are obtained from available travel surveys for cities in the respective typologies (see Table 5).…”
Section: Demandmentioning
confidence: 99%