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首页> 外文期刊>Transportation Research Part B: Methodological >Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem
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Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem

机译：具有内生到达时间约束的逆向优化以校准家庭活动模式问题

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摘要

A parameter estimation method is proposed for calibrating the household activity pattern problem so that it can be used as a disaggregate, activity-based analog of the traffic assign ment problem for activity-based travel forecasting. Inverse optimization is proposed for estimating parameters of the household activity pattern problem such that the observed behavior is optimal, the patterns can be replicated, and the distribution of the parameters is consistent. In order to fit the model to both the sequencing of activities and the arrival times to those activities, an inverse problem is formulated as a mixed integer linear pro gramming problem such that coefficients of the objectives are jointly estimated along with the goal arrival times to the activities. The formulation is designed to be structurally similar to the equivalent problems defined by Ahuja and Orlin and can be solved exactly with a cutting plane algorithm. The concept of a unique invariant common prior is used to regularize the estimation method, and proven to converge using the Method of Succes sive Averages. The inverse model is tested on sample households from the 2001 California Household Travel Survey and results indicate a significant improvement over the standard inverse problem in the literature as well as baseline prescriptive models that do not make use of sample data for calibration. Although, not unexpectedly, the estimated optimization model by itself is a relatively poor forecasting model, it may be used in determining responses of a population to spatio-temporal scenarios where revealed preference data is absent.
机译：提出了一种参数估计方法，用于校正家庭活动模式问题，以便可以将其用作基于活动的旅行预测的交通分配问题的分解，基于活动的模拟。为了估计家庭活动模式问题的参数，提出了逆向优化，以使观察到的行为最佳，可以复制这些模式，并且参数的分布是一致的。为了使模型适合活动的排序和活动的到达时间，将反问题公式化为混合整数线性规划问题，以便共同估算目标的系数以及到达目标的时间。活动。该公式的设计与Ahuja和Orlin定义的等效问题在结构上相似，并且可以使用切割平面算法精确求解。唯一不变的公共先验的概念用于规范化估计方法，并使用成功平均方法证明收敛。对反向模型进行了2001年加利福尼亚家庭旅行调查的样本家庭的测试，结果表明，与文献中的标准反向问题以及不使用样本数据进行校准的基准规范模型相比，该模型有了显着改进。尽管并非意外地，估计的优化模型本身是一个相对较差的预测模型，但可以将其用于确定总体对缺少显示的偏好数据的时空场景的响应。

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