Choice set generation is a challenging aspect of disaggregate level residential location choice modelling due to the large number of candidate alternatives in the universal choice set (hundreds to hundreds of thousands). The classical Manski method (Manski, 1977) is infeasible here because of the explosion of the number of possible choice sets with the increase in the number of alternatives. Several alternative approaches have been proposed in recent years to deal with this issue, but these have limitations alongside strengths. For example, the Constrained Multinomial Logit (CMNL) model (Martinez et al., 2009) offers gains in efficiency and improvements in model fit but has weaknesses in terms of replicating the Manski model parameters. The rth-order Constrained Multinomial Logit (rCMNL) model (Paleti, 2015) performs better than the CMNL model in producing results consistent with the Manski model, but the benefits disappear when the number of alternatives in the universal choice set increases. In this study, we propose an improved CMNL model (referred to as Improved Constrained Multinomial Logit Model, ICMNL) with a higher order formulation of the CMNL penalty term that does not depend on the number of alternatives in the choice set. Therefore, it is expected to result in better model fit compared to the CMNL and the rCMNL model in cases with large universal choice sets. The performance of the ICMNL model against the CMNL and the rCMNL model is evaluated in an empirical study of residential location choices of households living in the Greater London Area. Zone level models are estimated for residential ownership and renting decisions where the number of alternatives in the universal choice set is 498 in each case. The performance of the models is examined both on the estimation sample and the holdout sample used for validation. The results of both ownership and renting models indicate that the ICMNL model performs considerably better compared to the CMNL and the rCMNL model for both the estimation and validation samples. The ICMNL model can thus help transport and urban planners in developing better prediction tools.

中文翻译：

---

使用替代方案的隐式可用性为住宅位置选择建模

由于通用选择集中有大量候选选择（数百至数十万），因此选择集生成是分解级别的住宅区位选择建模的一个具有挑战性的方面。经典的Manski方法（Manski，1977）在这里是不可行的，因为随着选择数的增加，可能的选择集的数量激增。近年来，已经提出了几种替代方法来解决此问题，但是这些方法除了优点之外还具有局限性。例如，约束多项式Lo​​git（CMNL）模型（Martinez等，2009）在效率上有所提高，模型拟合得到了改善，但在复制Manski模型参数方面存在弱点。r阶约束多项式Lo​​git（rCMNL）模型（Paleti，（2015年）在产生与Manski模型一致的结果方面比CMNL模型更好，但是当通用选择集中的替代方法数目增加时，好处就消失了。在这项研究中，我们提出了一种改进的CMNL模型（称为改进的约束多项式Lo​​git模型，ICMNL），其中的CMNL惩罚项具有更高阶的表述，而不依赖于选择集中的替代项数。因此，在具有较大通用选择集的情况下，与CMNL和rCMNL模型相比，有望产生更好的模型拟合。ICMNL模型相对于CMNL和rCMNL模型的性能是通过对大伦敦地区居民的住所位置选择进行的实证研究进行评估的。估计区域级别的模型用于住宅所有权和租赁决策，其中在每种情况下，通用选择集中的替代项数量为498。在评估样本和用于验证的保留样本上都检查了模型的性能。所有权模型和租用模型的结果都表明，与CMNL和rCMNL模型相比，ICMNL模型在估计和验证样本方面的性能要好得多。因此，ICNML模型可以帮助运输和城市规划人员开发更好的预测工具。所有权模型和租用模型的结果都表明，与CMNL和rCMNL模型相比，ICMNL模型在估计和验证样本方面的性能要好得多。因此，ICNML模型可以帮助运输和城市规划人员开发更好的预测工具。所有权模型和租用模型的结果都表明，相比于CMNL和rCMNL模型，ICMNL模型在估计和验证样本方面的性能要好得多。因此，ICNML模型可以帮助运输和城市规划人员开发更好的预测工具。