This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.

中文翻译：

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将空间相互作用纳入零膨胀负二项式模型以生成货运行程

本文为货运旅行产品和景点制定了一个空间自回归零膨胀负二项式模型。该模型捕获以下货运旅行特征：计数数据类型、正旅行率、过度分散、零通货膨胀和空间自相关。空间自回归结构应用于模型的负二项式部分，以获得对不同回归量影响的无偏估计。此外，我们使用全信息最大似然估计器来估计参数。我们通过在钦奈进行的基于企业的货运调查进行实证分析。除了聚合模型之外，还为由机动两轮车和三轮车以及皮卡产生的行程估计了单独的模型。道路密度和地理位置指标等空间变量在所有模型中都不重要。相比之下，除了由皮卡吸引和产生的货运行程外，所有模型的空间自相关都很显着。从政策的角度来看，弹性结果表明考虑空间自相关的重要性。我们还强调了基于弹性的车辆类别聚合导致的偏差。