Most transport models rely on a discrete description of space, and are, therefore, subject to spatial aggregation bias. Spatial aggregation induces the use of centroid connectors and the omission of intrazonal trips in traffic assignment. This practice is shown to bias main traffic assignment outcomes, especially in spatially coarse models. To address these modeling errors, the literature suggests some solutions but no clear-cut conclusion on the contribution of these solutions is available. In the current research, we undergo a detailed investigation of the contribution of some of these modeling solutions in order to provide useful and practical recommendations to academics and policy makers. Different assignment strategies that are deemed to mitigate the impacts of spatial aggregation in traffic assignment are explored in different case studies. Findings from this research outline that demand-side assignment strategies outperform supply-side methods in addressing the spatial aggregation problem. The results also suggest that the inclusion of intrazonal demand in traffic assignment is not sufficient to overcome aggregation biases. The definition of connectors is also of importance.

大多数传输模型依赖于空间的离散描述，因此会受到空间聚集偏差的影响。空间聚集导致在交通分配中使用质心连接器并省略区域内旅行。事实证明，这种做法会偏向主要交通分配结果，尤其是在空间粗糙的模型中。为了解决这些建模错误，文献提出了一些解决方案，但没有关于这些解决方案的明确结论。在当前的研究中，我们对其中一些建模解决方案的贡献进行了详细的调查，以便为学术界和决策者提供有用和实用的建议。在不同的案例研究中，探索了不同的分配策略，这些策略被认为可减轻空间聚合在交通分配中的影响。从这项研究中发现，在解决空间聚集问题时，需求方分配策略优于供应方方法。结果还表明，将区域内需求包括在交通分配中不足以克服聚集偏差。连接器的定义也很重要。