# Spatial Performance Indicators for Traffic Flow Prediction
## 5 Discussion
This translates into an APAM distribution with zero skewness and negative kurtosis values. All the traffic prediction methods in this study showed positive skewness and kurtosis values with subtle differences, making this KPI less informative for our case study. One interesting finding is that ASTGCN has comparably higher skewness and kurtosis than STSGCN, suggesting that more nodes have higher APAM values than the rest of the distribution. All spatial KPIs showed comparable differences between the different clustering scenarios. Unfortunately, the comparison between different traffic prediction methods shows subtle differences, except for Getis-Ord General G, which makes it less informative. This is similar to the problem of standard accuracy KPIs, where the difference between the state-of-the-art methods is negligible. The subtle difference is because the difference between traffic prediction methods is not as extreme as the clustering scenarios demonstrate. However, ASTGCN showed a clear difference in Getis-Ord General G and noticeable differences in Global Moran's I and the univariate analysis of APAM. These results demonstrate the potential of spatial KPIs to differentiate methods that are less indistinguishable from standard accuracy KPIs. Choosing a traffic prediction method that is evaluated by both the standard prediction accuracy and spatial KPIs will help the operation of traffic management systems. Spatial KPIs provide more confidence in traffic prediction methods. By considering both the standard prediction accuracy and spatial KPIs, stakeholders have more tools to compare different traffic prediction methods, ultimately leading to more reliable predictions and reduced operational risks. Using spatial KPIs, we can infer whether the distribution of traffic flow prediction error and node importance is suitable for applications such as traffic signal control or route guidance. For example, in traffic signal control, some control algorithms such as model predictive control [33] require some knowledge of the future states, which we infer based on traffic prediction models. Traffic prediction models are assumed to be sufficiently accurate to represent traffic network dynamics and fail to do so, resulting in the degradation of network performance. A slightly higher RMSE traffic flow prediction method with a uniform error distribution and fewer hotspots is preferable for traffic signal control compared to other traffic flow prediction methods with a lower RMSE but more hotspots. Hotspots can degrade traffic signal control performance, resulting in traffic jams that propagate to neighboring locations. This information can be deduced from Global Moran's I and Getis-Ord General G. Similarly, the distribution of APAM values can infer the existence and distribution of influential nodes. This information is crucial for anticipating the propagation of errors because these influential nodes have high node centrality. In route guidance, the existence of hotspots can reduce the confidence level of a route passing through that area. Choosing a traffic prediction method with fewer hotspots can improve the confidence level of route guidance systems. Ultimately, route guidance systems can make informed decisions, potentially leading to more reliable and efficient route recommendations for users. Compared to standard KPIs, spatial KPIs are not straightforward to evaluate and different traffic management applications may prioritize different aspects of spatial performance. We could draw some conclusions from the value of spatial KPIs between different methods,but more caution is needed because factors such as different types of clusters (e.g., hotspots, coldspots, star clusters, and line clusters) could affect the evaluation measures, especially the Getis-Ord General G. The difference in spatial KPIs' values between the methods can also be negligible, leading to a problem similar to that of the standard accuracy KPIs.