# Spatial Performance Indicators for Traffic Flow Prediction  
[PERSON]  
1 Department of Industrial Systems Engineering and Product Design, Ghent University, 9000 Ghent, Belgium; [EMAIL_ADDRESS]
2  
[PERSON]  
1 Department of Industrial Systems Engineering and Product Design, Ghent University, 9000 Ghent, Belgium; [EMAIL_ADDRESS]  
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In the last decade, spatiotemporal prediction methods that take advantage of both temporal and spatial data have become popular because the addition of spatial contexts can improve the prediction accuracy [5]. Examples are Attention Based Spatial-Temporal Graph Convolutional Networks (ASTGCN) [6] that utilize recent, daily periodic, and weekly periodic data as temporal contexts and an undirected graph of the traffic network as spatial contexts using the attention mechanism, and Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) [7] that synchronously capture both spatial and temporal correlations directly. Different prediction methods have different spatiotemporal characteristics based on the spatial and temporal information. For example, the addition of hourly, daily, and weekly periodic data to complement recent data introduces temporal periodicity and improves the prediction performance during peak hours [8]. However, introducing a graph network that represents the spatial structure of the traffic network allows the traffic prediction method to utilize spatial information, resulting in better performance [9]. These characteristics affect the traffic prediction results, such as the performance of the traffic prediction and spatial distribution of the prediction at each location. Unfortunately, the literature usually relies only on standard performance KPIs, such as RMSE, MAE, and MAPE, to compare prediction techniques [10]. These KPIs only provide an average point-wise error of the prediction accuracy from the entire traffic network, without information on the underlying distribution related to the spatiotemporal characteristics of the method. Although this approach simplifies the evaluation process, the requirements of real-world traffic management systems are more complex than the average prediction errors, leading to a discrepancy between the research findings and their practical applicability. For example, a traffic flow prediction method with bad prediction node clusters will have a more negative impact than other methods with uniformly distributed prediction errors in traffic signal control applications. This is because bad prediction node clusters degrade the control performance of the area, and this degradation can propagate further to neighboring areas. The lack of a variety of prediction models' KPIs has been acknowledged, and researchers are attempting to solve this problem in various domains, such as databases [11], classification problems [12], sparse spatiotemporal point processes [13], and uncertainty in spatial forecasting [14]. However, such KPIs are usually limited to particular applications or require modifications to the prediction method, and most of them cannot be applied to traffic flow prediction problems.