Authors:
Muhammad Farhan Fathurrahman
1
;
2
and
Sidharta Gautama
1
;
2
Affiliations:
1
Department of Industrial Systems Engineering and Product Design, Ghent University, Ghent, Belgium
;
2
FlandersMake@UGent-Corelab ISyE, Lommel, Belgium
Keyword(s):
Traffic Prediction, Spatiotemporal Prediction, Spatial Performance Indicators, Global Moran’s I, Geary’s C, Getis-Ord General G.
Abstract:
Traffic prediction is vital for traffic management systems and helps enhance traffic management efficiency over a traffic network. Recently, spatiotemporal prediction models have been proposed that extend single traffic node temporal prediction. They employ the spatial context of the combined nodes in the urban network to improve prediction. However, the key performance indicators (KPI) of these methods are still limited to accuracy averaged over the full traffic network. They do not yet describe local spatiotemporal behaviour that can affect the traffic prediction accuracy in the traffic network. In this paper, we explore three spatial KPIs: Global Moran’s I, Geary’s C, and Getis-Ord General G to evaluate traffic flow prediction for freeway traffic networks. The study is conducted by evaluating traffic flow prediction results in the PeMSD8 dataset using spatiotemporal prediction and calculating different KPIs. Several synthetic scenarios based on the prediction results are created t
o showcase what the standard KPI cannot distinguish. The Global Moran’s I and Geary’s C can identify different levels of spatial autocorrelation and the Getis-Ord General G can distinguish spatial clustering in prediction results. The findings aim to improve the evaluation of different traffic prediction methods towards a better traffic management system.
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