Spatial Performance Indicators to Evaluate Spatiotemporal Traffic Prediction

Muhammad Farhan Fathurrahman, Muhammad Farhan Fathurrahman, Sidharta Gautama, Sidharta Gautama

2024

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 to 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.

Paper Citation

in Harvard Style

Fathurrahman M. and Gautama S. (2024). Spatial Performance Indicators to Evaluate Spatiotemporal Traffic Prediction. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 156-164. DOI: 10.5220/0012699200003702

in Bibtex Style

@conference{vehits24,
author={Muhammad Farhan Fathurrahman and Sidharta Gautama},
title={Spatial Performance Indicators to Evaluate Spatiotemporal Traffic Prediction},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={156-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012699200003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Spatial Performance Indicators to Evaluate Spatiotemporal Traffic Prediction
SN - 978-989-758-703-0
AU - Fathurrahman M.
AU - Gautama S.
PY - 2024
SP - 156
EP - 164
DO - 10.5220/0012699200003702
PB - SciTePress