Towards Situational Awareness of Urban Pedestrian Flows: An Assessment of Data Quality on the Performance of Predictive Models

This study assesses IoT sensor data's impact on urban pedestrian flow prediction. It contrasts univariate and multivariate LSTM models, revealing the latter's superior performance in accuracy and anomaly detection, especially with incomplete data. Findings advocate for matching data quality with predictive techniques to optimise urban pedestrian flow management.