Data Mining Applications for Pedestrian Behaviour Patterns at Unsignalized Crossings

This study analyses pedestrian behaviour patterns at unsignalized crossings using multiple data-mining approaches, aiming to improve pedestrian safety by understanding the relationship between movement patterns, location, and infrastructure. Utilising the STATS19 dataset from the UK Department for Transport, applied data analysis techniques, including heatmap visualisation, association rule learning, and Principal Component Analysis (PCA) with clustering, to identify high-risk behaviours and provide targeted interventions. Heatmap visualisation identifies spatial patterns and high-risk areas, while association rule learning reveals the relationships between pedestrian behaviours and infrastructure elements, highlighting the importance of facility placement and accessibility in encouraging safe crossing. PCA combined with clustering effectively reduces data complexity, revealing key factors that influence pedestrian safety. The findings emphasise the need for appropriate infrastructure, such as strategically placed zebra crossings and central refuges, to guide pedestrian behaviour and reduce accident risks. Underutilised facilities like footbridges and subways require redesign to align with pedestrian preferences. By analysing the relationship between pedestrian behaviour and infrastructure, this study aligns with the United Nations’ sustainability goals, supporting evidence-based interventions to achieve safer and more sustainable urban development. The results of this study offer insights for urban planners to prioritise safety measures and infrastructure improvements that enhance pedestrian safety at unsignalized crossings.