Parking demand diagnosis by automated payment transaction (APT) data: an application in a small-sized tourist city

The parking demand is a fundamental datum to evaluate and implement integrated policies of sustainable urban mobility in urban areas. Parking demand is hard to quantify and relevant in small- and medium-sized urban systems, especially with a high-tourist interest due to temporary flows and few available resources. The previous studies highlighted how demand assessment is a complex task, usually addressed by prediction models or exploitation of sensors in the field. Nevertheless, studies have yet to focus on the potential of automated payment transaction (APT) technologies in data collection and processing for parking demand analysis. However, they involve several challenges in data handling. This study proposes a method to automatically handle APT raw data to estimate some drivers of parking demand, such as the parking occupancy rates, the parking average time duration, and the rotation index of block-level on-street parking and/or parking area. The method has been experimented in the small-sized and touristic city of Sirmione (Italy) and used 23+ million APT data collected in 2 months of observation. The results are represented by control dashboards that are easy to read and interpret. The experimentation shows that practitioners and public administrations can adopt this method to diagnose parking demand with great accuracy and derive recommenda tions for future transport and urban planning. In the new paradigm of demand-oriented services, this method is crucial to quantify the ability of administrators to address parking demand.