Parking issues are critical in major cities of China nowadays. Searching and waiting for available parking spaces waste travellers’ time and induce traffic congestion on adjacent streets. Advanced parking guidance information systems are urgently needed to provide real-time parking information, predict short-term availability and assist drivers for trip planning. Many studies have been devoted to developing prediction methods for parking space availability. However, most research adopted artificial intelligence techniques instead of proposing theoretical prediction methods. The generation mechanism of parking arrivals and departures still lacks investigation. To this end, this study designed a theoretical method for parking space availability prediction. First, it defined that parking arrivals and departures are generated by past, current and future events. Next, this study developed a prediction model assuming that the probability of parking arrivals and departures obey normal distributions. Then it introduced the parking space availability prediction procedure. The proposed method was examined and analysed with field parking data from Jinan International Airport, Shandong, China. The model prediction results were consistent with field measurements. Additionally, the analysis revealed some parking behaviour characteristics. These findings could lead to implementation of parking prediction in parking guidance information systems.

CICTP