A parking lot in Tokyo, Japan. Cars drive in, but no locking plate rises from the ground. Instead, a camera reads the license plates. When each car entered, how long it stayed, which time slots saw the most traffic, and how much revenue was generated..

Data capture at parking facilities is advancing, and systems are becoming widespread in which AI proposes prices after taking into account factors including the surrounding environment and scheduled construction. The core of the data collection is the combination of license-plate recognition and on-site data, with entry, length of stay, time slot and revenue among the items analyzed.

The catalyst for this change is the strategy of PKSHA Technology, known as Japan's Palantir, which directly acquires companies with on-site operations and data and incorporates AI into them. A representative example is its acquisition of parking equipment maker アイテック as a subsidiary in 2019.

The combination of アイテック's on-site assets and PKSHA's software and AI has driven the expansion of lockless parking facilities and the provision of the i-BARMS PRO data analytics platform. AI learns from operating conditions and the results of price changes, and the system is operated to prepare pricing proposals, with people making the final decisions.

According to results published by PKSHA, the effects of introducing AI included an average 4.7% increase in parking revenue, a 12.6% improvement in gross profit, and a reduction in the time required to prepare on-site analysis reports from 4–5 hours to a few minutes. In addition, converting parking operations data into SaaS is expanding the use of data at operating sites.