How AI and data are transforming parking revenue management

Pauline opened her presentation by situating AI within a broader historical arc – beginning with Alan Turing’s foundational question, ‘Can computers think?’, and tracing the journey from early rule-based systems through to the machine learning revolution of the 1990s and the sophisticated optimisation engines that define AI’s commercial applications today.

IDeaS was an early adopter of machine learning for complex business optimisation, and their experience across multiple industries carries a consistent lesson: organisations that extract the most value from AI are those that treat it not as a tool for automation alone, but as a mechanism for fundamentally better decision-making. The volume of data generated by modern parking operations – transactions, occupancy fluctuations, seasonal patterns, event calendars, competitive pricing – is beyond what any human analyst can meaningfully interpret at speed. AI optimisation engines ingest this data and return actionable insights with a granularity and velocity that transforms what operators can realistically do.

A central theme of Pauline’s presentation was the vast quantity of data that businesses generate daily – and how much of it goes underutilised. IDeaS employs optimisation engines that ingest and analyse this data at scale, turning it into the insights organisations need to address complex operational problems.

Their capabilities span four core domains:

• Demand forecasting – predicting future occupancy and revenue based on historical patterns, external signals, and real-time data
• Data segmentation – understanding which customer types behave in which ways, and how pricing can be tailored accordingly
• Inventory management – ensuring the right spaces are available to the right customers at the right times
• Profit optimisation – identifying pricing and allocation strategies that maximise net revenue across the facility

For parking operators, these capabilities represent a step change from the traditional approach of reviewing last month’s performance and making intuitive adjustments. AI-powered optimisation means every pricing and allocation decision is grounded in data – and continuously refined as new data arrives.

Pauline drew specific attention to the parking sector, where IDeaS partners with major international airports including those in Melbourne and Sydney to apply AI-driven revenue management to car parking operations. These are complex environments – multi-product (short-stay, long-stay, valet, pre-book), multi-channel (app, web, walk-up), and highly sensitive to external factors including flight schedules, seasonal travel patterns, and competing off-airport operators.

A key tool in IDeaS’s arsenal for these environments is the digital twin – a virtual simulation of the real-world parking facility that allows operators to model different pricing strategies, demand scenarios, and operational configurations before committing to them in practice. This eliminates the guesswork from strategic decisions and allows airport parking managers to make confident, evidence-based changes with a clear understanding of likely outcomes.

The lessons from these airport applications translate directly to other large-scale parking environments – including hospitals with complex shift-based demand, universities with seasonal fluctuations, and events venues with extreme demand spikes around match days and concerts.

To bring the concept of technology-enabled revenue management to life, Pauline drew an analogy with Formula 1 racing. Success on the track is not a matter of raw power alone – it demands precision, real-time data, and the agility to make the right decision at the right moment under competitive pressure. The same is true of revenue management.

She described a progression that operators can follow to build towards AI-enabled revenue management:

1. Start with big data and business intelligence – establishing a clear, accurate picture of current performance, historical patterns, and the data assets already available
2. Layer in machine learning – moving from descriptive analytics (what happened) to predictive analytics (what is likely to happen)
3. Apply automation – enabling the system to act on predictions without requiring manual intervention for every pricing decision

This progression is accessible to parking operators of all sizes. The starting point is not a sophisticated AI system – it is a commitment to capturing and using data consistently, and building from there.

Perhaps the most commercially significant section of Pauline’s presentation addressed the shift from static, rule-based pricing to dynamic, data-driven pricing in parking. Traditional parking pricing is flat – the same rate applies regardless of demand, time of day, or competitive context. This is both a revenue opportunity lost and a customer experience failure: it charges peak-hour drivers the same as off-peak arrivals while doing nothing to distribute demand more evenly.

To illustrate the commercial impact of dynamic pricing, Pauline walked through a hypothetical scenario: a car park with 100 available spaces. A flat rate of $60 might generate consistent bookings but leaves revenue on the table when demand exceeds comfortable supply. A dynamic pricing model – one that raises rates as occupancy rises and reduces them to stimulate demand during quieter periods – can meaningfully increase total revenue from the same physical asset. As Pauline noted, charging $50 during off-peak periods may actually yield higher total revenue than a flat $60 rate, because it drives higher utilisation across time slots that would otherwise go unfilled.

For operators ready to implement dynamic pricing, the key enablers are accurate demand forecasting, real-time occupancy data, and an automated pricing engine that can act on both without requiring manual oversight. Orikan’s parking management platform supports exactly this capability – enabling operators to move beyond flat rates toward pricing strategies that respond intelligently to real market conditions.

One of Pauline’s most pointed observations was about the direction in which most businesses look when making decisions. The instinct, she noted, is almost always backward – to last year’s performance, last month’s occupancy, last week’s revenue. This rearview-mirror approach is understandable but fundamentally limiting in environments where the future is shaped by unpredictable factors: upcoming events, competitive entrants, economic shifts, or changes in travel behaviour.

AI provides the tools to orient decision-making toward the future rather than the past. By analysing historical data alongside real-time signals and forward-looking indicators – event calendars, booking pace, weather forecasts, competitive pricing – AI-powered systems can generate pricing and demand recommendations that account for what is coming, not just what has already happened.

This shift from reactive to proactive management is arguably the most important cultural change that AI brings to parking operations. It does not replace human judgement – it equips operators with the information needed to exercise that judgement more effectively and with greater confidence.

Pauline Oliver’s presentation at Orikan Collaborate 2024 made a compelling case that is difficult to dismiss: the organisations leading in parking revenue management over the next decade will be those that embrace AI and data now, before competitive pressure makes the transition urgent. The tools are available, the data exists, and the commercial opportunity – as illustrated by Pauline’s pricing scenarios – is substantial.

The shift from reactive, flat-rate parking operations to data-driven, dynamically optimised revenue management is not a distant aspiration. It is a practical pathway that operators can begin today, starting with the data they already have and building toward the AI-powered capabilities that will define the industry’s best performers.

Orikan’s parking management platform is designed to support operators at every stage of this journey – from real-time occupancy monitoring and reporting through to dynamic pricing and demand forecasting tools. To explore how data and AI can transform the commercial performance of your parking operation, speak with the Orikan team today.