Last year at the National Parking Association (NPA) conference, ParkEngage proudly introduced one of the most innovative parking management solutions in recent years: Software Defined PARCS (Parking Access and Revenue Control Systems). It was an exciting moment for us and for the industry, as we introduced a new way to approach parking operations—one that is more flexible, adaptable, and technology-driven than traditional methods.

A Game-Changer for Parking Operators

Software Defined PARCS (SDP) completely redefines how parking management systems are built and operated. In traditional setups, parking operators have relied heavily on hardware-dependent systems—kiosks, barriers, and physical infrastructure that require ongoing maintenance and frequent upgrades. This model often results in high costs and operational downtime, especially as technology evolves.

But with Software Defined PARCS, the reliance on hardware is significantly reduced. Instead, ParkEngage’s cloud-based, IoT-enabled system integrates the latest technology to deliver a dynamic, scalable solution that can be updated and upgraded with a few clicks—without needing to replace physical hardware. For parking operators, this means fewer disruptions, lower maintenance costs, and the flexibility to introduce new services on demand.

How Software Defined PARCS Works

At the core of SDP is its cloud-based infrastructure, which allows for real-time communication and updates across all components of the parking system. By leveraging the power of the Internet of Things (IoT), every part of the parking operation—whether it’s entry and exit systems, payment platforms, or enforcement tools—can be managed and optimized remotely.

Here’s what makes SDP stand out:

• Dynamic Flexibility: Operators can quickly adapt their system to accommodate changing needs. Whether it’s adding new payment methods, modifying pricing, or adjusting access control for special events, all changes can be made through the software without the need for new hardware installations.
• Seamless Integration: SDP integrates effortlessly with existing infrastructure, allowing operators to upgrade without overhauling their entire system. It works with LPR (License Plate Recognition), mobile payments, and other touchless technologies, making it future-proof.
• Cost Efficiency: By minimizing hardware dependency, Software Defined PARCS eliminates the need for frequent physical upgrades, saving operators on both capital expenditures and maintenance costs.
• Real-Time Data Analytics: SDP provides operators with actionable data, allowing them to optimize space management, reduce congestion, and adjust pricing in real time.

A Year of Success: The Impact on the Industry

Since its introduction, Software Defined PARCS has already begun to transform parking management across the industry. Many operators who have adopted SDP are now experiencing increased efficiency, smoother operations, and significant cost savings.

For parking operators, the benefits are clear:

• Scalability: As operations grow, SDP grows with you. There’s no need to worry about outgrowing your parking infrastructure when the system can be upgraded at any time through the cloud.
• Customer Satisfaction: With faster entry and exit times, seamless payment methods, and overall frictionless parking, customers enjoy a vastly improved experience.
• Reduced Downtime: The days of waiting for hardware replacements or repairs are gone. Software updates and upgrades happen in real time, ensuring your system is always up-to-date and fully operational.

Looking Ahead: The Future of Parking is Here

As we gear up for this year’s National Parking Association conference, we are excited to reflect on the transformative impact Software Defined PARCS has had on parking management. This innovation is just the beginning. At ParkEngage, we are committed to continuous improvement, developing even more ways for parking operators to streamline their operations, reduce costs, and enhance the customer experience.

Are you ready to transform your parking operations with Software Defined PARCS? Connect with us today to learn more about how SDP can revolutionize the way you manage your facilities.

Bringing Equity in Mobility

• By Rajiv Jain

Published in Parking & Mobility (May 2024)

Every new technological cycle leads to greater advancements but also brings significant disruptions to society’s social fabric. From the Industrial Revolution to the Digital Revolution, we have seen how, on one hand, technologies have uplifted societies worldwide; on the other hand, although unintended, they have created ever-widening social divides.

Now, we are at the dawn of another technology revolution: the Artificial Intelligence (AI) Revolution. This time, it is going to be different. The underpinnings of this technological revolution are very different.

In the past, different factions of society adopted every new technology at different levels, leading to significant advancement for some and further marginalization of the disadvantaged.

Three factors explain this disparity in the adoption of any new technology, and these factors can make it different this time around with the AI revolution.

1. Usability gap. Unlike in the past when human-to-technology interfaces were built keeping common users in mind while disregarding the disadvantaged, the new interfaces for AI are going to be natural human expressions, including natural languages.
2. Content gap. In the past, technology was merely a tool. The value one derived from using that tool varied with the skills of the person using it. The self-generative capability of AI changes the status of technology in this new cycle of technology revolution, from a “passive tool” to an “active and participating companion.”
3. Innovation gap. In the past, due to high barriers to technological innovation, primary drivers for innovation were misaligned from the priorities of social good. It required significant resources, profit-driven entrepreneurs, or heavily funded public bodies to bring innovation. When AI takes over the role of the innovator, then the “profit for few” will give way to the “social good for all” as the primary driver of innovation. Of course, that greatly depends on the social values that we impart into the AI training models now while AI is in its infancy.

AI Makes the Technology Framework for Equity in Mobility Implementable

But how will this AI revolution change technology adoption, leading to social good in mobility?

In the May 2022 edition of Parking & Mobility magazine, I wrote an article proposing a comprehensive technology framework for defining adaptive public policies for social equity in mobility. Since then, various public bodies at different levels have done a lot of policy work. There has also been widespread recognition that solving social inequities is not just the government’s responsibility.

However, the technology framework defined in my earlier article was a resource-intensive proposition for any organization, whether public or private, to bring to fruition. Conceptually, the framework is a complete solution to the social equity in mobility problem, but since it was built upon pre-AI technologies, the framework suffered from the above-mentioned gaps in usability, content, and innovation, making its implementation very prohibitive.

Let’s look at each of the five layers of the framework—data collection, data privacy, quantification, policy definition, and policy execution—and see how AI bridges the above-mentioned gaps and makes the overall framework more implementable when applied to them.

Human-generated data collection is essential in any analytical model to effectively understand and solve social inequity problems. A data collection framework severely fails to collect human-generated data without natural human expression and natural language interfaces. AI bridges this usability gap with its natural interface languages.

A data privacy framework is essential for the overall trust in the system. Any leakage of private data can lead to long-lasting mistrust in the system and future impediments to cooperation and data sharing by individuals and organizations. AI is unparalleled in identifying private information patterns and automatically self-generating anonymization. AI efficiently bridges this content gap.

AI also bridges the content gap in the Quantification framework. With its sophisticated pattern recognition and content generation capability, AI can very efficiently enable granularization and localization of large and diverse datasets, leading to the analytics down to recognizing actionable patterns of information.

A policy definition framework requires not only very specialized data science skills but also strong domain expertise in policy making. Moreover, both technical and policy experts must work hand in hand to perform analysis and make predictions. Typically, there is a significant communication barrier between the two experts due to the language of their respective professions. AI bridges the usability gap by learning from policy experts and bridges the content gap by applying social AI models for predictions and performing the impact analysis of their predictions.

Finally, the policy execution framework is where bridging the innovation gap is most crucial. Significant resources are required to roll out social equity policies. With very few financial rewards to profit-seeking entrepreneurs and cash-strapped government bodies, traditional drivers of innovation are absent for individuals and organizations to execute these social equity policies. This is where AI becomes the innovator and flips the drivers from the “profit for few” to the “social good for all,” thus bridging the innovation gap.

Conclusion

The AI Revolution will be very different from any other technology revolution. It will eliminate the age-old fallacies of technology innovation, which have always led to unintended harm to the social fabric of society through the widening of the social divide and the irreparable marginalization of the disadvantaged.

Of course, that greatly depends on the social values we impart into AI training models. If the foundational models are trained on value systems, social inequities must be addressed first for society to thrive. Then AI will balance and maximize social good in its every prediction and prescription.