Like the plot of a film noir whodunit, the world of transportation data is murky. Separating what we suspect about parking and mobility from what we can prove is difficult. There are precious few clues, and those that do exist are buried in a cloud of data thicker than cigarette smoke in a 1940s speakeasy. The facts are convoluted, and the bigger the data, the bigger the mystery.
The stakes are high. Figuring out parking is essential to move people and reduce congestion by shifting demand and getting drivers parked quickly. Inefficiency siphons off revenue, leaving less to invest in vital infrastructure. Further, congestion slows public transit, distracts drivers, and makes bicycling more dangerous. It’s our responsibility as mobility professionals to do all we can to fix broken parking systems, including the application of analytics.
“Analytics” is becoming the buzzword du jour. But like other buzzwords, it has been watered-down through overuse in a marketplace hungry for differentiation. Reports and dashboards, while often helpful, don’t constitute analytics. These tools fail in two key ways:
- First, the provision of datasets alone doesn’t reflect a real understanding of patterns. Comprehending patterns is critical to identifying behavioral trends, the impact of technology, and the effectiveness of business processes.
- Second, these tools don’t communicate patterns and their importance to decision-makers.
That means the detective work rests on you. Most parking professionals, though, are already tasked to the hilt juggling employees, budgets, parking spaces, facilities, equipment, customers, elected officials, and the media requests. There’s very little time to dig into the data.
Sometimes you need help, like an expert hard-boiled and skilled at navigating the dark labyrinth of mysterious data. In the movies, that’s usually a private investigator. In the real world, it’s a data scientist.
Data scientists are trained in statistical and economic theory. Using data classification, data clustering, machine learning, and predictive modeling, data scientists can uncover hidden patterns. These clues are critical to gain insights to guide decision-making and make proactive recommendations to improve parking.
The application of analytics to parking and mobility can achieve critical objectives like:
- Transforming the customer’s experience and saving them time. Parking is a process, and a frustrating one. But we can use data to reduce the time it takes to find a parking space by managing demand, limit the distance to walk to a parking meter, and cut down on the button pushes needed to pay to park. Data can even help make it easier to pay or contest a parking ticket.
- Optimize operations. Data can guide decisions about how we manage our resources and staff, reducing costs and improving parking revenues. We can use data to properly train our personnel and make recommendations about when and where they should work to improve the customer experience, increase revenue, and optimize efficiency.
- Improve sustainability. Analytics is key to understanding returns on investment, managing demand, promoting driving alternatives, and studying green technology. We can use data to reduce wear and tear on parking meters and increase the return on investment of parking technologies.
- Achieve key transportation objectives. We can use parking data to help make the streets safer for vulnerable road users (VRUs), improve access, and solve the first-and-last-mile problem.
We use data to question long-standing myths about parking behavior based on anecdotal evidence. City officials have a duty to their constituents, merchants, and commuters to question assumptions to determine if, like a bad alibi, they risk falling apart under scrutiny. By applying data and using a critical and discerning eye, we can solve the mysteries of parking and mobility. And like any good gumshoe, parking professionals shouldn’t be afraid to lean on their peers or professionals for fresh analytical perspectives and ideas.
About the AuthorMore Content by Matt Darst