Originally featured as an online exclusive for Parking Magazine June 2023

Bold predictions about Smart Cities of the future provide a compelling vision of urban living on the horizon. From Paris to Miami, the 15-minute city concept is gaining traction, allowing residents to access most of their daily needs within a short walk or bike ride. San Francisco and Denver are among the cities experimenting with free mass transit, which will remove the cost barrier to public transportation and reduce traffic congestion. Autonomous vehicles are piloting across California and cities like Austin and Las Vegas. And, in many cities, micro-mobility options, including e-bikes and scooters, along with rideshare and delivery apps geared towards making our lives easier, are transforming the fabric of city life.

While many of these developments remain years from being fully realized, the pandemic significantly accelerated how people use technology to move within cities. People who had never used apps like Instacart or Doordash for food delivery were forced to lean heavily on them—and now realize the convenience of keeping them in their post-pandemic lives. Now, as the pace of innovation continues to accelerate, consumers are more open to and consistently embrace new mobility solutions.

All these technologies collect data that sheds light on how people move about a city and the obstacles they experience along the way. This data from various mobility sources can provide invaluable insight into city planning and urban development and improve the quality of life for residents and commuters alike. The problem is that cities don’t often have access to the appropriate data, and even if they did, their current systems infrastructure isn’t equipped to handle it properly. To capitalize and take action on the burgeoning amount of data available, cities need a more seamless, secure, and strategic way to manage it.

This is no small task. Most cities have disparate systems operating behind the scenes, tracking usage, movement, and payments. Many cities have parking, enforcement, and permitting systems that are completely siloed from each other and use platforms at different levels of digitalization. Because they’re disjointed, these systems don’t provide a method for securely aggregating and synthesizing data, centralized reporting, or future technology integration. But, as new technology emerges, cities will have the ability to holistically view their mobility trends in real-time. For example, interactive mapping and comprehensive payment platforms can enable operating and urban planning decisions that are data-driven. Cities can visually understand and monitor parking rates, congestion trends, and fleet movements and effectively manage high-demand street locations by pulling up-to-the-moment data from various sources in one place.

To realize the benefits of this technology, cities must integrate and assimilate all their mobility data so it can be managed effectively and safely. One complication, however, is the collection of personal identifying information (PII), which, if misused or mishandled, presents safety and security risks. It is critical that all cities prioritize their data collection procedures—only collecting PII when necessary and safeguarding and anonymizing data as it accumulates across their systems, keeping PII safe.

Despite these formidable challenges, there is a silver lining. Once assessed and clearly understood, this data, managed through a mobility platform, becomes a powerful asset that can pinpoint obstacles, usage patterns, and transportation needs throughout a city. Machine learning algorithms can analyze data on traffic patterns, commuting habits, and public transit, providing insights to city officials to enable them to optimize mobility infrastructure, improve urban planning, and raise the quality of life for residents.

The benefits of being data-driven are immense. Cities can begin to act against the complexities of mobility management to achieve the desired outputs around livability and accessibility. Traffic congestion can be tamed by identifying the most congested areas and times of day, adjusting traffic signals, installing bus and carpool lanes, and providing financial incentives for delivery vehicles to alter their routes to less trafficked roads. Urban planning can be improved by identifying optimal locations for businesses based on proximity to public transit and the types of services that residents need but don’t have, creating more walkable and convenient neighborhoods. Pedestrian and cyclist safety can be dramatically improved by using data to identify high-risk areas or where accessibility improvements are most needed and then properly allocating resources for protected bike lanes and safer crosswalks.

The data-driven city of the future may seem far away, but cities can start acting now to make it a reality. By integrating a resilient mobility platform into their infrastructure that can grow and adapt to innovations like AI and machine learning, cities can improve urban living today and be ready for what’s to come. In the end, effectively harnessing mobility data is the key to unlocking cities that are more engaging, equitable, and livable for all.