The Missing Link in Vision Zero? Smarter Curbs Powered by Real-Time AI

There’s one place in towns and cities around the world where delivery vans, cyclists, pedestrians, buses, cars, and emergency vehicles all collide: the curb. Everyone needs it, but there simply isn’t enough of it, causing serious consequences.  

The WHO estimates that around 1.19 million people die each year from road traffic crashes, with more than half being vulnerable road users, including pedestrians, cyclists and motorcyclists. Vision Zero’s goal is to eliminate all traffic fatalities and severe injuries, citing these challenges as a design and systems problem. Serious injuries stem from predictable and preventable interactions that happen long before a crash makes it into the data.

Yet most cities have no system for observing these everyday interactions, with traditional enforcement capturing only snapshots, not the continuous patterns that lead to harm. AI-driven curb management gives cities a way to see and measure interactions. By using computer vision and real-time curb analytics, cities can understand how their streets operate everyday and rethink street design to improve safety.

How AI Removes Safety Blind Spots

With so many people fighting for curb access, conflicts result in blocked bike lanes and double-parking, causing bikes, scooters, buses and other vehicles into traffic. 

Vision Zero, first beginning in 1999 in Sweden, has become a global initiative. Yet focus on curb space has been overlooked because of limited observational capacity, as enforcement officers can’t monitor continuously. The result is a patchwork of data about when, where, or why conflicts emerge. Therefore, there’s a reliance on complaints or crash reports instead of real-time indicators.

However, AI provides continuous visibility into curb activity. Computer vision systems, cameras with privacy protections and AI models detect and classify behaviors relevant to safety and capture micro-patterns that regularly precede injuries. For example, AI can identify:

• A temporarily stopped vs. intentionally parked vehicle.
• A delivery truck blocking a bike lane.
• A ride-hail vehicle loading in a no-loading area.
• How long a vehicle idled.
• Whether it’s causing a safety conflict.

Detection matters for Vision Zero because it provides the ability to quantify risk events instead of reacting only to crashes. It also establishes a baseline of unsafe interactions that informs targeted interventions.

How Automated Curb Management Reduces Conflict

Unsafe curb behaviors are not minor parking issues—they are precursors to potentially life-threatening collisions.

Most curb zones have rules such as ‘no stopping 7–10am’ or ‘commercial loading only,’ and AI cross-references what it sees with the rule set. Therefore, it becomes a real-time referee.

Automated curb management shifts unsafe curb behaviors by replacing subjective enforcement by understaffed parking enforcement departments with consistent, automated rule application. It makes curb rules more predictable and visible while eliminating ambiguity that leads to unsafe improvisation.

Cities can then use AI to monitor dashboards and analytics showing where blockages happen most often and when the same dangerous behaviors repeat. 

From this shift in enforcement consistency, cities can observe increases in compliance with loading and no-stopping rules. Blocked bike lanes and double-parked vehicles decline and conflict points across the curb fall.

Automation affects behavior differently than human patrols because predictability is far more powerful than random enforcement sweeps. When rules are applied uniformly and immediately, drivers understand that the curb has clear expectations. Ambiguity disappears and accidental violations decline as the perception of fairness isn’t tied to individual discretion or chance. 

Vision Zero focuses on reducing exposure to risk, not just reacting to incidents, as most crashes are preceded by hundreds of unsafe interactions and near misses. By reducing the frequency of dangerous behaviors, automated curb management lowers the probability that a dangerous interaction becomes a serious injury. Enforcement becomes a safety intervention, removing the everyday friction and unpredictability that create conditions for harm. 

Using AI Responsibly to Design Safer Streets

AI-generated curbside data is becoming foundational to how cities design safer streets. It can identify conflict hotspots that crash data alone misses and highlights infrastructure that underperforms, like loading zones that are too small. This helps guide redesigns like protected bike lanes, curb extensions, commercial delivery windows, or designated passenger loading areas.

Cities have already begun incorporating AI technology. Denver received a $250,000 grant to create a digital map of curbs, including signage and parking zones, to better understand how the city streets function. In Bellevue, Washington, transportation officials plan to transition street parking from free to paid since they found that over 30% of motorists overstayed the time limit and parking demand regularly exceeded 100%. This meant that motorists parked in crosswalks, blocking hydrants and engaged in other forms of illegal parking.

Evidence shows that visibility is essential to safety. A 2025 Canadian Automotive Association report logged over 600,000 near misses in only seven months finding that serious conflicts occur far more often than crashes—roughly one high-risk near miss for every 770 pedestrian crossings. This confirms how frequently routine curb behaviors create the conditions for harm, including:

• Blocked bike lanes that push cyclists into vehicle traffic.
• Double-parking or informal drop-offs.
• Poor sightlines created by illegally stopped vehicles. 
• Pedestrians stepping out between obstructing vehicles.
• Delivery trucks or ride-hail activity disrupting traffic movement patterns.

AI makes these patterns measurable at scale. Cities can integrate curb analytics with broader mobility systems like school-zone activity, freight peaks, and eventually AVs and sidewalk-robotic operations. This helps them ensure that curb decisions support safety across the entire network. 

However, deploying this technology safely requires disciplined governance. Best-in-class systems apply privacy-by-design at every stage. Video should be processed on-device in real time and deleted immediately, with only anonymized metadata retained for analysis. Any images used to validate system performance should be low-resolution and undergo automatic de-identification, with faces and license plates blurred beyond recognition. 

License plate information must only be collected in designated parking or loading areas and solely for processing payments or citations, after which images are deleted. Access must also be restricted to trained personnel. These protocols ensure that curb analytics serve public safety without expanding the scope of surveillance.

The strategic shift is treating the curb as safety infrastructure, not passive parking real estate. When deployed responsibly, AI gives cities the clarity to spot risks early and redesign infrastructure based on how streets truly function to shape safer behavior. It replaces reactive crash response with proactive prevention, aligning the curb with Vision Zero’s goal of eliminating serious injuries before they occur.

Article by Ganesh Vanama, Computer Vision Engineer at Automotus