Traditional vs AI Traffic Counting: Why Smart Cities Switched in 2025

We've been in the traffic analytics space for eight years now, and we've watched the industry hit a wall. The traditional methods that transportation departments have relied on for decades? They're breaking under the weight of modern demands.

The Real Cost of Sticking with Manual Counts

Let's be honest about what manual traffic counting actually involves.

It's expensive. Every survey means deploying multiple trained observers (often two or more per lane. Those hours add up fast. When you're managing tight municipal budgets, each survey becomes a careful calculation of whether you can afford to gather the data you actually need.

It's prone to error. We're not criticizing the surveyors—they're professionals doing difficult work. But humans get tired. After hours of watching traffic, concentration slips. When you're trying to simultaneously track turning vehicles, pedestrians crossing, and cyclists weaving through, even the best observer will miss things. Add rain or extreme heat, and accuracy drops further.

Coverage is fundamentally limited. Want 24/7 monitoring? Need data from 20 intersections simultaneously? Manual methods simply can't scale to that. You end up with incomplete data from select locations and then you're making million-dollar infrastructure decisions based on those limited samples.

Safety is always a concern. Every time you put a surveyor roadside or install pneumatic tubes on an active road, there's risk. It's a reality of the work, but it's one we should be moving past.

Modern Transportation Demands More Than Manual Methods Can Deliver

The transportation networks we're managing today weren't what manual counting was designed for.

Traffic patterns have gotten chaotic. Uber and Lyft changed everything. Amazon delivery vans show up at weird hours. Commute patterns shifted after the pandemic and never fully shifted back. Those traditional peak hours? They're spreading and fragmenting. A four-hour manual count on a Tuesday morning doesn't tell you what Thursday afternoon looks like, let alone capture overnight delivery traffic.

Then there's multimodal complexity. Modern intersections aren't just about cars anymore. You've got dedicated bus lanes, bike signals, e-scooters, pedestrian crossings—all interacting in ways that matter for safety and efficiency. Try having one person track all of that simultaneously at a busy downtown intersection. It's nearly impossible.

And cities need answers faster than ever. When a new bike lane goes in or signal timing changes, departments want to know the impact quickly—not weeks later after manual data entry and processing.

How is GoodVision Solving This with AI Traffic Analytics

This is where we come in. Since 2017, we've built a platform that uses AI and computer vision to automatically analyze traffic video footage. Our algorithms detect and track vehicles, pedestrians, cyclists - basically everything moving through the scene - and generate comprehensive traffic data without anyone having to watch the video.

This approach delivers measurable advantages:

Processing happens automatically. Upload your footage, and we handle the rest. What would take your team days or weeks to manually review gets processed in about an hour. Your staff can focus on actually analyzing the data and making decisions, not on staring at video counting vehicles.

Consistency you can rely on. Our AI model maintains 95%+ accuracy throughout the entire video duration. Hour one looks the same as hour eight. No fatigue, no distraction. We've trained our model on over 2 billion data points, so it handles diverse traffic scenarios reliably.

Scale to any size project. One intersection or citywide study—our cloud infrastructure handles both. Need to analyze 1,000 hours of footage? Same process as one hour, just more compute time. Projects that used to require weeks of manual effort now complete in days.

Track 'em all from one video source. That single video of your intersection? It gives you cars, trucks, buses, motorcycles, bicycles, and pedestrians all at once. One survey, multiple datasets. Need to pull pedestrian counts six months later for a different project? Query the same data. No need to go back and recount.

No one in harm's way. Cameras mount safely on existing infrastructure. No surveyors standing roadside, no equipment in active lanes. Analyze footage after collection or stream it live for real-time insights.

Better economics. Yes, there's a software cost. But compared to repeated manual surveys? The savings compound quickly. You're getting exponentially more data per dollar spent, especially when you leverage cameras you already have.

Real Projects, Real Results

We don't just talk about benefits of automated traffic data collection in theory. We see them play out daily.

In Los Angeles, a consultancy used our platform to analyze a dangerous intersection handling 30,000 vehicles daily. They processed over 100 hours of footage covering 45,000 vehicle movements and identified that near-misses were occurring at three per hour, with 64% tied to left turns. That quantified data supported their recommendation for protected left-turn phases before more crashes happened.

New Zealand Transport Agency came to us because manual data collection was eating up their budget and timeline. They saw 50-60% cost savings in data processing and management, with faster turnaround on deliverables. The automation we provided for classification, causal analysis, and reporting improved both their project delivery speed and the quality of their recommendations.

In Warsaw, a consulting firm faced a brutal deadline on an intermodal hub design. They had 14 days for the entire traffic analysis piece. Using our platform's fast processing and intuitive interface AND our particularly strong pedestrian counting accuracy they completed everything: data collection, parameter calibration, and transport solution design, all within that two-week window.

What You Need to Know About Implementation (Hint: not a lot)

Making this work isn't complicated, but there are a few practical considerations:

Video quality matters, but standard equipment works fine. Regular HD (720p or 1080p) at normal bitrates is sufficient for most scenarios. Frame rates of 15-30 FPS work best for busy scenes, though we can process down to 10 FPS. The basic test: if you can clearly see vehicles in the video, our AI can analyze them.

Your existing cameras probably work. We process common video formats and even live RTSP feeds for long-term traffic surveys. Use footage from your CCTV system, custom-made cameras, or drone videos. No proprietary hardware is needed.

Camera positioning makes a difference. Unobstructed views yield better data. For ideal results, mount cameras 8-12 meters high for intersections, make sure they're stable, and ensure you can see all the lanes you care about. The fundamentals of good video still apply, but you don't need specialized equipment.

Our platform is built for your workflow. Upload footage, let it process, then explore results through our web interface or desktop app. Visualize traffic volumes over time, map movement paths, chart modal share. Filter interactively to extract specific metrics. 

Making the Transition

The gap between what manual methods can deliver and what you actually need keeps widening. We're not saying this to sell you something (well, maybe kind of), we're saying it because we've watched it happen across hundreds of transportation departments over the years.

You need more comprehensive data. You need it faster. You need it to cover longer time periods and more locations. Manual processes simply haven't evolved to match those requirements.

AI video analytics gives you a practical way forward. It works with the cameras you have, processes data automatically, and delivers results through a cloud platform you can access anywhere. Agencies we work with report significant time and cost savings while getting richer datasets that support better decisions.

So here's the question: Can your current data collection methods actually provide the quality and quantity of information your projects need?

If you're reading this, you probably already know the answer.

Ready to see what this looks like for your projects? Request a free demo. Upload your traffic footage and get comprehensive analysis in hours, not weeks.

About GoodVision: We've spent eight years building AI video analytics for traffic engineers and urban planners. Our model is trained on over 2 billion data points, works with standard video equipment, and delivers results through an intuitive cloud platform. No specialized hardware required.