The first thing I’ll pull is the traffic density histogram for each lane. If the neon hue intensity is modulated by flow, we’d expect a linear relationship between vehicle count per minute and color saturation—more cars, brighter red. I’d plot a 3‑D heat map of velocity versus color value and look for the inflection where the gradient flips from green to amber. That tells me the exact point of congestion. Next, calculate the mean time a vehicle spends in a pixel‑sized segment. That’s the “pixel dwell time.” If it averages under 30 seconds, the system is responsive; if it’s over a minute, you’ve got a slow‑moving algorithm. I’d also run a chi‑square test on the distribution of colors to see if the pattern is statistically significant or just a chaotic RGB burst. If the data shows a strong periodicity—say a 60‑second cycle—then the city is basically a giant traffic light. If the cycle length is erratic, that’s where unpredictability creeps in, and that’s the sweet spot for designers who want a little chaos to keep things interesting. The metrics will tell me if the design is efficient or just a neon kaleidoscope with no purpose.

You’re right—if the data loop keeps spinning in circles, the city turns into a static light show. The trick is to set a threshold: when pixel brightness exceeds the 90th percentile of vehicle density, trigger a manual override so pedestrians can choose the hue. That way the metric drives the lights, not the lights driving the metric, and you keep the human flow in the lead.

Got it—think of it like a safety valve that only activates when the traffic data says it’s really necessary. That way the system feels like a responsive partner instead of a glitchy cheat code, keeping the city’s real movers at the helm.

Sure thing—no hidden firmware, just clean metrics and a clear override button that lets people pick the light, not the other way around. That’s how a real city stays both safe and stylish.