Reasons, then tests
- YouImprove westbound progression on West 26th. Keep the 95 s cycle; side streets no worse.
- TrafficPal AIReading counts and controller logs. Testing alternatives.
- TrafficPal AITwo plans meet every condition.
- YouGo with trial 12.
TrafficPal
Nothing to model yourself
Built by us in a few days
Walk away. Come back to tested plans.
TrafficPal AI reads your network, proposes changes and tests each one in simulation against what runs today. The digital twin is yours without TrafficPal AI too: test any plan of your own the same way.
Here is what each step of a study asks of you, with the tools most firms use today and with TrafficPal.
| Step | Today’s tools signal-timing and simulation software | TrafficPal |
|---|---|---|
| Building the model | Weak. Weeks of specialist time. Lanes, volumes and timings entered by hand, intersection by intersection; a simulation also needs its controllers coded and calibrated. All on a desktop install. |
Strong. Delivered ready, in your browser. Upload your data and get access in days, not weeks. Nothing to install; open it from anywhere. |
| Finding a plan | Weak. Optimized by formula. Plans are searched with delay equations and one fixed objective; a simulation, if there is one, only checks the plan they pick. |
Strong. An AI engineer searches with you. You set the goal and the limits; it proposes plans and refines them from each result. Step in at any time: the final choice is yours. |
| Testing and comparing | Weak. Rarely simulated. A simulation is usually out of budget; when there is one, each plan is run on its own and their report tables compared afterwards. |
Strong. Every plan simulated, all on the same plots. Against the same calibrated traffic as today’s plan: travel times, queues, conflicts and the signal performance measures you already use, side by side. |
| Lanes, phasing, detectors | Partial. Possible, by hand. Each new lane or phasing is coded by hand, then estimated by formula or simulated again. |
Strong. Changed like a timing plan. You or the AI engineer change them and test them against your goal. |
| Safety | Weak. Not built in. At best, vehicle paths are exported to separate tools. |
Strong. In every run. Pedestrian and vehicle conflicts, and a check against the MUTCD and the agency’s rules. |
| For the client | Partial. Static reports. Printed analysis tables; shared online dashboards, where offered, are an add-on. |
Strong. A results web page, not just a report. Send the client a page with the comparisons, their plots and clips of the simulation; a printable report too. |
From public documentation for the most widely used signal-timing and traffic-simulation software, and FHWA’s Traffic Analysis Toolbox, October 2026.
Founder and CEO, VTL, a Carnegie Mellon spin-off Professor of Electrical and Computer Engineering, Carnegie Mellon University
Ozan’s research spans vehicular and wireless networks and artificial intelligence; before Carnegie Mellon, he did research at Bell Communications Research (Bellcore). Today he leads a team bringing research at the edge of applied AI into the daily work of traffic engineers.
Priced per study. Early access is open now.
We’re taking on a few first firms. We build the twin of a corridor you choose and deliver it at an early-access price, and you tell us what works and what doesn’t. Places are limited.