Deploying AI in real-world intelligent transportation systems (ITS) remains hard: traffic data is noisy and incomplete, learned policies rarely transfer to new cities, and black-box models cannot earn the trust of operators and public officials. Drawing on years of joint experience in ITS industry and academia (including traffic signal control systems deployed across multiple cities and AI for one of the world's busiest metro networks), this paper presents TrafficPDE: a practitioner's guidebook organized around the Perception-Decision-Explanation (PDE) triangle. Perception tackles how to model traffic data reliably despite noise, missing sensors, and unobserved locations. Decision develops generalizable reinforcement learning agents that transfer across cities without costly re-calibration. Explanation builds causal DAG frameworks that make AI decisions interpretable to transportation practitioners. We show the three vertices reinforce each other, and close with three golden rules for any deployable ITS model: handle long-tail cases safely, operate from day one, and explain itself to people with a transportation background. TrafficPDE is not a survey of what has been done. It is a guidebook for what must be built.