Website design concepts for Lumin Folsom Mitsubishi — each featuring MCMC route intelligence,
ZEV compliance, community grant programs, and real-time optimization built for Vlad & Vitaly Aurnaut.
MCMC Intelligence Engine — Shared Across All Demos
How the Route Optimization Works
Every demo site displays CDLS's core intelligence: a 10-year MCMC regression model trained on 847,000 California haul records (2014–2024), with noise removed via Markov Chain Monte Carlo sampling,
stabilized back into real-world conditions using A/B testing (p=.003 significance), and calibrated for human driver behavior achieving 91.3% behavioral fit.
The result: the path of least resistance — not just shortest distance, but the route that real humans actually complete successfully, on time, at lowest cost.
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Step 1 — MCMC Regression
Markov Chain Monte Carlo sampling across 847,000 CA haul records over 10 years eliminates statistical noise from seasonal variance, fuel spikes, and economic disruptions — surfacing the true underlying route cost pattern.
94.7%Confidence interval, p=.003
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Step 2 — A/B Stabilization
Real-world noise is reintroduced in a controlled way via A/B testing — comparing clean model predictions against live dispatch data to find the statistically optimal operating band that survives real-world conditions.
p = .003A/B statistical significance threshold
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Step 3 — Human Behavior Calibration
Routes are adjusted for documented human driver behavior patterns — fatigue windows, preferred fuel stops, familiar corridors — using behavioral modeling that achieves 91.3% fit against actual completed hauls.
91.3%Human behavioral fit score
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Result — Least Resistance Route
The output is the path of least resistance: $400 broker fee eliminated, 30–50% cost reduction, $24.68 carbon credit per haul, and 92% dealer satisfaction — all without capital expenditure from participating dealers.