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Operated by: JPSM Group
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SmoothQ

Our Technology

Next-Generation Prediction Engine Combining M/M/c Queue Theory and Machine Learning

M/M/c Queue Theory

SmoothQ's prediction engine is built on mathematically proven M/M/c queue theory. We calculate theoretical wait times from three parameters: arrival rate (位), service rate (渭), and number of servers (c).

Little's Law

Wq = Lq / 位

Wq = Average Wait Time, Lq = Average Queue Length, 位 = Arrival Rate

位 (Lambda)

Arrival Rate (customers per hour)

渭 (Mu)

Service Rate (processing capacity per server)

c

Number of Service Windows

AI & Machine Learning

In addition to theoretical models, we leverage state-of-the-art machine learning to improve prediction accuracy. We consider diverse factors including historical data patterns, weather, and event information.

Time Series Analysis

Learning patterns by day and time from historical congestion data to predict future congestion.

Behavioral Psychology Model

Analyzing user behavior patterns from a psychological perspective to factor in congestion avoidance behavior.

External Factor Analysis

Analyzing and reflecting the impact of external factors like weather, holidays, and local events on congestion.

Ensemble Learning

Achieving higher accuracy and stability than single models by combining multiple prediction models.

Data Quality Tiers

Verified Data (Tier 1)

Highest precision predictions based on directly obtained facility data

Accuracy: 95%+

Historical Data (Tier 2)

Prediction model based on historical measurement data

Accuracy: 85-95%

Estimated Model (Tier 3)

Predictions estimated from facility characteristics

Accuracy: 70-85%

Statistical Model (Tier 4)

Estimation based on statistical data from similar facilities

Accuracy: Reference

System Architecture

Our cloud-native system design balances high availability and low latency, providing stable service 24/7/365.

1

Data Collection

Real-time data collection from 130,000+ facilities nationwide. Supporting APIs, scraping, and various methods.

2

Data Processing

Normalizing and cleansing collected data, transforming it into formats suitable for prediction models.

3

Prediction Engine

Combining M/M/c theory with AI models to generate high-precision wait time predictions.

4

Delivery

Delivering prediction results to users nationwide with low latency through CDN.

Want to Use Our Prediction Data via API?

Integrate SmoothQ's prediction engine into your application.

View API Documentation