TrustMyETA: Routing ETA Uncertainty Quantification
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TrustMyETA: Routing ETA Uncertainty Quantification

React
TypeScript
Tailwind CSS
Node.js
Monte Carlo Sim
Geospatial Analysis
System Architecture
1000+
Monte-Carlo Simulations
Uncertainty Quantification
<100ms
Latency
app
DEMO

Overview

TrustMyETA is a travel time prediction system that calculates a 'Trust Score' for any given route, providing a probabilistic arrival time range based on risk factors like weather, traffic, incidents, and route geometry. It is currently at a prototype stage.

Functional Summary

TrustMyETA is a travel time prediction system that calculates a “Trust Score” for any given route. Instead of a single ETA, it provides a probabilistic arrival time range based on risk factors.

TrustMyETA Dashboard Interface

Dashboard View: Risk Analysis & Trust Score

Core Logic

Uses Monte Carlo Simulation (1000 iterations) to model travel variability.

Input Vectors

Analyzes Route Geometry (tortuosity), real-time Weather (snow, visibility), and Traffic patterns.

Output

A normalized Reliability Score (0-100%), P95 (worst-case) arrival time, and categorized risk factors (Safe / Caution / High).

System Architecture

graph TD User[User] -->|Request ETA| Frontend[React Web App] Frontend -->|POST /api/analyze| Backend[Node.js / Fastify API] subgraph "Trust Engine (Backend)" Backend -->|Get Route| Routing[OSRM / Routing Service] Backend -->|Get Weather| Weather[Open-Meteo API] Backend -->|Get Traffic| Traffic[TomTom API / Mock Service] Routing -->|Route Geometry | Complexity[Complexity Analyzer] Weather -->|Forecast| Normalizer[Risk Normalizer] Traffic -->|Congestion| Normalizer Complexity --> RiskModel[Risk Assessment Model] Normalizer --> RiskModel RiskModel -->|Variance Factors| Simulator[Monte Carlo Simulator] Simulator -->|1000 Iterations| Confidence[Confidence Score & Range] Confidence --> Report[Reliability Report] RiskModel --> Report end Report -->|JSON| Frontend

Technical Stack & Skills

The project demonstrates proficiency in Modern Frontend Engineering, Geospatial Analysis, and System Architecture.

React 19 + Vite
High-performance build tooling & modern React patterns.
TypeScript
Strict typing for robust domain modeling.
Tailwind CSS
Utility-first, responsive design architecture.
Visualization
Complex data plotting with Recharts & Leaflet.

Backend & Data Science Concepts

  • ArchitectureNode.js / Fastify (REST API) for high-throughput request handling.
  • ValidationZod (Schema validation) ensures strict API contracts typesafety.
  • AlgorithmsImplemented custom Monte Carlo statistical modeling and standard deviation analysis in pure TypeScript.

Technical Challenges

One of the detailed challenges was efficiently running 1000+ Monte Carlo iterations in real-time on the client side without blocking the main thread, solved by optimizing the statistical modeling algorithms in TypeScript.

Project Links

Live / Demo

Key Features

Probabilistic Estimation

Uses Monte Carlo simulations (1000 iter.) to go beyond single-point estimates, providing full probability distributions for arrival times.

Confidence Metrics

Calculates a unique 'Trust Score' (normalized 0-100%) and P95 worst-case scenarios to help users make safer travel decisions.

Multi-Input Analysis

Synthesizes route tortuosity, real-time weather data (snow/visibility), and historic traffic patterns into a single reliability metric.