feat: initial release of Road Management Insights (RMI) sample queries library

[n0531m]

Overview

This PR introduces a comprehensive library of persona-driven SQL samples and interactive notebooks for the Road Management Insights (RMI) BigQuery dataset.

The goal of this library is to provide Google Cloud and Google Maps Platform users with production-ready patterns for traffic analysis, operational monitoring, and
predictive modeling, all optimized for the RMI data model.

Key Features

1. Persona-Driven Analytical Patterns

The library is organized into seven distinct analytical personas, each with a dedicated set of queries and a corresponding interactive notebook:

• Traffic Operations Manager: Focused on real-time health, incident response, and latency monitoring.
• Data Scientist: Advanced statistical analysis, including ARIMA+ and TimesFM (Foundation Model) forecasting.
• Urban Planner: Long-term trend analysis, geofenced congestion reporting, and infrastructure project ROI.
• RMI Planner: Business value translation, usage projections, and network gap analysis.
• Data Engineer: Robust ETL patterns, SRI flattening, data cleaning, and quality audits.
• BigQuery Admin: Cost governance, resource lineage, and performance optimization (using INFORMATION_SCHEMA).
• Logistics Coordinator (Preview): SLA reliability, total delay cost, and peak-hour shift analysis.

2. Rigorous Data Quality & Optimization

• Spatial Integrity: Standardized filters (e.g., ST_LineString checks and length deviation thresholds) are applied across all spatial queries to ensure analysis is
  performed on high-integrity geometries.
• Cost Efficiency: Queries utilize BigQuery Scripting (Static Partition Pruning) and clustering strategies to minimize scan volume and maximize performance.
• Foundation Models: Demonstrates "Zero-Shot" multi-series forecasting using Google's TimesFM 2.0 model.

3. Integrated Documentation

• Unified Landing Page: The README.md features a comprehensive Query Catalog, mapping business questions directly to SQL source code.
• Enriched Inline Comments: Every SQL sample has been meticulously commented to explain both the technical "how" and the business "why" behind the logic.

4. Interactive Notebooks

Seven persona-specific Jupyter notebooks are included, pre-configured for one-click import into Google Colab, Colab Enterprise, or BigQuery Studio.

Verification

All queries have been end-to-end verified against the boston_oct_2025_sample_data reference dataset. Training and evaluation metrics for BigQuery ML models have been
validated for accuracy and diurnal consistency.

Directory Structure

• /queries: Categorized SQL files (GA and Preview).
• /notebooks: Persona-specific interactive assets.
• README.md: Integrated library guide and query catalog.

[henrikvalv3]

Is this correct?

[henrikvalv3]

• The readme is missing links to the following queries: bqa2, bqa4, bqa5, de6, rmip5, and up6
• README.md has broken links due to a /ga/ path segment

[n0531m]

Good catch! Are you aware of any good link checkers?