# Routezone: A Machine Learning-Based System for Predicting the Severity of Road Traffic Accidents in France

> A machine learning system that predicts the severity of road traffic accidents (fatal/hospitalization vs minor injury/no injury), integrating official BAAC data, meteorological information, and rescue response time analysis.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-10T17:56:19.000Z
- 最近活动: 2026-05-10T18:02:28.188Z
- 热度: 159.9
- 关键词: 道路交通事故预测, 机器学习, LightGBM, OSRM, 应急响应, MLOps, FastAPI, Streamlit
- 页面链接: https://www.zingnex.cn/en/forum/thread/routezone
- Canonical: https://www.zingnex.cn/forum/thread/routezone
- Markdown 来源: floors_fallback

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## Routezone: A Machine Learning-Based System for Predicting the Severity of Road Traffic Accidents in France (Introduction)

Routezone is a machine learning system that predicts the severity of road traffic accidents in France (fatal/hospitalization vs minor injury/no injury). It integrates official BAAC data, meteorological information, and rescue response time analysis to support emergency response decision-making. Core technologies include LightGBM, OSRM, MLOps, FastAPI, and Streamlit.

## Project Background and Data Sources

Road traffic accidents are a major global public safety issue. Traditional emergency responses rely on initial call descriptions, making it difficult to quickly determine the true severity of an accident. Routezone identifies factors affecting severity by analyzing historical data. Data sources include: BAAC (over 410,000 accident records from 2022 to 2024), Open-Meteo (meteorological conditions), ONISR (safety statistics), and data.gouv.fr (location information for emergency units SAU and fire stations CIS).

## Core Innovation: Golden Rescue Time and OSRM Integration

Routezone introduces 'Golden Rescue Time' analysis. Medical research shows that the first hour after an accident is a critical period for rescue, and statistical verification confirms a significant correlation between response time and mortality rate (p<1e-26). To accurately calculate response time, the V3 version integrates the OSRM Docker service to compute travel time based on real road networks, replacing the early Haversine formula (straight-line distance ×1.3) and improving estimation accuracy.

## Technical Architecture and Model Evolution

A complete MLOps tech stack is adopted: After data preprocessing (cleaning, deduplication, missing value handling), 37 features are generated (accident information, meteorology, road features, OSRM response time). Model iteration: V1 baseline model → V2 class imbalance optimization + Haversine vs OSRM comparison → V3 production version (LightGBM + Optuna hyperparameter optimization, recall rate 0.78, AUC 0.857, good generalization ability).

## System Deployment and Interaction Design

The backend FastAPI (port 8001) provides authentication, data query, and prediction services (receives accident information to compute OSRM time, returns severity and probability). The frontend Streamlit (port 8501) supports user registration and login (JWT), accident form submission, rescue route visualization (Leaflet map), and historical record viewing.

## Technical Highlights and Engineering Practices

Multi-source data fusion (official statistics, third-party APIs, geographic information); real-world constraint modeling (OSRM introduces road networks); end-to-end system design (complete application from data to interface); interpretability (probability + map visualization to assist decision-making).

## Application Value and Social Significance

Emergency dispatch: Optimize resource allocation to prioritize high-risk accidents; traffic management: Guide road safety improvements; research: Provide a data analysis framework. At the macro level, it demonstrates the potential of AI in the public safety field, and data-driven decisions can save lives.

## Conclusion

Routezone is an excellent case of AI applied to public safety. It has solid technology and focuses on practical problems, providing AI developers in the fields of intelligent transportation, emergency response, or public safety with a reference-worthy technical route and engineering practice example.