Usage Guide

TrafficSafeAnalyzer delivers accident analytics and decision support through a Streamlit interface. This guide walks through the daily workflow, expected inputs, and where to find generated artefacts.

Start the app

Activate your virtual or conda environment（或在容器中运行，见下）.
From the project root, run:
```
streamlit run app.py
```
Open http://localhost:8501. Keep the terminal running while you work in the browser.

使用 Docker？运行 docker build -t trafficsafeanalyzer . 与 docker run --rm -p 8501:8501 trafficsafeanalyzer 后，同样访问 http://localhost:8501。

Load input data

Use the sidebar form labelled “数据与筛选”.

Accident data (.xlsx) — columns should include at minimum:
- 事故时间 (timestamp)
- 所在街道 (region or district)
- 事故类型
- 事故数/accident_count (if absent, the loader aggregates counts)
Strategy data (.xlsx) — include:
- 发布时间
- 交通策略类型
- optional descriptors such as 策略名称, 策略内容
Select the global filters (region, date window, strategy filter) and click 应用数据与筛选.
Uploaded files are cached. Upload a new file or press “Rerun” to refresh after making edits.
Sample datasets for rapid smoke testing live in sample/事故/*.xlsx (accidents) and sample/交通策略/*.xlsx (strategies); copy them before making modifications.

Tip: services/io.py performs validation; rows missing key columns are dropped with a warning in the Streamlit log.

Navigate the workspace

🏠 总览 (Overview) — KPI cards, time-series plot, filtered table, and download buttons for HTML (overview_series.html), CSV (filtered_view.csv), and run metadata (run_metadata.json).
📈 预测模型 (Forecast) — choose an intervention date and horizon, compare ARIMA / KNN / GLM / SVR forecasts, and export arima_forecast.csv（提交后结果会在同一数据集下保留，便于调整其他控件）。
📊 模型评估 (Model evaluation) — run rolling-window backtests, inspect RMSE/MAE/MAPE, and download model_evaluation.csv.
⚠️ 异常检测 (Anomaly detection) — isolation forest marks outliers on the accident series; tweak contamination via the main page controls.
📝 策略评估 (Strategy evaluation) — Aggregates metrics per strategy type, recommends the best option, writes strategy_evaluation_results.csv, and updates recommendation.txt.
⚖️ 策略对比 (Strategy comparison) — side-by-side metrics for selected strategies, useful for “what worked best last month” reviews.
🧪 情景模拟 (Scenario simulation) — apply intervention models (persistent/decay, lagged effects) to test potential roll-outs.
🔍 AI 分析 — 默认示例 API Key/Base URL 已预填，可直接体验；如需切换自有凭据，可在侧边栏更新后生成洞察（运行时读取，不会写入磁盘）。
📍 事故热点 (Hotspot) — reuse the already uploaded accident data to identify high-risk intersections and produce targeted mitigation ideas; no separate hotspot upload is required.

Each tab remembers the active filters from the sidebar so results stay consistent.

Downloaded artefacts

Generated files are saved to the project root unless you override paths in the code:

overview_series.html
filtered_view.csv
run_metadata.json
arima_forecast.csv
model_evaluation.csv
strategy_evaluation_results.csv
recommendation.txt

After a session, review and archive these outputs under docs/ or a dated folder as needed.

Operational tips

Auto refresh: enable from the sidebar (requires streamlit-autorefresh). Set the interval in seconds for live dashboards.
Logging: set LOG_LEVEL=DEBUG before launch to see detailed diagnostics in the terminal and Streamlit log.
Reset filters: choose “全市” and the full date span, then re-run the sidebar form.
Common warnings:
- “数据中没有检测到策略”: verify the strategy Excel file and column names.
- ARIMA failures: shorten the horizon or ensure at least 10 historical data points before the intervention date.
- Hotspot data issues: ensure the accident workbook includes 事故时间, 所在街道, 事故类型, and 事故具体地点 so intersections can be resolved.

Need deeper integration or batch automation? Extract the core functions from services/ and orchestrate them in a notebook or scheduled job.

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