74 lines
4.3 KiB
Markdown
74 lines
4.3 KiB
Markdown
# Usage Guide
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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.
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## Start the app
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1. Activate your virtual or conda environment.
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2. From the project root, run:
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```bash
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streamlit run app.py
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```
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3. Open `http://localhost:8501`. Keep the terminal running while you work in the browser.
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## Load input data
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Use the sidebar form labelled “数据与筛选”.
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- **Accident data (`.xlsx`)** — columns should include at minimum:
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- `事故时间` (timestamp)
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- `所在街道` (region or district)
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- `事故类型`
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- `事故数`/`accident_count` (if absent, the loader aggregates counts)
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- **Strategy data (`.xlsx`)** — include:
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- `发布时间`
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- `交通策略类型`
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- optional descriptors such as `策略名称`, `策略内容`
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- Select the global filters (region, date window, strategy filter) and click `应用数据与筛选`.
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- Uploaded files are cached. Upload a new file or press “Rerun” to refresh after making edits.
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- 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.
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> Tip: `services/io.py` performs validation; rows missing key columns are dropped with a warning in the Streamlit log.
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## Navigate the workspace
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- **🏠 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`).
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- **📈 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）。
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- **📊 模型评估 (Model evaluation)** — run rolling-window backtests, inspect RMSE/MAE/MAPE, and download `model_evaluation.csv`.
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- **⚠️ 异常检测 (Anomaly detection)** — isolation forest marks outliers on the accident series; tweak contamination via the main page controls.
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- **📝 策略评估 (Strategy evaluation)** — Aggregates metrics per strategy type, recommends the best option, writes `strategy_evaluation_results.csv`, and updates `recommendation.txt`.
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- **⚖️ 策略对比 (Strategy comparison)** — side-by-side metrics for selected strategies, useful for “what worked best last month” reviews.
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- **🧪 情景模拟 (Scenario simulation)** — apply intervention models (persistent/decay, lagged effects) to test potential roll-outs.
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- **🔍 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）。
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- **📍 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.
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Each tab remembers the active filters from the sidebar so results stay consistent.
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## Downloaded artefacts
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Generated files are saved to the project root unless you override paths in the code:
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- `overview_series.html`
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- `filtered_view.csv`
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- `run_metadata.json`
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- `arima_forecast.csv`
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- `model_evaluation.csv`
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- `strategy_evaluation_results.csv`
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- `recommendation.txt`
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After a session, review and archive these outputs under `docs/` or a dated folder as needed.
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## Operational tips
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- **Auto refresh**: enable from the sidebar (requires `streamlit-autorefresh`). Set the interval in seconds for live dashboards.
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- **Logging**: set `LOG_LEVEL=DEBUG` before launch to see detailed diagnostics in the terminal and Streamlit log.
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- **Reset filters**: choose “全市” and the full date span, then re-run the sidebar form.
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- **Common warnings**:
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- *“数据中没有检测到策略”*: verify the strategy Excel file and column names.
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- *ARIMA failures*: shorten the horizon or ensure at least 10 historical data points before the intervention date.
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- *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.
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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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