From 00e766eaa738c61d0e6fbfd63a52d65e7468a9b6 Mon Sep 17 00:00:00 2001 From: ntnt Date: Fri, 10 Oct 2025 08:13:22 +0800 Subject: [PATCH] =?UTF-8?q?modify:=20=E5=88=A0=E9=99=A4=E9=9A=90=E7=A7=81?= =?UTF-8?q?=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .DS_Store | Bin 8196 -> 0 bytes .gitignore | 5 - CHANGELOG.md | 47 - LICENSE | 19 - app.py | 1576 ------------------------------- docs/install.md | 0 docs/usage.md | 0 overview_series.html | 7 - readme.md | 148 --- recommendation.txt | 1 - requirements.txt | 31 - run_metadata.json | 11 - simulation.html | 7 - strategy_evaluation_results.csv | 5 - 14 files changed, 1857 deletions(-) delete mode 100644 .DS_Store delete mode 100644 .gitignore delete mode 100644 CHANGELOG.md delete mode 100644 LICENSE delete mode 100644 app.py delete mode 100644 docs/install.md delete mode 100644 docs/usage.md delete mode 100644 overview_series.html delete mode 100644 readme.md delete mode 100644 recommendation.txt delete mode 100644 requirements.txt delete mode 100644 run_metadata.json delete mode 100644 simulation.html delete mode 100644 strategy_evaluation_results.csv diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 129a0d4986cee8f4ca72c20b6796fc7ddadca743..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8196 zcmeHMTWl0n7(U5vDjZYeHktc7F2NgBGXrgHKQSkv|e86bb7ftX*<-z})GYhtbH)BxdBy+y`|MUOn z?D^;W=k%X4#?W1`Zey&TF{aTa(5RvA3QdfQYfj^#rh*XU&zQ?>Hz@XGTt73%I#NUt zh$0Y0Ac{Z~fhYpkLImi{7EQa#xvxf}K8ipTf$K5?;{6b(OJE|zl0y0FpiY(mgq5V0 zjqa%qkUsGc6CsuqN>`dwRSyVV5tbNG?lc}3=7fn5OA3`cgK}pGS4LQ&K(0=HacRzw zP#E=51fmGcjew}#t69L(Y>bW1tluk=nQ_+%^6l+kgI82mRnOC6^K0}Q%)!*q@P2*7 zSX6ZEiT?4t<9oq9b}k?sF=~TRe_~tK_x9v$F+P~}-NKj`j2H{EX*XMN(|Nngb(78m zHtA}EY|eMxJ$~SbX@*jHdonM^<_x8V13#DVk&~qynqOHNg}~lx)1*$(?xm@NCA0R3 z*r^3|%Uf5rt?SsF=-amI?z6SV!bOYqrTVaA*Y$4Xf&eqm9G~U!?n#$m^L)kP{$T&v}PJYU0(KNY6+-%klSoE8A`IcD4 z(u~tI53Rn!Y&Q3ck)E7Qy%jpFGH=oM1+;n1NGrp&db_^we#f(7MxZq7^$q5Lo6gv7 z%&^oto%%*|u&Cl6Gj=HD7JaFi%KBqtw8bE%bX{hTc~C67Gacmn$8Asiq-iyFCyfmU zqv@R8KjGQA@E2_-t$d`U7fyYallSEyMPqlfX%30$x-3iXp|QT9)=ZiMn(>7;Vi=BB zbn^6I=ur_A0`KZAXG@=!2umK(D#2x5HKlWfRk~n}-I5>g;lbFIW9LFPg z6p!Hqp1{+10WabuoWyH*6K~-(-oXc$#%K5(U*bHz!}s_Bzu*u2iHp37*YSGZz?=CR z-p1GR4&KQ(@=bg<@8>C==0~{CC)EaccuKK*m0a%m6y42Ew{i8kjGwx4x;5Rkb=&qG z|DAL@w?HP?lBGA+#aFDp_4dv!U6+Wcm|G_6-%~*Henz%P-b-H0;_O+OiTbkT<rdh-Jd5Y>Jn{Wyynf2K<$U1JdtXFs)iQUCW{ z{rkT(in=HQQ3U><5kO^cvbUS|cD47msI}vC9imG#VOLTpU8s}gIH4@Z2`~M_knZDT eDtrQi6N9TWZ{!98@x7}X?jQUvs diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 47477e0..0000000 --- a/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -sample/ -strategy_evaluation_results.csv -run_metadata.json -*.log -simulation.html diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index d7cdd69..0000000 --- a/CHANGELOG.md +++ /dev/null @@ -1,47 +0,0 @@ -# Changelog - -## [1.1.0] - 2025-08-28 - -### Added -- Integrated GPT-based analysis for comprehensive traffic safety insights -- Added automated report generation with AI-powered recommendations -- Implemented natural language query processing for data exploration -- Added export functionality for analysis reports (PDF/CSV formats) -- Included sentiment analysis for accident description texts - -### Enhanced -- Improved data visualization with interactive charts and heatmaps -- Optimized prediction algorithms with enhanced machine learning models -- Expanded dataset with additional traffic parameters and weather conditions -- Upgraded user interface with responsive design and dark mode support - -### Fixed -- Resolved session state KeyError in multi-tab navigation -- Fixed data persistence issues between application refreshes -- Corrected timestamp parsing errors in accident data import -- Addressed memory leaks in large dataset processing - -### Documentation -- Updated README with new GPT analysis features and usage examples -- Added API documentation for extended functionality -- Included sample datasets and tutorial guides - -## [1.0.0] - 2025-08-19 - -### Added -- Initial release of TrafficSafeAnalyzer -- Streamlit app with tabs for data analysis, prediction, and strategy evaluation - -### Fixed -- Resolved session state KeyError - -## [1.0.0] - 2025-08-19 - -### Added - -- Initial release of TrafficSafeAnalyzer. -- Streamlit app with tabs for data analysis, prediction, and strategy evaluation. - -### Fixed - -- Resolved session state KeyError. \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 670a9dc..0000000 --- a/LICENSE +++ /dev/null @@ -1,19 +0,0 @@ -Copyright (c) 2025 TongNian - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file diff --git a/app.py b/app.py deleted file mode 100644 index 2c3905b..0000000 --- a/app.py +++ /dev/null @@ -1,1576 +0,0 @@ - -import os -from datetime import datetime, timedelta -import json -import hashlib - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from sklearn.neighbors import KNeighborsRegressor -from sklearn.ensemble import IsolationForest -from sklearn.svm import SVR - -import statsmodels.api as sm -from statsmodels.tsa.arima.model import ARIMA - -import streamlit as st -import plotly.graph_objects as go -import plotly.express as px - -# --- Optional deps (graceful fallback) -try: - from scipy.stats import ttest_ind, mannwhitneyu - HAS_SCIPY = True -except Exception: - HAS_SCIPY = False - -try: - from streamlit_autorefresh import st_autorefresh - HAS_AUTOREFRESH = True -except Exception: - HAS_AUTOREFRESH = False - -# Add import for OpenAI API -try: - from openai import OpenAI - HAS_OPENAI = True -except Exception: - HAS_OPENAI = False - - -# ======================= -# 1. Data Integration -# ======================= -@st.cache_data(show_spinner=False) -def load_and_clean_data(accident_file, strategy_file): - accident_df = pd.read_excel(accident_file, sheet_name=None) - accident_data = pd.concat(accident_df.values(), ignore_index=True) - - accident_data['事故时间'] = pd.to_datetime(accident_data['事故时间']) - accident_data = accident_data.dropna(subset=['事故时间', '所在街道', '事故类型']) - - strategy_df = pd.read_excel(strategy_file) - strategy_df['发布时间'] = pd.to_datetime(strategy_df['发布时间']) - strategy_df = strategy_df.dropna(subset=['发布时间', '交通策略类型']) - - severity_map = {'财损': 1, '伤人': 2, '亡人': 4} - accident_data['severity'] = accident_data['事故类型'].map(severity_map).fillna(1) - - accident_data = accident_data[['事故时间', '所在街道', '事故类型', 'severity']] \ - .rename(columns={'事故时间': 'date_time', '所在街道': 'region', '事故类型': 'category'}) - strategy_df = strategy_df[['发布时间', '交通策略类型']] \ - .rename(columns={'发布时间': 'date_time', '交通策略类型': 'strategy_type'}) - - return accident_data, strategy_df - - -@st.cache_data(show_spinner=False) -def aggregate_daily_data(accident_data: pd.DataFrame, strategy_data: pd.DataFrame) -> pd.DataFrame: - # City-level aggregation - accident_data = accident_data.copy() - strategy_data = strategy_data.copy() - - accident_data['date'] = accident_data['date_time'].dt.date - daily_accidents = accident_data.groupby('date').agg( - accident_count=('date_time', 'count'), - severity=('severity', 'sum') - ) - daily_accidents.index = pd.to_datetime(daily_accidents.index) - - strategy_data['date'] = strategy_data['date_time'].dt.date - daily_strategies = strategy_data.groupby('date')['strategy_type'].apply(list) - daily_strategies.index = pd.to_datetime(daily_strategies.index) - - combined = daily_accidents.join(daily_strategies, how='left') - combined['strategy_type'] = combined['strategy_type'].apply(lambda x: x if isinstance(x, list) else []) - combined = combined.asfreq('D') - combined[['accident_count', 'severity']] = combined[['accident_count', 'severity']].fillna(0) - combined['strategy_type'] = combined['strategy_type'].apply(lambda x: x if isinstance(x, list) else []) - return combined - - -@st.cache_data(show_spinner=False) -def aggregate_daily_data_by_region(accident_data: pd.DataFrame, strategy_data: pd.DataFrame) -> pd.DataFrame: - """区域维度聚合。策略按天广播到所有区域（若策略本身无区域字段）。""" - df = accident_data.copy() - df['date'] = df['date_time'].dt.date - g = df.groupby(['region', 'date']).agg( - accident_count=('date_time', 'count'), - severity=('severity', 'sum') - ) - g.index = g.index.set_levels([g.index.levels[0], pd.to_datetime(g.index.levels[1])]) - g = g.sort_index() - - # 策略（每日列表） - s = strategy_data.copy() - s['date'] = s['date_time'].dt.date - daily_strategies = s.groupby('date')['strategy_type'].apply(list) - daily_strategies.index = pd.to_datetime(daily_strategies.index) - - # 广播 - regions = g.index.get_level_values(0).unique() - dates = pd.date_range(g.index.get_level_values(1).min(), g.index.get_level_values(1).max(), freq='D') - full_index = pd.MultiIndex.from_product([regions, dates], names=['region', 'date']) - g = g.reindex(full_index).fillna(0) - - strat_map = daily_strategies.to_dict() - g = g.assign(strategy_type=[strat_map.get(d, []) for d in g.index.get_level_values('date')]) - return g - - -from statsmodels.tsa.arima.model import ARIMA -from statsmodels.tools.sm_exceptions import ValueWarning -import warnings - -def evaluate_arima_model(series, arima_order): - """Fit ARIMA model and return AIC for evaluation.""" - try: - model = ARIMA(series, order=arima_order) - model_fit = model.fit() - return model_fit.aic - except Exception: - return float("inf") - -def arima_forecast_with_grid_search(accident_series: pd.Series, start_date: pd.Timestamp, - horizon: int = 30, p_values: list = range(0, 6), - d_values: list = range(0, 2), q_values: list = range(0, 6)) -> pd.DataFrame: - # Pre-process series - series = accident_series.asfreq('D').fillna(0) - start_date = pd.to_datetime(start_date) - - # Suppress warnings - warnings.filterwarnings("ignore", category=ValueWarning) - - # Define the hyperparameters to search through - best_score, best_cfg = float("inf"), None - - for p in p_values: - for d in d_values: - for q in q_values: - order = (p, d, q) - try: - aic = evaluate_arima_model(series, order) - if aic < best_score: - best_score, best_cfg = aic, order - except Exception as e: - continue - - # Fit the model with the best found order - print(best_cfg) - model = ARIMA(series, order=best_cfg) - fit = model.fit() - - # Forecasting - forecast_index = pd.date_range(start=start_date, periods=horizon, freq='D') - res = fit.get_forecast(steps=horizon) - df = res.summary_frame() - df.index = forecast_index - df.index.name = 'date' - df.rename(columns={'mean': 'forecast'}, inplace=True) - - return df - -# Example usage: -# dataframe = your_data_frame_here -# forecast_df = arima_forecast_with_grid_search(dataframe['accident_count'], start_date=pd.Timestamp('YYYY-MM-DD'), horizon=30) - - -def knn_forecast_counterfactual(accident_series: pd.Series, - intervention_date: pd.Timestamp, - lookback: int = 14, - horizon: int = 30): - series = accident_series.asfreq('D').fillna(0) - intervention_date = pd.to_datetime(intervention_date).normalize() - - df = pd.DataFrame({'y': series}) - for i in range(1, lookback + 1): - df[f'lag_{i}'] = df['y'].shift(i) - - train = df.loc[:intervention_date - pd.Timedelta(days=1)].dropna() - if len(train) < 5: - return None, None - X_train = train.filter(like='lag_').values - y_train = train['y'].values - knn = KNeighborsRegressor(n_neighbors=5) - knn.fit(X_train, y_train) - - history = df.loc[:intervention_date - pd.Timedelta(days=1), 'y'].tolist() - preds = [] - for _ in range(horizon): - if len(history) < lookback: - return None, None - x = np.array(history[-lookback:][::-1]).reshape(1, -1) - pred = knn.predict(x)[0] - preds.append(pred) - history.append(pred) - - pred_index = pd.date_range(intervention_date, periods=horizon, freq='D') - return pd.Series(preds, index=pred_index, name='knn_pred'), None - - -def detect_anomalies(series: pd.Series, contamination: float = 0.1): - series = series.asfreq('D').fillna(0) - iso = IsolationForest(contamination=contamination, random_state=42) - yhat = iso.fit_predict(series.values.reshape(-1, 1)) - anomaly_mask = (yhat == -1) - anomaly_indices = series.index[anomaly_mask] - - fig = go.Figure() - fig.add_trace(go.Scatter(x=series.index, y=series.values, mode='lines', name='Accident Count')) - fig.add_trace(go.Scatter(x=anomaly_indices, y=series.loc[anomaly_indices], mode='markers', - marker=dict(color='red', size=10), name='Anomalies')) - fig.update_layout(title="Anomaly Detection in Accident Count", - xaxis_title="Date", yaxis_title="Count") - return anomaly_indices, fig - - - - -def intervention_model(series: pd.Series, - intervention_date: pd.Timestamp, - intervention_type: str = 'persistent', - effect_type: str = 'sudden', - omega: float = 0.5, - decay: float = 10.0, - lag: int = 0): - series = series.asfreq('D').fillna(0) - intervention_date = pd.to_datetime(intervention_date) - Z_t = pd.Series(0.0, index=series.index) - if intervention_type == 'persistent': - Z_t.loc[intervention_date:] = 1.0 - else: - post_len = len(Z_t.loc[intervention_date:]) - Z_t.loc[intervention_date:] = np.exp(-np.arange(post_len) / decay) - if effect_type == 'gradual': - Z_t = Z_t * np.linspace(0, 1, len(Z_t)) - Z_t = Z_t.shift(lag).fillna(0) - Y_t = series + omega * Z_t - return Y_t, Z_t - - -def fit_and_extrapolate(series: pd.Series, - intervention_date: pd.Timestamp, - days: int = 30): - - series = series.asfreq('D').fillna(0) - # 统一为无时区、按天的时间戳 - series.index = pd.to_datetime(series.index).tz_localize(None).normalize() - intervention_date = pd.to_datetime(intervention_date).tz_localize(None).normalize() - - pre = series.loc[:intervention_date - pd.Timedelta(days=1)] - if len(pre) < 5: - return None, None, None - - x_pre = np.arange(len(pre)) - x_future = np.arange(len(pre), len(pre) + days) - - # 1️⃣ GLM：加入二次项 - X_pre_glm = sm.add_constant(np.column_stack([x_pre, x_pre**2])) - glm = sm.GLM(pre.values, X_pre_glm, family=sm.families.Poisson()) - glm_res = glm.fit() - X_future_glm = sm.add_constant(np.column_stack([x_future, x_future**2])) - glm_pred = glm_res.predict(X_future_glm) - - # SVR - # 2️⃣ SVR：加标准化 & 调参 / 改线性核 - from sklearn.preprocessing import StandardScaler - from sklearn.pipeline import make_pipeline - - svr = make_pipeline( - StandardScaler(), - SVR(kernel='rbf', C=10, gamma=0.1) # 或 kernel='linear' - ) - svr.fit(x_pre.reshape(-1, 1), pre.values) - svr_pred = svr.predict(x_future.reshape(-1, 1)) - - # 目标预测索引（未来可能超出历史范围 —— 用 reindex，不要 .loc[...]） - post_index = pd.date_range(intervention_date, periods=days, freq='D') - - glm_pred = pd.Series(glm_pred, index=post_index, name='glm_pred') - svr_pred = pd.Series(svr_pred, index=post_index, name='svr_pred') - - # ✅ 关键修复：对不存在的日期补 NaN，而不是 .loc[post_index] - post = series.reindex(post_index) - - residuals = pd.Series(post.values - svr_pred[:len(post)], - index=post_index, name='residual') - - return glm_pred, svr_pred, residuals - - -def evaluate_strategy_effectiveness(actual_series: pd.Series, - counterfactual_series: pd.Series, - severity_series: pd.Series, - strategy_date: pd.Timestamp, - window: int = 30): - strategy_date = pd.to_datetime(strategy_date) - pre_sev = severity_series.loc[strategy_date - pd.Timedelta(days=window):strategy_date - pd.Timedelta(days=1)].sum() - post_sev = severity_series.loc[strategy_date:strategy_date + pd.Timedelta(days=window - 1)].sum() - actual_post = actual_series.loc[strategy_date:strategy_date + pd.Timedelta(days=window - 1)] - counter_post = counterfactual_series.loc[strategy_date:strategy_date + pd.Timedelta(days=window - 1)] - counter_post = counter_post.reindex(actual_post.index) - effective_days = (actual_post < counter_post).sum() - count_effective = effective_days >= (window / 2) - severity_effective = post_sev < pre_sev - cf_sum = counter_post.sum() - F1 = (cf_sum - actual_post.sum()) / cf_sum if cf_sum > 0 else 0.0 - F2 = (pre_sev - post_sev) / pre_sev if pre_sev > 0 else 0.0 - if F1 > 0.5 and F2 > 0.5: - safety_state = '一级' - elif F1 > 0.3: - safety_state = '二级' - else: - safety_state = '三级' - return count_effective, severity_effective, (F1, F2), safety_state - - -def generate_output_and_recommendations(combined_data: pd.DataFrame, - strategy_types: list, - region: str = '全市', - horizon: int = 30): - results = {} - accident_series = combined_data['accident_count'] - severity_series = combined_data['severity'] - for strategy in strategy_types: - has_strategy = combined_data['strategy_type'].apply(lambda x: strategy in x) - if not has_strategy.any(): - continue - intervention_date = has_strategy[has_strategy].index[0] - glm_pred, svr_pred, residuals = fit_and_extrapolate(accident_series, intervention_date, days=horizon) - if svr_pred is None: - continue - count_eff, sev_eff, (F1, F2), state = evaluate_strategy_effectiveness( - actual_series=accident_series, - counterfactual_series=svr_pred, - severity_series=severity_series, - strategy_date=intervention_date, - window=horizon - ) - results[strategy] = { - 'effect_strength': float(residuals.mean()), - 'adaptability': float(F1 + F2), - 'count_effective': bool(count_eff), - 'severity_effective': bool(sev_eff), - 'safety_state': state, - 'F1': float(F1), - 'F2': float(F2), - 'intervention_date': str(intervention_date.date()) - } - best_strategy = max(results, key=lambda x: results[x]['adaptability']) if results else None - recommendation = f"建议在{region}区域长期实施策略类型 {best_strategy}" if best_strategy else "无足够数据推荐策略" - pd.DataFrame(results).T.to_csv('strategy_evaluation_results.csv', encoding='utf-8-sig') - with open('recommendation.txt', 'w', encoding='utf-8') as f: - f.write(recommendation) - return results, recommendation - - -# ======================= -# 3. UI Helpers -# ======================= -def hash_like(obj: str) -> str: - return hashlib.md5(obj.encode('utf-8')).hexdigest()[:8] - - -def compute_kpis(df_city: pd.DataFrame, arima_df: pd.DataFrame | None, - today: pd.Timestamp, window:int=30): - # 今日/昨日 - today_date = pd.to_datetime(today.date()) - yesterday = today_date - pd.Timedelta(days=1) - this_week_start = today_date - pd.Timedelta(days=today_date.weekday()) # 周一 - last_week_start = this_week_start - pd.Timedelta(days=7) - this_week_end = today_date - - today_cnt = int(df_city['accident_count'].get(today_date, 0)) - yest_cnt = int(df_city['accident_count'].get(yesterday, 0)) - wow = (today_cnt - yest_cnt) / yest_cnt if yest_cnt > 0 else 0.0 - - this_week = df_city.loc[this_week_start:this_week_end]['accident_count'].sum() - last_week = df_city.loc[last_week_start:last_week_start + pd.Timedelta(days=(this_week_end - this_week_start).days)]['accident_count'].sum() - yoy = (this_week - last_week) / last_week if last_week > 0 else 0.0 - - # 预测偏差（近7天） - forecast_bias = None - if arima_df is not None: - recent = df_city.index.max() - pd.Timedelta(days=6) - actual = df_city.loc[recent:df_city.index.max(), 'accident_count'] - fcst = arima_df['forecast'].reindex(actual.index).fillna(method='ffill') - denom = fcst.replace(0, np.nan) - bias = (np.abs(actual - fcst) / denom).dropna() - forecast_bias = float(bias.mean()) if len(bias) else None - - # 策略覆盖（近30天） - last_window = df_city.index.max() - pd.Timedelta(days=window-1) - strat_days = df_city.loc[last_window:, 'strategy_type'].apply(lambda x: len(x) > 0).sum() - coverage = strat_days / window - - # 上线策略数（去重） - active_strats = set(s for lst in df_city.loc[last_window:, 'strategy_type'] for s in lst) - active_count = len(active_strats) - - # 近30天安全等级（用 generate_output_and_recommendations 里 best 的等级） - # 这里只取最近出现过的策略做评估 - strategies = sorted(active_strats) - safety_state = '—' - if strategies: - res, _ = generate_output_and_recommendations(df_city.loc[last_window:], strategies, region='全市', horizon=min(30, len(df_city.loc[last_window:]))) - if res: - # 取适配度最高的策略的安全等级 - best = max(res, key=lambda k: res[k]['adaptability']) - safety_state = res[best]['safety_state'] - - return { - 'today_cnt': today_cnt, - 'wow': wow, - 'this_week': int(this_week), - 'yoy': yoy, - 'forecast_bias': forecast_bias, - 'active_count': active_count, - 'coverage': coverage, - 'safety_state': safety_state - } - - -def significance_test(pre: pd.Series, post: pd.Series): - pre = pre.dropna(); post = post.dropna() - if len(pre) < 3 or len(post) < 3: - return None, None - if HAS_SCIPY: - try: - stat, p = ttest_ind(pre, post, equal_var=False) - except Exception: - stat, p = mannwhitneyu(pre, post, alternative='two-sided') - return float(stat), float(p) - return None, None - - -def save_fig_as_html(fig, filename): - html = fig.to_html(full_html=True, include_plotlyjs='cdn') - with open(filename, 'w', encoding='utf-8') as f: - f.write(html) - return filename - -import numpy as np -import pandas as pd -from sklearn.metrics import mean_absolute_error, mean_squared_error -from statsmodels.tsa.arima.model import ARIMA - -# 依赖：已在脚本前面定义的 knn_forecast_counterfactual() 和 fit_and_extrapolate() -import numpy as np -import pandas as pd -from sklearn.metrics import mean_absolute_error, mean_squared_error -from statsmodels.tsa.arima.model import ARIMA - -# 依赖：knn_forecast_counterfactual、fit_and_extrapolate 已存在 - -def evaluate_models(series: pd.Series, - horizon: int = 30, - lookback: int = 14, - p_values: range = range(0, 6), - d_values: range = range(0, 2), - q_values: range = range(0, 6)) -> pd.DataFrame: - """ - 留出法（最后 horizon 天作为验证集）比较 ARIMA / KNN / GLM / SVR， - 输出 MAE・RMSE・MAPE，并按 RMSE 升序排序。 - """ - # 统一日频 & 缺失补零 - series = series.asfreq('D').fillna(0) - if len(series) <= horizon + 10: - raise ValueError("序列太短，无法留出 %d 天进行评估。" % horizon) - - train, test = series.iloc[:-horizon], series.iloc[-horizon:] - - def _to_series_like(pred, a_index): - # 将任意预测对齐成与 actual 同索引的 Series - if isinstance(pred, pd.Series): - return pred.reindex(a_index) - return pd.Series(pred, index=a_index) - - def _metrics(a: pd.Series, p) -> dict: - p = _to_series_like(p, a.index).astype(float) - a = a.astype(float) - - mae = mean_absolute_error(a, p) - - # 兼容旧版 sklearn：没有 squared 参数时手动开方 - try: - rmse = mean_squared_error(a, p, squared=False) - except TypeError: - rmse = mean_squared_error(a, p) ** 0.5 - - # 忽略分母为 0 的样本 - mape = np.nanmean(np.abs((a - p) / np.where(a == 0, np.nan, a))) * 100 - return {"MAE": mae, "RMSE": rmse, "MAPE": mape} - - results = {} - - # ---------- ARIMA ---------- - best_aic, best_order = float('inf'), None - for p in p_values: - for d in d_values: - for q in q_values: - try: - aic = ARIMA(train, order=(p, d, q)).fit().aic - if aic < best_aic: - best_aic, best_order = aic, (p, d, q) - except Exception: - continue - arima_train = train.asfreq('D').fillna(0) - arima_pred = ARIMA(arima_train, order=best_order).fit().forecast(steps=horizon) - results['ARIMA'] = _metrics(test, arima_pred) - - # ---------- KNN ---------- - try: - knn_pred, _ = knn_forecast_counterfactual(series, - train.index[-1] + pd.Timedelta(days=1), - lookback=lookback, - horizon=horizon) - if knn_pred is not None: - results['KNN'] = _metrics(test, knn_pred) - except Exception: - pass - - # ---------- GLM & SVR ---------- - try: - glm_pred, svr_pred, _ = fit_and_extrapolate(series, - train.index[-1] + pd.Timedelta(days=1), - days=horizon) - if glm_pred is not None: - results['GLM'] = _metrics(test, glm_pred) - if svr_pred is not None: - results['SVR'] = _metrics(test, svr_pred) - except Exception: - pass - - return (pd.DataFrame(results) - .T.sort_values('RMSE') - .round(3)) - - -import re -from collections import Counter -import jieba - -def parse_and_standardize_locations(accident_data): - """解析和标准化事故地点""" - df = accident_data.copy() - - # 提取关键路段信息 - def extract_road_info(location): - if pd.isna(location): - return "未知路段" - - location = str(location) - - # 常见路段关键词 - road_keywords = ['路', '道', '街', '巷', '路口', '交叉口', '大道', '公路'] - area_keywords = ['新城', '临城', '千岛', '翁山', '海天', '海宇', '定沈', '滨海', '港岛', '体育', '长升', '金岛', '桃湾'] - - # 提取包含关键词的路段 - for keyword in road_keywords + area_keywords: - if keyword in location: - # 提取以该关键词为中心的路段名称 - pattern = f'[^，。]*{keyword}[^，。]*' - matches = re.findall(pattern, location) - if matches: - return matches[0].strip() - - return location - - df['standardized_location'] = df['事故具体地点'].apply(extract_road_info) - - # 进一步清理和标准化 - location_mapping = { - '新城千岛路': '千岛路', - '千岛路海天大道': '千岛路海天大道口', - '海天大道千岛路': '千岛路海天大道口', - '新城翁山路': '翁山路', - '翁山路金岛路': '翁山路金岛路口', - # 添加更多标准化映射... - } - - df['standardized_location'] = df['standardized_location'].replace(location_mapping) - - return df - -def analyze_location_frequency(accident_data, time_window='7D'): - """分析地点事故频次""" - df = parse_and_standardize_locations(accident_data) - - # 计算时间窗口 - recent_cutoff = df['事故时间'].max() - pd.Timedelta(time_window) - - # 总体统计 - overall_stats = df.groupby('standardized_location').agg({ - '事故时间': ['count', 'max'], # 事故总数和最近时间 - '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损', - '道路类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '城区道路', - '路口路段类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '普通路段' - }) - - # 扁平化列名 - overall_stats.columns = ['accident_count', 'last_accident', 'main_accident_type', 'main_road_type', 'main_intersection_type'] - - # 近期统计 - recent_accidents = df[df['事故时间'] >= recent_cutoff] - recent_stats = recent_accidents.groupby('standardized_location').agg({ - '事故时间': 'count', - '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损' - }).rename(columns={'事故时间': 'recent_count', '事故类型': 'recent_accident_type'}) - - # 合并数据 - result = overall_stats.merge(recent_stats, left_index=True, right_index=True, how='left').fillna(0) - result['recent_count'] = result['recent_count'].astype(int) - - # 计算趋势指标 - result['trend_ratio'] = result['recent_count'] / result['accident_count'] - result['days_since_last'] = (df['事故时间'].max() - result['last_accident']).dt.days - - return result.sort_values(['recent_count', 'accident_count'], ascending=False) - - -def generate_intelligent_strategies(hotspot_df, time_period='本周'): - """生成智能针对性策略""" - strategies = [] - - for location_name, location_data in hotspot_df.iterrows(): - accident_count = location_data['accident_count'] - recent_count = location_data['recent_count'] - accident_type = location_data['main_accident_type'] - road_type = location_data['main_road_type'] - intersection_type = location_data['main_intersection_type'] - trend_ratio = location_data['trend_ratio'] - - # 基础信息 - base_info = f"{time_period}对【{location_name}】" - data_support = f"（近期{int(recent_count)}起，累计{int(accident_count)}起，{accident_type}为主）" - - # 智能策略生成 - strategy_parts = [] - - # 基于事故类型 - if accident_type == '财损': - strategy_parts.append("加强违法查处") - if '信号灯' in intersection_type: - strategy_parts.append("整治闯红灯、不按规定让行") - else: - strategy_parts.append("整治违法变道、超速行驶") - elif accident_type == '伤人': - strategy_parts.append("优化交通组织") - strategy_parts.append("增设安全设施") - if recent_count >= 2: - strategy_parts.append("开展专项整治") - - # 基于路口类型 - if intersection_type == '信号灯路口': - strategy_parts.append("优化信号配时") - elif intersection_type == '非信号灯路口': - strategy_parts.append("完善让行标志") - elif intersection_type == '普通路段': - if trend_ratio > 0.3: # 近期事故占比高 - strategy_parts.append("加强巡逻管控") - - # 基于趋势 - if trend_ratio > 0.5: - strategy_parts.append("列为重点管控路段") - if location_data['days_since_last'] <= 3: - strategy_parts.append("近期需重点关注") - - # 组合策略 - if strategy_parts: - strategy = base_info + "，" + "，".join(strategy_parts) + data_support - else: - strategy = base_info + "分析事故成因，制定综合整治方案" + data_support - - strategies.append(strategy) - - return strategies - -def calculate_location_risk_score(hotspot_df): - """计算路口风险评分""" - df = hotspot_df.copy() - - # 事故频次得分 (0-40分) - df['frequency_score'] = (df['accident_count'] / df['accident_count'].max() * 40).clip(0, 40) - - # 近期趋势得分 (0-30分) - df['trend_score'] = (df['trend_ratio'] * 30).clip(0, 30) - - # 事故严重度得分 (0-20分) - severity_map = {'财损': 5, '伤人': 15, '亡人': 20} - df['severity_score'] = df['main_accident_type'].map(severity_map).fillna(5) - - # 时间紧迫度得分 (0-10分) - df['urgency_score'] = ((30 - df['days_since_last']) / 30 * 10).clip(0, 10) - - # 总分 - df['risk_score'] = df['frequency_score'] + df['trend_score'] + df['severity_score'] + df['urgency_score'] - - # 风险等级 - conditions = [ - df['risk_score'] >= 70, - df['risk_score'] >= 50, - df['risk_score'] >= 30 - ] - choices = ['高风险', '中风险', '低风险'] - df['risk_level'] = np.select(conditions, choices, default='一般风险') - - return df.sort_values('risk_score', ascending=False) - - -# ======================= -# 4. App -# ======================= -def run_streamlit_app(): - st.set_page_config(page_title="Traffic Safety Analysis", layout="wide") - st.title("🚦 Traffic Safety Intervention Analysis System") - - # Sidebar — Upload & Global Filters & Auto Refresh - st.sidebar.header("数据与筛选") - - # Create a form for data inputs to batch updates - with st.sidebar.form(key="data_input_form"): - accident_file = st.file_uploader("上传事故数据 (Excel)", type=['xlsx']) - strategy_file = st.file_uploader("上传交通策略数据 (Excel)", type=['xlsx']) - - # Global filters - st.markdown("---") - st.subheader("全局筛选器") - # Placeholder for region selection (will be populated after data is loaded) - region_sel = st.selectbox("区域", options=["全市"], index=0, key="region_select") - # Default date range (will be updated after data is loaded) - min_date = pd.to_datetime('2022-01-01').date() - max_date = pd.to_datetime('2022-12-31').date() - date_range = st.date_input("时间范围", value=(min_date, max_date), min_value=min_date, max_value=max_date) - strat_filter = st.multiselect("策略类型（过滤）", options=[], help="为空表示不过滤策略；选择后仅保留当天包含所选策略的日期") - - # Apply button for data loading and filtering - apply_button = st.form_submit_button("应用数据与筛选") - - # Auto-refresh controls (outside the form, as it’s independent) - st.sidebar.markdown("---") - st.sidebar.subheader("实时刷新") - auto = st.sidebar.checkbox("自动刷新", value=False, help="启用后将按间隔自动刷新页面") - interval = st.sidebar.number_input("刷新间隔（秒）", min_value=5, max_value=600, value=30, step=5) - if auto and HAS_AUTOREFRESH: - st_autorefresh(interval=int(interval*1000), key="autorefresh") - elif auto and not HAS_AUTOREFRESH: - st.sidebar.info("未安装 `streamlit-autorefresh`，请使用上方“重新运行”按钮或关闭再开启此开关。") - - # Add OpenAI API key input in sidebar - st.sidebar.markdown("---") - st.sidebar.subheader("GPT API 配置") - openai_api_key = st.sidebar.text_input("GPT API Key", value='sk-dQhKOOG48iVEfgJfAb14458dA4474fB09aBbE8153d4aB3Fc', type="password", help="用于GPT分析结果的API密钥") - open_ai_base_url = st.sidebar.text_input("GPT Base Url", value='https://az.gptplus5.com/v1', type='default') - - # Initialize session state to store processed data - if 'processed_data' not in st.session_state: - st.session_state['processed_data'] = { - 'combined_city': None, - 'combined_by_region': None, - 'accident_data': None, - 'strategy_data': None, - 'all_regions': ["全市"], - 'all_strategy_types': [], - 'min_date': min_date, - 'max_date': max_date, - 'region_sel': "全市", - 'date_range': (min_date, max_date), - 'strat_filter': [] - } - - # Process data only when Apply button is clicked - if apply_button and accident_file and strategy_file: - with st.spinner("数据载入中…"): - # Load and clean data - accident_data, strategy_data = load_and_clean_data(accident_file, strategy_file) - combined_city = aggregate_daily_data(accident_data, strategy_data) - combined_by_region = aggregate_daily_data_by_region(accident_data, strategy_data) - - # Update available options for filters - all_regions = ["全市"] + sorted(accident_data['region'].unique().tolist()) - all_strategy_types = sorted({s for lst in combined_city['strategy_type'] for s in lst}) - min_date = combined_city.index.min().date() - max_date = combined_city.index.max().date() - - # Store processed data in session state - st.session_state['processed_data'].update({ - 'combined_city': combined_city, - 'combined_by_region': combined_by_region, - 'accident_data': accident_data, - 'strategy_data': strategy_data, - 'all_regions': all_regions, - 'all_strategy_types': all_strategy_types, - 'min_date': min_date, - 'max_date': max_date, - 'region_sel': region_sel, - 'date_range': date_range, - 'strat_filter': strat_filter - }) - - # Retrieve data from session state - combined_city = st.session_state['processed_data']['combined_city'] - combined_by_region = st.session_state['processed_data']['combined_by_region'] - accident_data = st.session_state['processed_data']['accident_data'] - strategy_data = st.session_state['processed_data']['strategy_data'] - all_regions = st.session_state['processed_data']['all_regions'] - all_strategy_types = st.session_state['processed_data']['all_strategy_types'] - min_date = st.session_state['processed_data']['min_date'] - max_date = st.session_state['processed_data']['max_date'] - region_sel = st.session_state['processed_data']['region_sel'] - date_range = st.session_state['processed_data']['date_range'] - strat_filter = st.session_state['processed_data']['strat_filter'] - - # Update selectbox and multiselect options dynamically (outside the form for display) - st.sidebar.markdown("---") - st.sidebar.subheader("当前筛选状态") - st.sidebar.write(f"区域: {region_sel}") - st.sidebar.write(f"时间范围: {date_range[0]} 至 {date_range[1]}") - st.sidebar.write(f"策略类型: {', '.join(strat_filter) or '无'}") - - # Proceed only if data is available - if combined_city is not None and combined_by_region is not None: - start_dt = pd.to_datetime(date_range[0]) - end_dt = pd.to_datetime(date_range[1]) - if region_sel == "全市": - base = combined_city.loc[start_dt:end_dt].copy() - else: - block = combined_by_region.xs(region_sel, level='region').copy() - base = block.loc[start_dt:end_dt] - if strat_filter: - mask = base['strategy_type'].apply(lambda x: any(s in x for s in strat_filter)) - base = base[mask] - - # Last refresh info - if 'last_refresh' not in st.session_state: - st.session_state['last_refresh'] = datetime.now() - last_refresh = st.session_state['last_refresh'] - - # Compute ARIMA for KPI bias - arima_df = None - try: - arima_df = arima_forecast_with_grid_search( - base['accident_count'], base.index.max() + pd.Timedelta(days=1), horizon=7 - ) - except Exception: - pass - - # KPI Overview - kpi = compute_kpis(base, arima_df, today=pd.Timestamp('2022-12-01')) - c1, c2, c3, c4, c5, c6 = st.columns(6) - c1.metric("今日事故数", f"{kpi['today_cnt']}", f"{kpi['wow']*100:.1f}% 环比") - c2.metric("本周事故数", f"{kpi['this_week']}", f"{kpi['yoy']*100:.1f}% 同比") - c3.metric("近7天预测偏差", ("{:.1f}%".format(kpi['forecast_bias']*100) if kpi['forecast_bias'] is not None else "—")) - c4.metric("近30天策略数", f"{kpi['active_count']}") - c5.metric("近30天策略覆盖率", f"{kpi['coverage']*100:.1f}%") - c6.metric("近30天安全等级", kpi['safety_state']) - - # Top-right meta - meta_col1, meta_col2 = st.columns([4, 1]) - with meta_col2: - st.caption(f"🕒 最近刷新：{last_refresh.strftime('%Y-%m-%d %H:%M:%S')}") - - # Tabs (add new tab for GPT analysis) - tab_dash, tab_pred, tab_eval, tab_anom, tab_strat, tab_comp, tab_sim, tab_gpt, tab_hotspot = st.tabs( - ["🏠 总览", "📈 预测模型", "📊 模型评估", "⚠️ 异常检测", "📝 策略评估", "⚖️ 策略对比", "🧪 情景模拟", "🔍 GPT 分析", "📍 事故热点"] - ) - - - with tab_hotspot: - st.header("📍 事故多发路口分析") - st.markdown("独立分析事故数据，识别高风险路口并生成针对性策略") - - # 独立文件上传 - st.subheader("📁 数据上传") - hotspot_file = st.file_uploader("上传事故数据文件", type=['xlsx'], key="hotspot_uploader") - - if hotspot_file is not None: - try: - # 加载数据 - @st.cache_data(show_spinner=False) - def load_hotspot_data(uploaded_file): - """独立加载事故热点分析数据""" - df = pd.read_excel(uploaded_file, sheet_name=None) - accident_data = pd.concat(df.values(), ignore_index=True) - - # 数据清洗和预处理 - accident_data['事故时间'] = pd.to_datetime(accident_data['事故时间']) - accident_data = accident_data.dropna(subset=['事故时间', '所在街道', '事故类型', '事故具体地点']) - - # 添加严重度评分 - severity_map = {'财损': 1, '伤人': 2, '亡人': 4} - accident_data['severity'] = accident_data['事故类型'].map(severity_map).fillna(1) - - return accident_data - - with st.spinner("正在加载数据..."): - accident_data = load_hotspot_data(hotspot_file) - - # 显示数据概览 - st.success(f"✅ 成功加载数据：{len(accident_data)} 条事故记录") - - col1, col2, col3 = st.columns(3) - with col1: - st.metric("数据时间范围", - f"{accident_data['事故时间'].min().strftime('%Y-%m-%d')} 至 {accident_data['事故时间'].max().strftime('%Y-%m-%d')}") - with col2: - st.metric("事故类型分布", - f"财损: {len(accident_data[accident_data['事故类型']=='财损'])}起") - with col3: - st.metric("涉及区域", - f"{accident_data['所在街道'].nunique()}个街道") - - # 地点标准化函数（独立版本） - def standardize_hotspot_locations(df): - """标准化事故地点""" - df = df.copy() - - def extract_road_info(location): - if pd.isna(location): - return "未知路段" - - location = str(location) - - # 常见路段关键词 - road_keywords = ['路', '道', '街', '巷', '路口', '交叉口', '大道', '公路', '口'] - area_keywords = ['新城', '临城', '千岛', '翁山', '海天', '海宇', '定沈', '滨海', '港岛', '体育', '长升', '金岛', '桃湾'] - - # 提取包含关键词的路段 - for keyword in road_keywords + area_keywords: - if keyword in location: - # 简化地点名称 - words = location.split() - for word in words: - if keyword in word: - return word - return location - - # 如果没找到关键词，返回原地点（截断过长的） - return location[:20] if len(location) > 20 else location - - df['standardized_location'] = df['事故具体地点'].apply(extract_road_info) - - # 手动标准化映射（根据实际数据调整） - location_mapping = { - '新城千岛路': '千岛路', - '千岛路海天大道': '千岛路海天大道口', - '海天大道千岛路': '千岛路海天大道口', - '新城翁山路': '翁山路', - '翁山路金岛路': '翁山路金岛路口', - '海天大道临长路': '海天大道临长路口', - '定沈路卫生医院门口': '定沈路医院段', - '翁山路海城路西口': '翁山路海城路口', - '海宇道路口': '海宇道', - '海天大道路口': '海天大道', - '定沈路交叉路口': '定沈路', - '千岛路路口': '千岛路', - '体育路路口': '体育路', - '金岛路路口': '金岛路', - } - - df['standardized_location'] = df['standardized_location'].replace(location_mapping) - - return df - - # 热点分析函数 - def analyze_hotspot_frequency(df, time_window='7D'): - """分析地点事故频次""" - df = standardize_hotspot_locations(df) - - # 计算时间窗口 - recent_cutoff = df['事故时间'].max() - pd.Timedelta(time_window) - - # 总体统计 - overall_stats = df.groupby('standardized_location').agg({ - '事故时间': ['count', 'max'], - '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损', - '道路类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '城区道路', - '路口路段类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '普通路段', - 'severity': 'sum' - }) - - # 扁平化列名 - overall_stats.columns = ['accident_count', 'last_accident', 'main_accident_type', - 'main_road_type', 'main_intersection_type', 'total_severity'] - - # 近期统计 - recent_accidents = df[df['事故时间'] >= recent_cutoff] - recent_stats = recent_accidents.groupby('standardized_location').agg({ - '事故时间': 'count', - '事故类型': lambda x: x.mode()[0] if len(x.mode()) > 0 else '财损', - 'severity': 'sum' - }).rename(columns={'事故时间': 'recent_count', '事故类型': 'recent_accident_type', 'severity': 'recent_severity'}) - - # 合并数据 - result = overall_stats.merge(recent_stats, left_index=True, right_index=True, how='left').fillna(0) - result['recent_count'] = result['recent_count'].astype(int) - - # 计算趋势指标 - result['trend_ratio'] = result['recent_count'] / result['accident_count'] - result['days_since_last'] = (df['事故时间'].max() - result['last_accident']).dt.days - result['avg_severity'] = result['total_severity'] / result['accident_count'] - - return result.sort_values(['recent_count', 'accident_count'], ascending=False) - - # 风险评分函数 - def calculate_hotspot_risk_score(hotspot_df): - """计算路口风险评分""" - df = hotspot_df.copy() - - # 事故频次得分 (0-40分) - df['frequency_score'] = (df['accident_count'] / df['accident_count'].max() * 40).clip(0, 40) - - # 近期趋势得分 (0-30分) - df['trend_score'] = (df['trend_ratio'] * 30).clip(0, 30) - - # 事故严重度得分 (0-20分) - severity_map = {'财损': 5, '伤人': 15, '亡人': 20} - df['severity_score'] = df['main_accident_type'].map(severity_map).fillna(5) - - # 时间紧迫度得分 (0-10分) - df['urgency_score'] = ((30 - df['days_since_last']) / 30 * 10).clip(0, 10) - - # 总分 - df['risk_score'] = df['frequency_score'] + df['trend_score'] + df['severity_score'] + df['urgency_score'] - - # 风险等级 - conditions = [ - df['risk_score'] >= 70, - df['risk_score'] >= 50, - df['risk_score'] >= 30 - ] - choices = ['高风险', '中风险', '低风险'] - df['risk_level'] = np.select(conditions, choices, default='一般风险') - - return df.sort_values('risk_score', ascending=False) - - # 策略生成函数 - def generate_hotspot_strategies(hotspot_df, time_period='本周'): - """生成热点针对性策略""" - strategies = [] - - for location_name, location_data in hotspot_df.iterrows(): - accident_count = location_data['accident_count'] - recent_count = location_data['recent_count'] - accident_type = location_data['main_accident_type'] - intersection_type = location_data['main_intersection_type'] - trend_ratio = location_data['trend_ratio'] - risk_level = location_data['risk_level'] - - # 基础信息 - base_info = f"{time_period}对【{location_name}】" - data_support = f"（近期{int(recent_count)}起，累计{int(accident_count)}起，{accident_type}为主）" - - # 智能策略生成 - strategy_parts = [] - - # 基于路口类型和事故类型 - if '信号灯' in str(intersection_type): - if accident_type == '财损': - strategy_parts.extend(["加强闯红灯查处", "优化信号配时", "整治不按规定让行"]) - else: - strategy_parts.extend(["完善人行过街设施", "加强非机动车管理", "设置警示标志"]) - elif '普通路段' in str(intersection_type): - strategy_parts.extend(["加强巡逻管控", "整治违法停车", "设置限速标志"]) - else: - strategy_parts.extend(["分析事故成因", "制定综合整治方案"]) - - # 基于风险等级 - if risk_level == '高风险': - strategy_parts.append("列为重点整治路段") - strategy_parts.append("开展专项整治行动") - elif risk_level == '中风险': - strategy_parts.append("加强日常监管") - - # 基于趋势 - if trend_ratio > 0.4: - strategy_parts.append("近期重点监控") - - # 组合策略 - if strategy_parts: - strategy = base_info + "，" + "，".join(strategy_parts) + data_support - else: - strategy = base_info + "加强交通安全管理" + data_support - - strategies.append({ - 'location': location_name, - 'strategy': strategy, - 'risk_level': risk_level, - 'accident_count': accident_count, - 'recent_count': recent_count - }) - - return strategies - - # 分析参数设置 - st.subheader("🔧 分析参数设置") - col1, col2, col3 = st.columns(3) - with col1: - time_window = st.selectbox("统计时间窗口", ['7D', '15D', '30D'], index=0, key="hotspot_window") - with col2: - min_accidents = st.number_input("最小事故数", 1, 50, 3, key="hotspot_min_accidents") - with col3: - top_n = st.slider("显示热点数量", 3, 20, 8, key="hotspot_top_n") - - if st.button("🚀 开始热点分析", type="primary"): - with st.spinner("正在分析事故热点分布..."): - # 执行热点分析 - hotspots = analyze_hotspot_frequency(accident_data, time_window=time_window) - - # 过滤最小事故数 - hotspots = hotspots[hotspots['accident_count'] >= min_accidents] - - if len(hotspots) > 0: - # 计算风险评分 - hotspots_with_risk = calculate_hotspot_risk_score(hotspots.head(top_n * 3)) - top_hotspots = hotspots_with_risk.head(top_n) - - # 显示热点排名 - st.subheader(f"📊 事故多发路口排名（前{top_n}个）") - - display_df = top_hotspots[[ - 'accident_count', 'recent_count', 'trend_ratio', - 'main_accident_type', 'main_intersection_type', 'risk_score', 'risk_level' - ]].rename(columns={ - 'accident_count': '累计事故', - 'recent_count': '近期事故', - 'trend_ratio': '趋势比例', - 'main_accident_type': '主要类型', - 'main_intersection_type': '路口类型', - 'risk_score': '风险评分', - 'risk_level': '风险等级' - }) - - # 格式化显示 - styled_df = display_df.style.format({ - '趋势比例': '{:.2f}', - '风险评分': '{:.1f}' - }).background_gradient(subset=['风险评分'], cmap='Reds') - - st.dataframe(styled_df, use_container_width=True) - - # 生成策略建议 - strategies = generate_hotspot_strategies(top_hotspots, time_period='本周') - - st.subheader("🎯 针对性策略建议") - - for i, strategy_info in enumerate(strategies, 1): - strategy = strategy_info['strategy'] - risk_level = strategy_info['risk_level'] - - # 根据风险等级显示不同颜色 - if risk_level == '高风险': - st.error(f"🚨 **{i}. {strategy}**") - elif risk_level == '中风险': - st.warning(f"⚠️ **{i}. {strategy}**") - else: - st.info(f"✅ **{i}. {strategy}**") - - # 可视化分析 - st.subheader("📈 数据分析可视化") - - col1, col2 = st.columns(2) - - with col1: - # 事故频次分布图 - fig1 = px.bar( - top_hotspots.head(10), - x=top_hotspots.head(10).index, - y=['accident_count', 'recent_count'], - title="事故频次TOP10分布", - labels={'value': '事故数量', 'variable': '类型', 'index': '路口名称'}, - barmode='group' - ) - fig1.update_layout(xaxis_tickangle=-45) - st.plotly_chart(fig1, use_container_width=True) - - with col2: - # 风险等级分布 - risk_dist = top_hotspots['risk_level'].value_counts() - fig2 = px.pie( - values=risk_dist.values, - names=risk_dist.index, - title="风险等级分布", - color_discrete_map={'高风险': 'red', '中风险': 'orange', '低风险': 'green'} - ) - st.plotly_chart(fig2, use_container_width=True) - - # 详细数据下载 - st.subheader("💾 数据导出") - - col_dl1, col_dl2 = st.columns(2) - - with col_dl1: - # 下载热点数据 - hotspot_csv = top_hotspots.to_csv().encode('utf-8-sig') - st.download_button( - "📥 下载热点数据CSV", - data=hotspot_csv, - file_name=f"accident_hotspots_{datetime.now().strftime('%Y%m%d')}.csv", - mime="text/csv" - ) - - with col_dl2: - # 下载策略报告 - report_data = { - "analysis_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - "time_window": time_window, - "data_source": hotspot_file.name, - "total_records": len(accident_data), - "analysis_parameters": { - "min_accidents": min_accidents, - "top_n": top_n - }, - "top_hotspots": top_hotspots.to_dict('records'), - "recommended_strategies": strategies, - "summary": { - "high_risk_count": len(top_hotspots[top_hotspots['risk_level'] == '高风险']), - "medium_risk_count": len(top_hotspots[top_hotspots['risk_level'] == '中风险']), - "total_analyzed_locations": len(hotspots), - "most_dangerous_location": top_hotspots.index[0] if len(top_hotspots) > 0 else "无" - } - } - - st.download_button( - "📄 下载完整分析报告", - data=json.dumps(report_data, ensure_ascii=False, indent=2), - file_name=f"hotspot_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M')}.json", - mime="application/json" - ) - - else: - st.warning("⚠️ 未找到符合条件的事故热点数据，请调整筛选参数") - - # 显示原始数据预览（可选） - with st.expander("📋 查看原始数据预览"): - st.dataframe(accident_data[['事故时间', '所在街道', '事故类型', '事故具体地点', '道路类型']].head(10), - use_container_width=True) - - except Exception as e: - st.error(f"❌ 数据加载失败：{str(e)}") - st.info("请检查文件格式是否正确，确保包含'事故时间'、'事故类型'、'事故具体地点'等必要字段") - - else: - st.info("👆 请上传事故数据Excel文件开始分析") - st.markdown(""" - ### 📝 支持的数据格式要求： - - **文件格式**: Excel (.xlsx) - - **必要字段**: - - `事故时间`: 事故发生时的时间 - - `事故类型`: 财损/伤人/亡人 - - `事故具体地点`: 详细的事故发生地点 - - `所在街道`: 事故发生的街道区域 - - `道路类型`: 城区道路/其他等 - - `路口路段类型`: 信号灯路口/普通路段等 - """) - # --- Tab 1: 总览页 - with tab_dash: - fig_line = go.Figure() - fig_line.add_trace(go.Scatter(x=base.index, y=base['accident_count'], name='事故数', mode='lines')) - fig_line.update_layout(title="事故数（过滤后）", xaxis_title="Date", yaxis_title="Count") - st.plotly_chart(fig_line, use_container_width=True) - fname = save_fig_as_html(fig_line, "overview_series.html") - st.download_button("下载图表 HTML", data=open(fname, 'rb').read(), - file_name="overview_series.html", mime="text/html") - - st.dataframe(base, use_container_width=True) - csv_bytes = base.to_csv(index=True).encode('utf-8-sig') - st.download_button("下载当前视图 CSV", data=csv_bytes, file_name="filtered_view.csv", mime="text/csv") - - meta = { - "region": region_sel, - "date_range": [str(start_dt.date()), str(end_dt.date())], - "strategy_filter": strat_filter, - "rows": int(len(base)), - "min_date": str(base.index.min().date()) if len(base) else None, - "max_date": str(base.index.max().date()) if len(base) else None - } - with open("run_metadata.json", "w", encoding="utf-8") as f: - json.dump(meta, f, ensure_ascii=False, indent=2) - st.download_button("下载运行参数 JSON", data=open("run_metadata.json", "rb").read(), - file_name="run_metadata.json", mime="application/json") - - # --- Tab 2: 预测模型 - with tab_pred: - st.subheader("多模型预测比较") - # 使用表单封装交互组件 - with st.form(key="predict_form"): - default_date = base.index.max() - pd.Timedelta(days=60) if len(base) else pd.Timestamp('2022-01-01') - selected_date = st.date_input("选择干预日期 / 预测起点", value=default_date) - horizon = st.number_input("预测天数", min_value=7, max_value=90, value=30, step=1) - submit_predict = st.form_submit_button("应用预测参数") - - if submit_predict and len(base.loc[:pd.to_datetime(selected_date)]) >= 10: - first_date = pd.to_datetime(selected_date) - try: - train_series = base['accident_count'].loc[:first_date] - arima30 = arima_forecast_with_grid_search( - train_series, - start_date=first_date + pd.Timedelta(days=1), - horizon=horizon - ) - except Exception as e: - st.warning(f"ARIMA 运行失败：{e}") - arima30 = None - - knn_pred, _ = knn_forecast_counterfactual(base['accident_count'], - first_date, - horizon=horizon) - glm_pred, svr_pred, residuals = fit_and_extrapolate(base['accident_count'], - first_date, - days=horizon) - - fig_pred = go.Figure() - fig_pred.add_trace(go.Scatter(x=base.index, y=base['accident_count'], - name="实际", mode="lines")) - if arima30 is not None: - fig_pred.add_trace(go.Scatter(x=arima30.index, y=arima30['forecast'], - name="ARIMA", mode="lines")) - if knn_pred is not None: - fig_pred.add_trace(go.Scatter(x=knn_pred.index, y=knn_pred, - name="KNN", mode="lines")) - if glm_pred is not None: - fig_pred.add_trace(go.Scatter(x=glm_pred.index, y=glm_pred, - name="GLM", mode="lines")) - if svr_pred is not None: - fig_pred.add_trace(go.Scatter(x=svr_pred.index, y=svr_pred, - name="SVR", mode="lines")) - - fig_pred.update_layout( - title=f"多模型预测比较（起点：{first_date.date()}，预测 {horizon} 天）", - xaxis_title="日期", yaxis_title="事故数" - ) - st.plotly_chart(fig_pred, use_container_width=True) - - col_dl1, col_dl2 = st.columns(2) - if arima30 is not None: - col_dl1.download_button("下载 ARIMA 预测 CSV", - data=arima30.to_csv().encode("utf-8-sig"), - file_name="arima_forecast.csv", - mime="text/csv") - elif submit_predict: - st.info("⚠️ 干预前数据较少，可能影响拟合质量。") - else: - st.info("请设置预测参数并点击“应用预测参数”按钮。") - - # --- Tab 3: 模型评估 - with tab_eval: - st.subheader("模型预测效果对比") - with st.form(key="model_eval_form"): - horizon_sel = st.slider("评估窗口（天）", 7, 60, 30, step=1) - submit_eval = st.form_submit_button("应用评估参数") - - if submit_eval: - try: - df_metrics = evaluate_models(base['accident_count'], horizon=horizon_sel) - st.dataframe(df_metrics, use_container_width=True) - best_model = df_metrics['RMSE'].idxmin() - st.success(f"过去 {horizon_sel} 天中，RMSE 最低的模型是：**{best_model}**") - st.download_button( - "下载评估结果 CSV", - data=df_metrics.to_csv().encode('utf-8-sig'), - file_name="model_evaluation.csv", - mime="text/csv" - ) - except ValueError as err: - st.warning(str(err)) - else: - st.info("请设置评估窗口并点击“应用评估参数”按钮。") - - # --- Tab 4: 异常检测 - with tab_anom: - anomalies, anomaly_fig = detect_anomalies(base['accident_count']) - st.plotly_chart(anomaly_fig, use_container_width=True) - st.write(f"检测到异常点：{len(anomalies)} 个") - st.download_button("下载异常日期 CSV", - data=anomalies.to_series().to_csv(index=False).encode('utf-8-sig'), - file_name="anomalies.csv", mime="text/csv") - - # --- Tab 5: 策略评估 - with tab_strat: - st.info(f"📌 检测到的策略类型：{', '.join(all_strategy_types) or '（数据中没有策略）'}") - if all_strategy_types: - results, recommendation = generate_output_and_recommendations(base, all_strategy_types, - region=region_sel if region_sel!='全市' else '全市') - st.subheader("各策略指标") - df_res = pd.DataFrame(results).T - st.dataframe(df_res, use_container_width=True) - st.success(f"⭐ 推荐：{recommendation}") - st.download_button("下载策略评估 CSV", - data=df_res.to_csv().encode('utf-8-sig'), - file_name="strategy_evaluation_results.csv", mime="text/csv") - with open('recommendation.txt','r',encoding='utf-8') as f: - st.download_button("下载推荐文本", data=f.read().encode('utf-8'), file_name="recommendation.txt") - else: - st.warning("数据中没有检测到策略。") - - # --- Tab 6: 策略对比 - with tab_comp: - def strategy_metrics(strategy): - mask = base['strategy_type'].apply(lambda x: strategy in x) - if not mask.any(): - return None - dt = mask[mask].index[0] - glm_pred, svr_pred, residuals = fit_and_extrapolate(base['accident_count'], dt, days=30) - if svr_pred is None: - return None - actual_post = base['accident_count'].loc[dt:dt+pd.Timedelta(days=29)] - pre = base['accident_count'].loc[dt-pd.Timedelta(days=30):dt-pd.Timedelta(days=1)] - stat, p = significance_test(pre, actual_post) - count_eff, sev_eff, (F1, F2), state = evaluate_strategy_effectiveness( - actual_series=base['accident_count'], - counterfactual_series=svr_pred, - severity_series=base['severity'], - strategy_date=dt, window=30 - ) - return { - "干预日": str(dt.date()), - "前30天事故": int(pre.sum()), - "后30天事故": int(actual_post.sum()), - "每日均值(前/后)": (float(pre.mean()), float(actual_post.mean())), - "t统计/p值": (stat, p), - "F1/F2": (float(F1), float(F2)), - "有效天数过半?": bool(count_eff), - "严重度下降?": bool(sev_eff), - "安全等级": state - } - if all_strategy_types: - st.subheader("策略对比") - with st.form(key="strategy_compare_form"): - colA, colB = st.columns(2) - with colA: - sA = st.selectbox("策略 A", options=all_strategy_types, key="stratA") - with colB: - sB = st.selectbox("策略 B", options=[s for s in all_strategy_types if s != st.session_state.get("stratA")], key="stratB") - submit_compare = st.form_submit_button("应用策略对比") - - if submit_compare: - mA = strategy_metrics(sA) - mB = strategy_metrics(sB) - if mA and mB: - show = pd.DataFrame({ - "指标": ["干预日", "前30天事故", "后30天事故", "每日均值(前)", "每日均值(后)", "t统计", "p值", "F1", "F2", "有效天数过半?", "严重度下降?", "安全等级"], - f"{sA}": [mA["干预日"], mA["前30天事故"], mA["后30天事故"], - mA["每日均值(前/后)"][0], mA["每日均值(前/后)"][1], - mA["t统计/p值"][0], mA["t统计/p值"][1], - mA["F1/F2"][0], mA["F1/F2"][1], - mA["有效天数过半?"], mA["严重度下降?"], mA["安全等级"]], - f"{sB}": [mB["干预日"], mB["前30天事故"], mB["后30天事故"], - mB["每日均值(前/后)"][0], mB["每日均值(前/后)"][1], - mB["t统计/p值"][0], mB["t统计/p值"][1], - mB["F1/F2"][0], mB["F1/F2"][1], - mB["有效天数过半?"], mB["严重度下降?"], mB["安全等级"]], - }) - st.dataframe(show, use_container_width=True) - st.download_button("下载对比表 CSV", - data=show.to_csv(index=False).encode('utf-8-sig'), - file_name="strategy_compare.csv", mime="text/csv") - else: - st.info("所选策略可能缺少足够的干预前数据或未在当前过滤范围内出现。") - else: - st.info("请选择策略并点击“应用策略对比”按钮。") - else: - st.warning("没有策略可供对比。") - - # --- Tab 7: 情景模拟 - with tab_sim: - st.subheader("情景模拟") - st.write("选择一个日期与策略，模拟“在该日期上线该策略”的影响：") - with st.form(key="simulation_form"): - sim_date = st.date_input("模拟策略上线日期", value=(base.index.max() - pd.Timedelta(days=14))) - sim_strategy = st.selectbox("模拟策略类型", options=all_strategy_types or ["示例策略"]) - sim_days = st.slider("模拟天数", 7, 60, 30) - submit_simulation = st.form_submit_button("应用模拟参数") - - if submit_simulation: - glm_pred, svr_pred, residuals = fit_and_extrapolate(base['accident_count'], pd.to_datetime(sim_date), days=sim_days) - if svr_pred is None: - st.warning("干预前数据不足，无法进行模拟。") - else: - count_eff, sev_eff, (F1, F2), state = evaluate_strategy_effectiveness( - actual_series=base['accident_count'], - counterfactual_series=svr_pred, - severity_series=base['severity'], - strategy_date=pd.to_datetime(sim_date), - window=sim_days - ) - fig_sim = go.Figure() - fig_sim.add_trace(go.Scatter(x=base.index, y=base['accident_count'], name='实际', mode='lines')) - fig_sim.add_trace(go.Scatter(x=svr_pred.index, y=svr_pred, name='Counterfactual(SVR)', mode='lines')) - fig_sim.update_layout(title=f"情景模拟：{sim_strategy} 自 {sim_date} 起", xaxis_title="日期", yaxis_title="事故数") - st.plotly_chart(fig_sim, use_container_width=True) - - st.success(f"模拟结果：F1={F1:.2f}, F2={F2:.2f}, 等级={state}；" - f"{'事故数在多数天小于counterfactual' if count_eff else '效果不明显'}；" - f"{'严重度下降' if sev_eff else '严重度无下降'}。") - st.download_button("下载模拟图 HTML", - data=open(save_fig_as_html(fig_sim, "simulation.html"), "rb").read(), - file_name="simulation.html", mime="text/html") - else: - st.info("请设置模拟参数并点击“应用模拟参数”按钮。") - - # --- New Tab 8: GPT 分析 - with tab_gpt: - from openai import OpenAI - st.subheader("GPT 数据分析与改进建议") - # open_ai_key = f"sk-dQhKOOG48iVEfgJfAb14458dA4474fB09aBbE8153d4aB3Fc" - if not HAS_OPENAI: - st.warning("未安装 `openai` 库。请安装后重试。") - elif not openai_api_key: - st.info("请在左侧边栏输入 OpenAI API Key 以启用 GPT 分析。") - else: - if all_strategy_types: - # Generate results if not already - results, recommendation = generate_output_and_recommendations(base, all_strategy_types, - region=region_sel if region_sel != '全市' else '全市') - df_res = pd.DataFrame(results).T - kpi_json = json.dumps(kpi, ensure_ascii=False, indent=2) - results_json = df_res.to_json(orient="records", force_ascii=False) - recommendation_text = recommendation - - # Prepare data to send - data_to_analyze = { - "kpis": kpi_json, - "strategy_results": results_json, - "recommendation": recommendation_text - } - data_str = json.dumps(data_to_analyze, ensure_ascii=False) - - prompt = str(f""" - 请分析以下交通安全分析结果，包括KPI指标、策略评估结果和推荐。 - 提供数据结果的详细分析，以及改进思路和建议。 - 数据：{str(data_str)} - """) - #st.text_area(prompt) - if st.button("上传数据至 GPT 并获取分析"): - try: - client = OpenAI( - base_url=open_ai_base_url, - # sk-xxx替换为自己的key - api_key=openai_api_key - ) - response = client.chat.completions.create( - model="gpt-4o", - messages=[ - {"role": "system", "content": "You are a helpful assistant that analyzes traffic safety data."}, - {"role": "user", "content": prompt} - ], - stream=False - ) - gpt_response = response.choices[0].message.content - st.markdown("### GPT 分析结果与改进思路") - st.markdown(gpt_response, unsafe_allow_html=True) - except Exception as e: - st.error(f"调用 OpenAI API 失败：{str(e)}") - else: - st.warning("没有策略数据可供分析。") - - # Update refresh time - st.session_state['last_refresh'] = datetime.now() - - else: - st.info("请先在左侧上传事故数据与策略数据，并点击“应用数据与筛选”按钮。") - -if __name__ == "__main__": - run_streamlit_app() \ No newline at end of file diff --git a/docs/install.md b/docs/install.md deleted file mode 100644 index e69de29..0000000 diff --git a/docs/usage.md b/docs/usage.md deleted file mode 100644 index e69de29..0000000 diff --git a/overview_series.html b/overview_series.html deleted file mode 100644 index 1cb6996..0000000 --- a/overview_series.html +++ /dev/null @@ -1,7 +0,0 @@ - - - -
-
- - \ No newline at end of file diff --git a/readme.md b/readme.md deleted file mode 100644 index d35f5f0..0000000 --- a/readme.md +++ /dev/null @@ -1,148 +0,0 @@ -# TrafficSafeAnalyzer - -一个基于 Streamlit 的交通安全分析系统，支持事故数据分析、预测模型、异常检测和策略评估。 - -## 功能 - -- 加载和清洗事故与策略数据（Excel 格式） -- 使用 ARIMA、KNN、GLM、SVR 等模型预测事故趋势 -- 检测异常事故点 -- 评估交通策略效果并提供推荐 - -## 安装步骤 - -### 前提条件 - -- Python 3.8+ -- Git -- 可选：Docker（用于容器化部署） - -### 安装 - -1. 克隆仓库： - -```bash -git clone https://github.com/tongnian0613/TrafficSafeAnalyzer.git -cd TrafficSafeAnalyzer -``` - -2. 创建虚拟环境（推荐）： - -```bash -conda create -n trafficsa python=3.12 -y -conda activate trafficsa -pip install -r requirements.txt -streamlit run app.py -``` - -3. 安装依赖： - - (1) 基本安装（必需依赖） - - ```bash - pip install streamlit pandas numpy matplotlib plotly scikit-learn statsmodels scipy - ``` - - (2) 完整安装（包含所有可选依赖） - - ```bash - pip install -r requirements.txt - ``` - - (3) 或者手动安装可选依赖 - - ```bash - pip install streamlit-autorefresh openpyxl xlrd cryptography - ``` - - (4) 运行应用： - - ```bash - streamlit run app.py - ``` - -## 依赖项 - -列于 `requirements.txt`： - -```txt -streamlit>=1.20.0 -pandas>=1.3.0 -numpy>=1.21.0 -matplotlib>=3.4.0 -plotly>=5.0.0 -scikit-learn>=1.0.0 -statsmodels>=0.13.0 -scipy>=1.7.0 -streamlit-autorefresh>=0.1.5 -python-dateutil>=2.8.2 -pytz>=2021.3 -openpyxl>=3.0.9 -xlrd>=2.0.1 -cryptography>=3.4.7 -openai>=2.0.0 -``` - -## 配置参数 - -- **数据文件**：上传事故数据（`accident_file`）和策略数据（`strategy_file`），格式为 Excel -- **环境变量**（可选）： - - `LOG_LEVEL=DEBUG`：启用详细日志 - - 示例：`export LOG_LEVEL=DEBUG`（Linux/macOS）或 `set LOG_LEVEL=DEBUG`（Windows） - -## 输入输出格式 - -### 输入 -- **事故数据 Excel**：需包含 `事故时间`、`所在街道`、`事故类型` 列 -- **策略数据 Excel**：需包含 `发布时间`、`交通策略类型` 列 - -### 输出 -- **预测结果**：CSV 文件（例如 `arima_forecast.csv`） -- **图表**：HTML 文件（例如 `overview_series.html`） -- **策略推荐**：文本文件（`recommendation.txt`） - -## 调用示例 - -运行 Streamlit 应用： -```bash -streamlit run app.py -``` - -访问 http://localhost:8501，上传数据文件并交互分析。 - -## 常见问题排查 - -**问题**：`ModuleNotFoundError: No module named 'streamlit'` -**解决**：运行 `pip install -r requirements.txt` 或检查 Python 环境 - -**问题**：数据加载失败 -**解决**：确保 Excel 文件格式正确，检查列名是否匹配 - -**问题**：`NameError: name 'strategy_metrics' is not defined` -**解决**：确保 `strategy_metrics` 函数定义在 `app.py` 中，且位于 `run_streamlit_app` 函数内 - -## 日志分析 - -- **日志文件**：`logs/app.log`（需在代码中配置 logging 模块） -- **查看日志**：`tail -f logs/app.log` -- **常见错误**： - - `ValueError`：检查输入数据格式 - - `ConnectionError`：验证网络连接或文件路径 - -## 升级说明 - -- **当前版本**：v1.0.0 -- **升级步骤**： - 1. 备份数据和配置文件 - 2. 拉取最新代码：`git pull origin main` - 3. 更新依赖：`pip install -r requirements.txt --upgrade` - 4. 重启应用：`streamlit run app.py` - -参考 `CHANGELOG.md` 查看版本变更详情。 - -## 许可证 - -MIT License - 详见 LICENSE 文件。 - -[![GitHub license](https://img.shields.io/github/license/tongnian0613/repo)](https://github.com/tongnian0613/TrafficSafeAnalyzer/LICENSE) -[![Build Status](https://img.shields.io/travis/username/repo)](https://travis-ci.org/tongnian0613/repo) diff --git a/recommendation.txt b/recommendation.txt deleted file mode 100644 index 28bbe96..0000000 --- a/recommendation.txt +++ /dev/null @@ -1 +0,0 @@ -建议在全市区域长期实施策略类型 交通管制措施 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 91abf65..0000000 --- a/requirements.txt +++ /dev/null @@ -1,31 +0,0 @@ -# Core dependencies -streamlit>=1.20.0 -pandas>=1.3.0 -numpy>=1.21.0 -matplotlib>=3.4.0 -plotly>=5.0.0 -scikit-learn>=1.0.0 -statsmodels>=0.13.0 - -# Scientific computing (optional but recommended) -scipy>=1.7.0 - -# Streamlit extensions (optional) -streamlit-autorefresh>=0.1.5 - -# Additional utilities -python-dateutil>=2.8.2 -pytz>=2021.3 - -# For data processing -openpyxl>=3.0.9 # For Excel file support -xlrd>=2.0.1 # For older Excel files - -# Security/authentication -cryptography>=3.4.7 - -# OpenAI -openai - -# Note: hashlib and json are part of Python standard library -# Note: os and datetime are part of Python standard library \ No newline at end of file diff --git a/run_metadata.json b/run_metadata.json deleted file mode 100644 index 5c34421..0000000 --- a/run_metadata.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "region": "全市", - "date_range": [ - "2022-01-01", - "2022-12-31" - ], - "strategy_filter": [], - "rows": 365, - "min_date": "2022-01-01", - "max_date": "2022-12-31" -} \ No newline at end of file diff --git a/simulation.html b/simulation.html deleted file mode 100644 index 20c179a..0000000 --- a/simulation.html +++ /dev/null @@ -1,7 +0,0 @@ - - - -
-
- - \ No newline at end of file diff --git a/strategy_evaluation_results.csv b/strategy_evaluation_results.csv deleted file mode 100644 index ebb6c7b..0000000 --- a/strategy_evaluation_results.csv +++ /dev/null @@ -1,5 +0,0 @@ -,effect_strength,adaptability,count_effective,severity_effective,safety_state,F1,F2,intervention_date -交通信息预警,-8.965321179202334,-0.7855379968058066,True,False,三级,0.2463091369521552,-1.0318471337579618,2022-01-13 -交通整治活动,-2.65100612878524,-1.667254385637472,True,False,三级,0.08411173458110731,-1.7513661202185793,2022-01-11 -交通管制措施,-10.70286313762653,0.19010392243197832,True,False,三级,0.2989387495766646,-0.1088348271446863,2022-01-20 -政策制度实施,-2.6771799687750018,-5.1316650216481605,True,False,三级,0.07856225107911223,-5.2102272727272725,2022-01-06