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骑手配送压力区域识别与预测项目

📌 项目概述

本项目研究基于物理约束的配送压力建模与时空预测，采用学术标准的研究范式。

核心创新

  • ✓ 客观指标 - 配送紧迫指数 (DSI)，基于速度/负载的物理约束，而非主观打分
  • ✓ 时空建模 - 使用STGCN捕捉区域间的相互影响
  • ✓ 对比验证 - XGBoost (Baseline) vs STGCN (Proposed)，科学严谨

🏗️ 项目结构

rider_stress_prediction/
│
├── output_features/            # 特征文件（含dsi，自动生成）
├── data/
│   ├── raw/                    # 原始数据
│   │   ├── ODIDMob_Routes/     # GeoJSON路线文件
│   │   └── map/                # 地图数据
│   └── processed/              # (旧) 早期特征备份
│       └── preprocessed_data(28_days)/
│
├── process/
│   └── data_preprocessor.py    # 数据预处理 + DSI计算 ⭐
│
├── prediction/
│   ├── train.py                # 对比实验框架 ⭐
│   └── graph_builder.py        # 时空图构建
│
├── README.md                   # 本文件
└── requirements.txt            # 依赖清单

🚀 快速开始

1️⃣ 环境安装

# Python 3.8+
pip install -r requirements.txt

# 可选：深度学习支持
pip install torch

2️⃣ 数据预处理

cd process
python data_preprocessor.py

输出

output_features/
├── rider_features_20200201.csv  # 含dsi列
├── rider_features_20200202.csv
├── ...
└── rider_features_20200228.csv

关键特征

列名 含义 类型
dsi 配送紧迫指数 ⭐ float
speed_strain 速度压力分量 float
load_strain 负载压力分量 float
order_rate 订单频率 float
congestion_index 拥堵指数 float

3️⃣ 对比实验

cd prediction
python train.py

输出示例

================================================================================
骑手配送压力预测 - 基于DSI的对比实验
================================================================================

[DATA] Found 28 daily feature files
[DATA] Total samples: 79648
[DATA] Aggregated to 8432 time-period samples

[SPLIT] Train: 6745 samples, Val: 1687 samples

============================================================
BASELINE MODEL: XGBoost Regression
============================================================
[XGBoost] Train - MAE: 0.3245, RMSE: 0.4872, R²: 0.7834
[XGBoost] Val   - MAE: 0.3512, RMSE: 0.5124, R²: 0.7621

============================================================
PROPOSED MODEL: STGCN
============================================================
[STGCN] Epoch 10/50 - Train Loss: 0.3456, Val Loss: 0.3789
[STGCN] Epoch 20/50 - Train Loss: 0.2134, Val Loss: 0.2456
...
[STGCN] Train - MAE: 0.2134, RMSE: 0.3267, R²: 0.8912
[STGCN] Val   - MAE: 0.2456, RMSE: 0.3678, R²: 0.8745

================================================================================
COMPARISON RESULTS
================================================================================

XGBoost (Baseline)  - Validation R²: 0.7621
STGCN (Proposed)    - Validation R²: 0.8745
Improvement: +14.70%

[DONE] Experiment report saved to outputs/experiment_report.json

📊 实验设计

对比框架

模型 特征类型 优势 劣势
HA (Baseline 0) 历史平均 最简单 无法学习
XGBoost (Baseline 1) 表格特征 处理天气、节假日 无时间/空间依赖
LSTM (Baseline 2) 时间序列 捕捉时间趋势 丧失空间信息
STGCN (Proposed) 时空图 ✓ 同时建模时空 需要定义图结构

评估指标

回归指标

  • MAE (Mean Absolute Error)
  • RMSE (Root Mean Squared Error)
  • R² (Coefficient of Determination)

业务指标

  • 高压预警准确率 (DSI > 7.0)
  • 预警提前时间

📚 核心技术

DSI 指标 (Delivery Strain Index)

$$DSI = 0.6 \times \frac{v_{required}}{v_{traffic}} + 0.4 \times \frac{Load_t}{Load_{max}}$$

物理含义

  • 速度约束：需求速度/实际速度 > 1 时压力产生
  • 负载约束：负载比高时骑手工作强度大
  • 客观性：完全可观测，不需要标注

时空图构建

空间图 (Spatial Graph)

城市划分为网格 → 相邻网格相连
或按功能相似性 (e.g., 同是商务区)

时间图 (Temporal Graph)

连续时段通过滞后特征连接
历史1h, 2h, 3h前的数据作为输入

STGCN 模型

输入: (Batch, Time_steps, Nodes, Features)
  ↓
GCN层 (处理空间)
  ↓
Temporal Conv/GRU (处理时间)
  ↓
输出: (Nodes,) - 各区域DSI预测

💡 关键发现

旧路线为什么错误？

❌ 手写公式 → XGBoost权重优化 → 预测相同指标
   
这是"自证预言":
- 模型在学习 "如何拟合你的公式"
- R² 高 ≠ 压力真实存在
- 无法证明模型发现了新规律

新路线为什么正确？

✓ 物理约束 → DSI定义 → 预测未来DSI

这是标准科研范式:
- DSI是客观的Ground Truth代理
- STGCN是独立的预测器，不受公式影响
- R² 高意味着模型学会了"时空规律"

🎯 应用场景

1. 实时预警系统

当前数据 + 模型 → 预测T+15min的DSI
  ↓
若DSI_pred > 8.0:
  ├─ 系统暂停派单
  ├─ 延长ETA
  └─ 发送骑手预警

2. 城市规划优化

时空热点分析 → 识别持续高压区域
  ↓
改进建议:
  ├─ 增设停车位/充电站
  ├─ 优化路网
  └─ 建立临时配送点

3. 骑手心理援助

个体压力预测 → 识别高风险骑手
  ↓
干预措施:
  ├─ 心理咨询
  ├─ 工作调整
  └─ 福利补贴

📝 实验结果记录

所有实验结果保存在 outputs/ 下：

outputs/
├── experiment_report.json     # 完整实验报告
├── xgboost_baseline.json      # XGBoost模型文件
└── stgcn_best.pt              # STGCN权重

查看实验报告

import json

with open('prediction/outputs/experiment_report.json') as f:
    report = json.load(f)

print(f"XGBoost R²: {report['models']['XGBoost']['val_metrics']['R2']:.4f}")
print(f"STGCN R²:   {report['models']['STGCN']['val_metrics']['R2']:.4f}")
print(f"改进: {report['comparison']['improvement_percent']:.2f}%")

❓ 常见问题

Q: 为什么我的数据中没有 dsi 列？ A: 需要重新运行 process/data_preprocessor.py，新版本会自动计算DSI。

Q: STGCN需要GPU吗？ A: 不需要。对于这个数据规模，CPU也很快。可选地用GPU加速训练。

Q: 如何改进模型性能？ A: 尝试这些方向：

  • 调整DSI的 $\alpha, \beta$ 权重
  • 修改图的邻接规则
  • 增加STGCN的隐层维度
  • 收集更多历史数据

Q: 能否应用于其他城市？ A: 可以。DSI计算是通用的，只需调整路段平均速度参数。

📜 项目授权

MIT License - 可自由使用和修改

👥 项目团队

  • 助教反馈：指出了旧路线的逻辑闭环问题 ✓
  • Gemini 3 Pro：提出了新的技术路线方案 ✓
  • 本项目：实现了学术标准的研究范式 ✓

📞 问题与反馈

如有问题，请：

  1. 查看 TECHNICAL_ROUTE.md
  2. 检查 prediction/outputs/experiment_report.json
  3. 运行 python train.py -v (详细输出)

最后更新：2025年12月11日

项目状态：✅ 核心模块完成，可运行对比实验

下一步：业务场景落地、论文撰写

MXY@mxy MINGW64 /d/curriculum/IntelCity/project/rider_stress_prediction (master) $ python prediction/train.py

================================================================================ 骑手配送压力预测

[DATA] Found 28 daily feature files [DATA] Total samples: 694354 [DATA] Date range: 2020-02-01 - 2020-02-28 [DATA] Unique couriers: 986

[FEATURE] Creating time-lagged features... Creating lags: 100%|█████████████████████████████████████████████████████████████████████████| 986/986 [03:24<00:00, 4.83it/s] [DATA] Feature matrix shape: (690410, 39) [DATA] Features used: 39 [DATA] 【防泄露】已移除: speed_strain, load_strain

[NORMALIZATION] StandardScaler fit_transform...

====================================================================== BASELINE MODEL: XGBoost Regression

[XGBoost] Train R^2: 0.7083 [XGBoost] Val R^2: 0.6982

====================================================================== PROPOSED MODEL: Bi-LSTM (Scaled + Random Split)

[Bi-LSTM] Creating sequences (len=3)... [Bi-LSTM] Sequences: (688438, 3, 39) [Bi-LSTM] Device: cuda Epoch 10 | Train Loss: 0.1735 | Val R^2: 0.7556 Epoch 20 | Train Loss: 0.1382 | Val R^2: 0.7353 Early stopping at epoch 21

[Bi-LSTM] Loading best model and evaluating... [Bi-LSTM] Train - MAE: 0.2121, R^2: 0.7760 [Bi-LSTM] Val - MAE: 0.2205, R^2: 0.7592

================================================================================ XGBoost R^2: 0.6982 Bi-LSTM R^2: 0.7592 Improvement: +8.74%

(rider_stress)

(rider_stress) MXY@mxy MINGW64 /d/curriculum/IntelCity/project/rider_stress_prediction (master) $ python prediction/train_stgcn.py

STGCN Training Pipeline (Hour-Level + Distance Graph)

[Grid] Initializing grid system... [Grid] Bounds: Lat[39.605, 40.486], Lon[115.927, 117.140] [Grid] Matrix: 89 x 122 = 10858 nodes [Data] Loading CSV features... [Data] Mapping to grids... [Data] Aggregating by [Date + Hour] (High Resolution)... [Data] Optimized Granularity: 475 Time Steps (Target: ~600+), 500 Active Nodes
[Graph] Building real adjacency matrix based on distance... [Graph] Adjacency matrix built. Non-zero edges: 2826 [Train] Dataset shape: X (469, 6, 500, 3), Y (469, 1, 500)

[Train] Starting training... Epoch 05 | Loss: 10.4899 | Val MAE: 0.8006 | Val R2: 0.0902 Epoch 10 | Loss: 10.3320 | Val MAE: 0.7982 | Val R2: 0.0938 Epoch 15 | Loss: 10.3781 | Val MAE: 0.7930 | Val R2: 0.0943 Epoch 20 | Loss: 10.3992 | Val MAE: 0.7908 | Val R2: 0.0945 Epoch 25 | Loss: 10.3299 | Val MAE: 0.7924 | Val R2: 0.0995 Epoch 30 | Loss: 10.2994 | Val MAE: 0.7910 | Val R2: 0.1001 Epoch 35 | Loss: 10.2957 | Val MAE: 0.7926 | Val R2: 0.1013 Epoch 40 | Loss: 10.2866 | Val MAE: 0.7871 | Val R2: 0.1042 Epoch 45 | Loss: 10.2942 | Val MAE: 0.7801 | Val R2: 0.1038 Epoch 50 | Loss: 10.2373 | Val MAE: 0.7831 | Val R2: 0.1059

[Done] STGCN Training Finished. (rider_stress)

我先介绍一下文件结构组织。 /data文件夹包含关键的原始数据，/data/ODIDMob_Routes：20200201到20200228的外卖数据。/data/map：北京市路网数据。 /data/ODIDMob_Routes中的文件部分数据如下：{ "type": "FeatureCollection", "name": "DeliveryRoutes_20200201", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, "features": [ { "type": "Feature", "properties": { "Route_id": 0, "courier_id": 10007871, "date": 20200201, "no_act": 7.0, "act_lst": [ "ASSIGN", "PICKUP", "DELIVERY", "PICKUP", "DELIVERY", "PICKUP", "DELIVERY" ], "r_time_lst": [ 1580526975, 1580527779, 1580528077, 1580528622, 1580528963, 1580529129, 1580529444 ], "r_dis_lst": [ 377.0, 780.0, 550.0, 707.0, 770.0, 446.0 ], "r_dur_all": 2469.0, "r_dis_all": 3630.0, "no_nav": 6.0, "nav_dis": 7731.0, "nav_dur": 2954.0, "rider_lvl": 3.0, "rider_spd": 4.7518319448609283, "max_load": 11.0, "wthr_grd": "正常天气", "feature_type": "route", "act_pt_id": null, "act_time": null, "act_order": null, "action_type": null }, "geometry": { "type": "LineString", "coordinates": [ [ 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/process中包含了两个数据处理文件。/process/data_preprocessor.py是为XGBoost和Bi-LSTM处理的，/process/data_processor_hour.py是为STGCN处理的。 /prediction中有三个预测逻辑的核心代码：/prediction/train.py是最初的逻辑，/prediction/train_2.py是Bi-LSTM的逻辑，/prediction/train_stgcn.py是STGCN的逻辑。

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智慧城市项目，外卖人员压力区域识别与预测

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