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摘要: 预处理出租车轨迹数据并提取了出租车行程数据；识别了地铁运营时段内出租车-地铁组合出行，分类为接入出行、接出出行，并划定了地铁站点的潜在影响范围；采用极限梯度提升模型分析了站域建成环境对出租车-地铁组合出行的非线性影响，并引入SHAP模型解释了非线性特征的重要性、方向及阈值现象；运用必要条件分析方法探索了站域建成环境要素对出租车-地铁组合出行的必要性。研究结果表明：站域建成环境对不同类别的出租车-地铁组合出行具有不同的贡献度，其中土地利用混合度、公交站点密度、路网密度、居住用地密度对出租车-地铁组合出行具有显著的必要性；早高峰期间，影响接入出行的重要要素依次是公交站点密度、居住用地密度和路网密度，重要度分别为33.38%、30.10%、19.33%，影响接出出行的重要要素依次是公交站点密度、居住用地密度和路网密度，重要度分别为41.48%、15.61%、14.41%；晚高峰期间，影响接入出行的重要要素依次是居住用地密度、路网密度和公交站点密度，重要度分别为34.13%、23.84%、23.13%；影响接出出行的重要要素依次是居住用地密度、公交站点密度和土地利用混合度，重要度分别为40.88%、20.32%、14.72%；公交站点密度的SHAP值呈现3个阶段变化，以每平方公里15和50个站点为阈值点；路网密度在14 km·km-2处为重要分界点，低于该值对租车-地铁组合出行为负向贡献，高于该值为正向贡献；居住用地密度超过0.45 m2·km-2时对租车-地铁组合出行为正向贡献且趋于稳定。Abstract: The taxi trajectory data was preprocessed and taxi trip data was extracted. Taxi-metro combined travel was identified and categorized into subway extending access (SE-access) trips and subway extending egress (SE-egress) trips, and potential influence areas of subway stations were identified. The extreme gradient boosting (XGBoost) model was employed to analyze the nonlinear effects of station-area built environment on taxi-metro combined travel. The Shapley additive explanations (SHAP) model was introduced to explain the nonlinear characteristics: importance, orientation, and threshold phenomena. The necessity of station-area built environment factors for taxi-metro combined travel was analyzed using necessary condition analysis method. Analysis results indicate that the station-area built environment contributes differently to various types of taxi-metro combined travel, with land use mix, bus station density, road network density, and residential land density significantly necessary for taxi-metro combined travel. During the morning peak hours, key factors influencing SE-access trips are bus station density, residential land density, and road network density, with importance values of 33.38%, 30.10%, and 19.33%, respectively. For SE-egress trips, key factors are bus station density, residential land density, and road network density, with importance values of 41.48%, 15.61%, and 14.41%, respectively. During the evening peak hours, key factors influencing SE-access trips are residential land density, road network density, and bus station density, with importance values of 34.13%, 23.84%, and 23.13%, respectively. For SE-egress trips, key factors are residential land density, bus station density, and land use mix, with importance values of 40.88%, 20.32%, and 14.72%, respectively. The SHAP value of bus station density changes with a three-stage trend, with 15 and 50 stations per square kilometer as threshold points. For road network density, 14 km·km-2 is a critical demarcation value, meaning that the road network density below this threshold negatively contributes to taxi-metro combined travel, while the road network density above this threshold contributes positively. When residential land density exceeds 0.45 m2·km-2, it positively contributes to taxi-metro combined travel and tends to remain stable.
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图 6 基于SHAP值的早高峰接入出行影响因素分析
Figure 6. Analysis of influencing factors for access trip during morning peak hours based on SHAP values
图 7 基于SHAP值的晚高峰接入出行影响因素分析
Figure 7. Analysis of influencing factors for access trip during evening peak hours based on SHAP values
图 8 基于SHAP值的早高峰接出出行影响因素分析
Figure 8. Analysis of influencing factors for egress trip during morning peak hours based on SHAP values
图 9 基于SHAP值的晚高峰接出出行影响因素分析
Figure 9. Analysis of influencing factors for egress trip during evening peak hours based on SHAP values
图 10 建成环境要素对早高峰接入出行的必要性
Figure 10. Necessities of built environment on access trips during morning peak hours
图 11 建成环境要素对晚高峰接入出行的必要性
Figure 11. Necessities of built environment on access trips during evening peak hours
图 12 建成环境对早高峰接出出行的必要性
Figure 12. Necessities of built environment on egress trips during morning peak hours
图 13 建成环境对晚高峰接出出行的必要性
Figure 13. Necessities of built environment on egress trips during evening peak hours
图 14 贡献率前5个特征的SHAP依赖关系(早高峰接入出行)
Figure 14. SHAP dependences of top 5 contributing features (access trips during morning peak hours)
图 15 贡献率前5个特征的SHAP依赖关系(晚高峰接入出行)
Figure 15. SHAP dependences of top 5 contributing features (access trips during evening peak hours)
图 16 贡献率前5个特征的SHAP依赖关系(早高峰接出出行)
Figure 16. SHAP dependences of top 5 contributing features (egress trips during morning peak hours)
图 17 贡献率前5个特征的SHAP依赖关系(晚高峰接出出行)
Figure 17. SHAP dependences of top 5 contributing features (egress trips during evening peak hours)
表 1 出租车轨迹数据格式
Table 1. Data format of taxi trajectory
字段 数据类型 实例 备注 车牌号 文本 甘AXXXX 出租车的唯一标识 时间 20210402083526 2021年4月2日08:35:26 载客状态 空车 空载状态 方向角 正北 正北方向 经度 数字 36.30 单位为° 纬度 103.78 单位为° 瞬时速度 35 单位为km·h-1 里程数 100 单位为km 
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表 2 接入出行、接出出行换乘距离计算结果
Table 2. Calculation results of transfer distance for access and egress trips
类型 方式 时段 回归模型 R2 Sig 换乘距离/m 工作日 接入出行 早高峰 y=-0.005 4+2.777×10-4x1-1.392×10-8x12 0.997 0.000 3 847 晚高峰 y=-0.006 6+2.782×10-4x1-1.389×10-8x12 0.998 0.000 3 830 接出出行 早高峰 y=-0.001 8+2.751×10-4x2-1.365×10-8x22 0.997 0.000 3 796 晚高峰 y=-0.003+2.804×10-4x2-1.385×10-8x22 0.997 0.000 3 957 节假日 接入出行 早高峰 y=-0.047 2+2.849×10-4x1-1.418×10-8x12 0.998 0.000 3 910 晚高峰 y=-0.002 6+2.774×10-4x1-1.403×10-8x12 0.999 0.000 3 775 接出出行 早高峰 y=-0.066+3.088×10-4x2-1.566×10-8x22 0.998 0.000 3 794 晚高峰 y=-0.545×10-4x2-1.132×10-8x22 0.998 0.000 4 080
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表 3 建成环境评价指标
Table 3. Evaluation indicators of built environment
特征因子 特征指标 分析尺度 数据来源 密度 不同类型土地利用的密度 1 km×1 km 政府规划 多样性 土地利用混合度 1 km×1 km 根据土地利用类型数据计算 设计 路网密度 1 km×1 km 开放街道地图 公共交通邻近性 公交站点/线网密度 1 km×1 km 百度地图
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表 4 变量描述与统计
Table 4. Variable descriptions and statistics
变量类型 变量 描述 最小值 最大值 均值 标准差 因变量 Na1 每个网格内工作日早高峰时段接入出行量/人次 0 521 40.604 62.849 Ne1 每个网格内工作日晚高峰时段接入出行量/人次 0 364 31.022 47.775 Na2 每个网格内工作日早高峰时段接出出行量/人次 0 393 31.483 51.274 Ne2 每个网格内工作日晚高峰时段接出出行量/人次 0 404 39.135 54.606 自变量 E 每个网格内的土地利用混合度 0 0.444 0.183 0.108 PRT 每个网格内居住用地密度/(m2·km-2) 0 0.946 0.209 0.212 PC 每个网格内商业用地密度/(m2·km-2) 0 0.389 0.027 0.058 PI 每个网格内工业用地密度/(m2·km-2) 0 0.969 0.084 0.166 PRN 每个网格内休闲用地密度/(m2·km-2) 0 0.464 0.020 0.054 PO 每个网格内其他用地密度/(m2·km-2) 0 0.568 0.036 0.089 PRD 每个网格内路网密度/(km·km-2) 0 25.416 9.298 5.296 PBS 每个网格内公交站点密度/(个·km-2) 0 115.000 15.163 20.650 PBL 每个网格内公交线路密度/(km·km-2) 0 86.416 10.290 14.046
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表 5 XGBoost模型拟合优度
Table 5. Goodness of fit of XGBoost model
变量 R2 训练集 验证集 接入出行 早高峰 0.79 0.60 晚高峰 0.75 0.64 接出出行 早高峰 0.74 0.59 晚高峰 0.78 0.69
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表 6 站域建成环境要素的充分性和必要性(接入出行)
Table 6. Sufficiency and necessity of feeder-related built environment elements (access trips)
变量 早高峰接入出行 是否充分必要？(早高峰) 晚高峰接入出行 是否充分必要？(晚高峰) 充分性/% 必要性 充分性/% 必要性 E 9.74 0.164 否 13.47 0.120 否 PBS 33.38 0.398 是 23.13 0.346 是 PRD 19.33 0.589 是 23.84 0.576 是 PC 7.43 0.004 否 5.42 0.004 否 PI 0.00 0.000 否 0 0.000 否 PRT 30.10 0.295 是 34.13 0.363 是 PRN 0.00 0.005 否 0.00 0.004 否 PO 0.00 0.000 否 0.00 0.000 否
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表 7 站域建成环境要素的其充分性和必要性(接出出行)
Table 7. Sufficiency and necessity of feeder-related built environment elements (egress trips)
变量 早高峰接出出行 是否充分必要？(早高峰) 晚高峰接出出行 是否充分必要？(晚高峰) 充分性/% 必要性 充分性/% 必要性 E 7.30 0.167 否 14.72 0.103 否 PBS 41.48 0.352 是 20.32 0.343 是 PRD 14.41 0.574 是 14.58 0.561 是 PC 5.94 0.657 否 3.36 0.004 否 PI 12.10 0.000 否 4.44 0.000 否 PRT 15.61 0.434 是 40.88 0.380 是 PRN 3.15 0.005 否 1.69 0.005 否 PO 0.00 0.000 否 0.00 0.000 否
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