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摘要: 基于大数据挖掘的方法估算了铁路车站间OD客流，调查分析了铁路乘客对高铁和普铁的选择偏好，确定了高铁开通后普铁线路客运的供需关系与均衡，把普铁线路上的客流转换成相应的旅客列车列数，以此计量客运占用的普铁的通过能力；评估了各普铁路段的总通过能力，基于扣除系数法计算普铁各路段剩余的货运能力，并进一步在普铁线网上根据集装箱进港量确定集装箱班列枢纽站的候选集；将候选枢纽站的班列发车频率作为离散的内生变量，基于现实中的干线公路网络和普铁网络构建枢纽站选址和发班频率优化模型，求解模型，确定集装箱铁路集港服务网络的经济技术指标；以上海港和宁波港及其腹地为案例进行数值分析。计算结果表明：在案例的空间范围内的普铁运输线上，日均通过能力最小为79列，最大为137列；基于普铁各路段剩余的货运能力，计算得出各集装箱班列枢纽站的日均发班频率最小为6列，最大为19列；由计算得到的普铁路段上的流量可以看出，铁路运送到上海港和宁波港的日均集装箱量分别为13 677、12 094 TEU，分别占2个港口日均到达总量的25%和33%，相比目前占比5%~7%有大幅度增加。Abstract: The OD passenger flows between railway stations were estimated by the big data mining method, and the choice preferences of railway passengers for high-speed and non-high-speed railways were analyzed by investigation, and thus the supply-demand relationship and equilibirum of passenger transportation on non-high-speed railways after the opening of high-speed railways were determined. The passenger flows of non-high-speed railways were converted into the corresponding number of passenger trains, and the carrying capacity of non-high-speed railways occupied by passenger transportation was estimated. The total carrying capacity of non-high-speed railway links was measured, the remaining freight capacity of non-high-speed railway links was calculated based on the deduction coefficient method, and the alternatives of railway container liner terminals were given based on the container's arrival volumes. The train departure frequency of the alternatives was taken as a discrete endogenous variable, the optimization model of terminal locations and the departure frequency was built based on the trunk highway network and non-high-speed railway network in the reality, and the economic and technical indexes of container railway service network were determined by solving the model. The Shanghai Port, Ningbo Port, and their hinterlands were taken as examples to carry out a numerical analysis. Calculation results show that in terms of the non-high-speed railway links within the hinterlands of the two ports, the minimum average daily carrying capacity is 79 trains while the maximum is 137 trains. Based on the remaining freight capacity of non-high-speed railway links, the calculation result shows that the minimum average daily departure frequency of container trains of liner terminals is 6 trains while the maximum is 19 trains. From the calculated flows of the non-high-speed railway links, the average daily containers transported by the railway to Shanghai Port and Ningbo Port are 13 677 and 12 094 TEUs, respectively, accounting for 25% and 33% of their total daily arrivals, respectively, which is a significant increase compared with the current 5%-7%. 9 tabs, 2 figs, 27 refs.
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图 1 腹地内普铁的客流分配结果
Figure 1. Passenger flow assignment result of non-high speed railways in hinterland
图 2 最佳发班频率下铁路集装箱集港量
Figure 2. Port access container flows by railways under optimal departure frequencies
表 1 部分车站间的日均客流数据
Table 1. Daily passenger flow data between some stations
发站 到站 客流/人次 发站 到站 客流/人次 发站 到站 客流/人次 蚌埠 嘉兴 358 重庆 蚌埠 145 长沙 上海 1 156 常州 金华 264 郑州 合肥 512 镇江 常州 1 099 达州 九江 148 长沙 杭州 285 九江 宁波 322 恩施 丽水 272 宜昌 恩施 120 芜湖 合肥 1 614 阜阳 洛阳 167 徐州 杭州 463 苏州 长沙 658 杭州 温州 299 信阳 郑州 337 丽水 上海 394 合肥 南充 185 西安 郑州 544 绍兴 杭州 2 131 黄冈 南昌 195 武汉 洛阳 240 台州 温州 219 吉安 南京 148 上海 武汉 553 无锡 重庆 611 上饶 商丘 129 宁波 孝感 247 无锡 郑州 759 
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表 2 式(2)的标定结果
Table 2. Calibration results of Eq.(2)
参数 参数值 标准误差 t 显著性水平 样本数 R2 C 2.512 0.854 2.94 0.042 110 0.74 α1 0.494 0.095 5.20 0.001 α2 0.368 0.104 3.53 0.010 β 0.141 0.068 2.08 0.042 γ 0.386 0.098 -3.96 0.001
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表 3 式(7)的标定结果
Table 3. Calibration results of Eq.(7)
参数 参数值 标准误差 t 显著性水平 样本数 R2 θe -2.719 0.856 -3.176 0.049 θt -9.461 0.768 -12.319 0.001 168 0.78 θm 0.217 0.027 8.037 0.004
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表 4 车站间高铁和普铁的OD客流量
Table 4. Station-to-station passenger flows of high-speed and non-high-speed railways
发站 到站 距离/km Ci/元 Ti/min Mi P1 P2 q1/人次 q2/人次 C1 C2 T1 T2 M1 M2 蚌埠 嘉兴 481 86.6 221.3 320.4 115.2 6.9 17.7 0.35 0.65 125 233 常州 金华 392 78.7 180.0 300.8 98.0 6.3 14.4 0.38 0.62 100 164 达州 九江 1 023 183.4 456.8 701.0 243.2 14.6 36.5 0.42 0.58 62 86 恩施 丽水 1 244 224.0 572.3 744.0 325.0 17.9 45.8 0.43 0.57 117 155 阜阳 洛阳 450 81.0 207.0 300.4 102.2 6.5 16.6 0.35 0.65 58 109 杭州 温州 300 54.4 138.9 201.6 72.6 4.3 11.1 0.26 0.74 77 222 合肥 南充 1 080 194.4 496.8 720.0 259.2 15.5 39.7 0.39 0.61 72 113 黄冈 南昌 301 54.2 138.5 201.2 72.2 4.3 11.1 0.26 0.74 51 144 吉安 南京 800 128.0 320.0 533.3 188.2 10.2 64.0 0.32 0.68 47 101 上饶 商丘 860 138.0 482.0 573.3 202.4 11.1 38.6 0.32 0.68 41 88
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表 5 车站间剩余货运能力
Table 5. Left freight capacities between stations
路段节点 不同等级客车数量/列 货车与不同等级客车运行的时间之比 货车与不同等级客车运行的时间之差/h 剩余货运能力/列 节点1 节点2 快速 特快 快速 特快 快速 特快 宿州 徐州 5 4 0.73 0.53 0.11 0.18 119 宿州 蚌埠 4 4 0.73 0.53 0.26 0.45 118 蚌埠 滁州 10 5 0.73 0.53 0.41 0.69 93 亳州 阜阳 9 5 0.73 0.53 0.24 0.40 103 阜阳 淮南 2 1 0.73 0.53 0.26 0.44 132 徐州 宿迁 6 4 0.73 0.53 0.41 0.71 108 郑州 开封 7 5 0.73 0.53 0.27 0.46 107 开封 商丘 9 6 0.73 0.53 0.44 0.75 91 商丘 亳州 6 4 0.73 0.53 0.44 0.75 107 商丘 徐州 10 9 0.73 0.53 0.56 0.95 80
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表 6 候选发车点的发班频率
Table 6. Departure frequencies of terminal alternatives
出发城市 到达港口 发班频率 出发城市 到达港口 发班频率 出发城市 到达港口 发班频率 重庆 宁波港 一天两班 金华 宁波港 一天三班 宜春 宁波港 一周两班 苏州 上海港 一天一班 杭州 宁波港 一天一班 台州 宁波港 一天一班 金华 宁波港 一天四班 绍兴 宁波港 一天一班 温州 宁波港 一天两班 武汉 宁波港 一周一班 阜阳 宁波港 一天两班 徐州 宁波港 一天一班 常州 上海港 一天一班 芜湖 宁波港 一天一班 合肥 宁波港 一天一班 湖州 宁波港 一天一班 郑州 天津港 一周三班 无锡 上海港 一周六班 丽水 宁波港 一天一班 西安 宁波港 一天一班 长沙 盐田港 一周五班 滁州 上海港 隔天开班 南通 上海港 一周八班 芜湖 宁波港 隔天开班 南昌 上海港 一周两班 淮安 上海港 一周两班 南京 上海港 一天一班 宁波港 一天一班 宁波港 一天一班 宁波港 一周两班
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表 7 枢纽站及其辐射城市的日均集装箱发生量
Table 7. Average daily container generations of terminals and their catchment cities
枢纽城市 集装箱流量/TEU 辐射城市 集装箱流量/TEU 枢纽城市 集装箱流量/TEU 辐射城市 集装箱流量/TEU 重庆 764 绵阳 45 徐州 626 蚌埠 129 遂宁 17 淮南 30 南充 5 亳州 33 广元 484 淮北 72 达州 1 南昌 304 吉安 123 湘西州 16 新余 50 郑州 339 济源 14 宜春 312 焦作 213 九江 316 开封 70 抚州 92 商丘 78 南平 238 许昌 110 西安 216 咸阳 73 漯河 28 商洛 28 洛阳 208 安康 2 三门峡 21 宝鸡 44 武汉 759 娄底 15 运城 117 咸宁 53 汉中 6 黄石 127 南京 1 790 马鞍山 138 鄂州 27 巢湖 6 黄冈 121 长沙 306 娄底 15 荆门 41 湘潭 26 孝感 88 岳阳 63 随州 45 株洲 65
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表 8 枢纽站可能的发班频率
Table 8. Potential departure frequencies at terminals
组号 预设发班频率/(班·周-1) 班列出发城市 1 5~7 宜昌、荆州、宣城、阜阳、南阳、淮安、
九江、衢州、盐城、宜春、扬州2 8~10 郑州、泰州、无锡、西安、长沙、滁州、丽水 3 11~13 徐州、宿迁、芜湖、武汉、南通、常州 4 14~16 镇江、湖州、绍兴、合肥、苏州、重庆、
台州、南昌5 17~20 南京、温州、金华、杭州、嘉兴
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表 9 枢纽站的最佳班列发班频率
Table 9. Optimal departure frequencies at terminals
组号 发班频率/(班·周-1) 班列出发城市 设定 最佳 1 5~7 6 阜阳、南阳、衢州、扬州、宿迁 2 8~10 10 郑州、泰州、无锡、西安、盐城 3 11~13 13 湖州、徐州、芜湖、常州、杭州 4 14~16 16 重庆、台州、南昌、九江 5 17~20 19 南京、温州、金华、长沙、武汉
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