Multi-mode passenger flow sharing characteristics of highway-rail composite intercity corridor
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摘要: 选取了陕西省内距中心城市西安350 km范围内的咸阳、渭南、黄陵、延安4个节点城市, 搜集了相关公铁复合城际走廊上高速铁路、普通铁路、长途客车、小汽车高速出行的出行量、车内时间、票价或通行费等客流特征参数, 梳理了各种城际客流分担分析方法; 构建了距离转移曲线模型和多元Logit模型, 通过曲线拟合、试算和回归分析对模型进行了标定, 并根据模型标定结果分析了客流分担率对距离、时间和费用的敏感性, 得到区域城际多模式客流分担特征, 给出城际通道规划管理的相关建议。研究结果表明: 高速铁路、普通铁路和小汽车高速出行3种模式的分担率-距离转移曲线拟合结果理想, 决定系数均在0.94以上; MNL模型在车外时间取90~150 min时, 拟合效果较好, 决定系数均在0.79以上, 且在时间价值取50~70元·h-1情景下决定系数达到峰值; 随着城际出行距离的增加, 出行者选择从小汽车高速出行转移到城际铁路出行, 且高速铁路较普通铁路更有优势, 西安与近距离的咸阳之间小汽车高速分担率达96.91%, 与远距离的延安之间高速铁路分担率达53.66%, 普通铁路分担率达30.58%;以车外时间为120 min为例, 高速铁路、普通铁路、长途客车、小汽车4种出行模式的阻抗系数分别为0.029~0.044、0.034~0.042、0.030~0.040、0.028~0.048, 小汽车高速出行和高速铁路增长幅度较大, 2种出行对费用更加敏感, 在时间价值取60元·h-1条件下, 4种出行模式的阻抗系数为0.038~0.042, 4种出行对广义时间敏感性无明显差异; 建议进一步挖掘更多城市群城际通道客流分担规律, 并精确考虑城际出行链的城市端细节, 以更好地指导城际走廊的宏观规划与管理。Abstract: Four node cities, including Xianyang, Weinan, Huangling and Yan'an within 350 km from the central city of Xi'an in Shaanxi Province were selected. The passenger flow characteristic parameters such as travel volume, time inside the vehicle, ticket price or toll of high-speed railway, ordinary railway, long-distance bus and car on the highway were collected. Various methods of intercity passenger flow sharing analysis were summarized. A distance transfer curve model and multivariate Logit model were constructed and were calibrated by curve fitting, trial calculation and regression analysis. According to the model calibration results, the sensitivities of passenger flow sharing rate to distance, time and cost were analyzed, respectively. The sharing characteristics of regional intercity multi-mode passenger flow were obtained, and the relevant suggestions on the planning and management of intercity corridors were given. Analysis result indicates that the fitting results of sharing rate-distance transfer curves of three modes, including high-speed railway, ordinary railway and car on the highway, are ideal. The determination coefficients are all above 0.94. When MNL model takes 90-150 min out of the vehicle, the fitting effect is preferable and the determination coefficients are all above 0.79. The determination coefficients reach the peak values when the time value is 50-70 yuan·h-1. With the increase of intercity travel distance, the travelers choose to transfer from car on the highway to intercity railway travel. A high-speed railway has more advantage than an ordinary railway. The sharing rate of the car on the highway between Xi'an and Xianyang in a near distance is 96.91%. The sharing rate of the high-speed railway between Xi'an and Yan'an in a long distance is 53.66%, and that of the ordinary railway is 30.58%. Taking the outside time of 120 min as an example: the ranges of impedance coefficient of high-speed railway, ordinary railway, long-distance bus and car on the highway are 0.029-0.044, 0.034-0.042, 0.030-0.040 and 0.028-0.048, respectively, car and high-speed railway have a larger growth range, making both services more sensitive to travel costs. The impedance coefficients of the four modes are 0.038-0.042 under the condition that the time value is 60 yuan·h-1. There is no significant difference between the four services on the generalized time senisitivity of travel. It is suggested that more passenger flow sharing rules of inter city corridors in urban agglomerations should be explored. The details of an urban end of an inter city travel chain should be considered accurately to guide the macro planning and management of intercity corridors preferably.
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图 4 决定系数随时间价值变化趋势
Figure 4. Changing trends of determination coefficient over time value
图 5 车外时间为90 min时各出行方式阻抗系数变化趋势
Figure 5. Changing trends of impedance coefficient of travel modes under 90 min out of vehicle
图 6 车外时间为120 min时各出行方式阻抗系数变化趋势
Figure 6. Changing trends of impedance coefficient of travel modes under 120 min out of vehicle
图 7 车外时间为150 min时各出行方式阻抗系数变化趋势
Figure 7. Changing trends of impedance coefficient of travel modes under 150 min out of vehicle
表 1 客流分担分析方法比较
Table 1. Comparison of passenger flow sharing analysis methods
客流分担分析方法 理论基础 处理模式数量 所需数据 应用场景 总体评价 相近模式法 弱 1种 其他地区客流数据 工程应用 差 弹性系数法 弱 1种 历史数据 工程应用 差 转移曲线法 中 1种或多种 客流分担统计数据 工程应用、科学研究 良 Logit模型及其改进模型 强 2种及以上 各模式时间、费用等阻抗数据 科学研究 优 博弈论等其他方法 强 2种及以上 出行细节数据 理论层面 良 
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表 2 城市社会经济指标
Table 2. Social-economic indicators of cities
城市 市区建成区面积/km2 市区常住人口/万人 市区人均GDP/万元 咸阳 155.5 56.27 10.67 渭南 99.3 122.81 4.29 黄陵 5.6 13.23 11.52 延安 77.6 66.97 6.51
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表 3 4个节点城市到西安的客流特征参数
Table 3. Passenger flow characteristic parameters from four node cities to Xi'an
出行方式 高速铁路 普通铁路 长途客车 小汽车 城市 咸阳 出行量/人次 1 720 697 158 80 792 车内时间/min 13 18 46 55 票价或收费/元 9.5 9.0 10.0 4.0 渭南 出行量/人次 2 715 1 861 1 137 10 024 车内时间/min 17 39 56 73 票价或收费/元 29.5 11.0 21.5 24.0 黄陵 出行量/人次 203 136 13 328 车内时间/min 82 130 164 160 票价或收费/元 60.5 32.5 55.5 95.0 延安 出行量/人次 3 666 2 089 234 843 车内时间/min 140 200 260 260 票价或收费/元 95.5 50.5 90.0 160.0
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表 4 多模式通道各出行方式的分担率
Table 4. Sharing rates of each travel mode in multi-mode transportation channeds
城市 咸阳 渭南 黄陵 延安 高速铁路 0.020 6 0.172 5 0.298 5 0.536 6 普通铁路 0.008 4 0.118 3 0.200 0 0.305 8 长途客车 0.001 9 0.072 3 0.019 1 0.034 3 小汽车 0.969 1 0.637 0 0.482 4 0.123 4
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表 5 各出行方式分担率随距离拟合函数
Table 5. Fitting functions of sharing rate with distance in travel modes
出行方式 拟合函数 决定系数 小汽车 P=-0.32ln(L)+2.030 1 0.944 4 高速铁路 P=0.198 8ln(L)-0.661 1 0.944 1 普通铁路 P=0.116 8ln(L)-0.381 4 0.979 1 长途客车 P=0.000 3L0.863 7 0.343 9
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表 6 考虑车外时间的MNL模型标定结果
Table 6. Calibration result of MNL model considering the time out of vehicle
车外时间/min 时间价值/(元·h-1) λ1 λ2 λ3 λ4 拟合决定系数 显著性 90 20 0.038 0.045 0.037 0.033 0.790 0.002 30 0.045 0.049 0.042 0.040 0.800 0.001 40 0.049 0.052 0.045 0.045 0.804 0.001 50 0.053 0.053 0.048 0.049 0.805 0.001 60 0.056 0.055 0.049 0.052 0.805 0.001 70 0.058 0.056 0.051 0.055 0.805 0.001 80 0.059 0.057 0.052 0.056 0.804 0.001 90 0.061 0.057 0.053 0.058 0.803 0.001 100 0.062 0.058 0.053 0.059 0.803 0.001 110 0.063 0.058 0.054 0.061 0.802 0.001 120 20 0.029 0.034 0.030 0.028 0.796 0.002 30 0.033 0.037 0.033 0.033 0.804 0.001 40 0.036 0.038 0.035 0.037 0.807 0.001 50 0.038 0.039 0.037 0.039 0.808 0.001 60 0.040 0.040 0.038 0.042 0.808 0.001 70 0.041 0.040 0.038 0.043 0.808 0.001 80 0.042 0.041 0.039 0.045 0.807 0.001 90 0.043 0.041 0.040 0.046 0.807 0.001 100 0.043 0.041 0.040 0.047 0.806 0.001 110 0.044 0.042 0.040 0.048 0.805 0.001 150 20 0.023 0.028 0.025 0.024 0.801 0.001 30 0.026 0.029 0.027 0.028 0.807 0.001 40 0.028 0.030 0.029 0.031 0.809 0.001 50 0.029 0.031 0.030 0.034 0.810 0.001 60 0.031 0.031 0.031 0.036 0.810 0.001 70 0.031 0.031 0.031 0.037 0.810 0.001 80 0.032 0.032 0.032 0.038 0.809 0.001 90 0.032 0.032 0.032 0.039 0.809 0.001 100 0.033 0.032 0.032 0.040 0.808 0.001 110 0.033 0.032 0.033 0.041 0.808 0.001
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