A Validation Study of Interactive Stress Relationship Between Urban Rail and Regular Bus Transportation Systems
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摘要: 为了揭示城市轨道交通与常规公交之间的交互胁迫的演化特点, 通过借鉴城市化水平与生态环境之间的交互胁迫理论, 构建轨道交通与常规公交交互胁迫模型, 以我国2009年之前开通运营城市轨道交通的10个城市为样本, 基于轨道交通与常规公交之间的客运总量、线路长度及运营车辆3个维度的历史演化规律分析基础上, 验证轨道交通与常规公交之间交互胁迫关系, 进而对不同城市轨道交通与常规公交演化特点进行分类分析。研究结果表明: ①城市轨道交通与常规公交之间存在交互胁迫关系, 演变轨迹符合双指数曲线。②不同城市的轨道交通与常规公交在3个维度的演变轨迹上存在不均衡性, 客运总量的拐点出现早于运营线路长度和运营车辆数。③根据拐点的出现情况, 将10个城市分为5类, 其中, 重庆的轨道交通与常规公交在客运总量、线路长度及运营车辆3个维度的拐点出现均较晚, 表明重庆的公交发展模式较为协调, 常规公交在轨道交通的胁迫下仍然保持了一定的竞争力。Abstract: An interactive stress model of urban rail and conventional bus transportation is established based on the interactive stress theory traditionally applied to study the relationship between urbanization level and ecological environment. It is used to reveal the interactive relationship between urban rail and conventional bus transportation systems. Ten cities in China that have operated urban rail transit before Year 2009 are selected for a case study. The interactive stess relationship between urban rail and conventional bus transportation systems among the ten cities is verified through the following three indicators: passenger flow, the length of operating lines, and the number of operating vehicles. Then, the evolution pathways of urban rail transit and conventional public transport in different cities are classified. Study results of this paper show that: ① There is an interactive stress relationship between urban rail and public bus transportation systems, and the evolution trajectory can be best described using the double exponential curve. ② There is an imbalance between rail transit and conventional bus transport in different cities over their evolution pathways measured through the above three indicators. The inflexion point of the total passenger volume occurs earlier than the length of operating lines and the number of operating vehicles. ③ According to inflexion points, ten cities are divided into five categories. The inflexion points of Chongqing in the three dimensions are late, indicating that the development mode of public transport in Chongqing is relatively coordinated. Conventional public transport still maintains certain competitiveness under the stress of rail transit.
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图 1 城市常规公交与轨道交通交互关系双指数曲线
Figure 1. Double exponential curve for interactive stress effect between urban bus and rail transport system
表 1 2009年及2019年城市公共交通发展状况
Table 1. Development of urban public transportation of 2009 and 2019
城市 公共汽(电)车 轨道交通 客运总量/(万人·次) 线路长度/km 运营车辆/辆 客运总量/(万人·次) 线路长度/km 运营车辆/辆 2009年 2019年 2009年 2019年 2009年 2019年 2009年 2019年 2009年 2019年 2009年 2019年 北京 516 517 313 366 18 270 27 632 21 716 23 010 142 268 396 238 228 699 2 014 6 449 长春 63 897 75 194 899 6 405 4 212 5 087 2 954 23 204 39 118 140 895 重庆 150 056 257 088 3 896 29 745 8 077 14 276 4 181 104 187 19 328 136 2 130 大连 96 151 92 212 941 5 492 4 811 5 683 6 885 21 967 87 181 143 628 广州 242 200 224 090 16 604 24 368 10 715 15 388 67 520 330 994 147 523 722 2 854 南京 104 178 91 518 3 119 12 018 6 081 8 700 11 353 115 730 22 394 120 1 736 上海 270 600 208 500 23 033 24 780 16 272 17 899 131 837 388 445 355 705 1 833 5 911 深圳 213 603 158 973 2 270 21 606 11 928 17 110 13 823 203 216 25 316 162 2 598 天津 116 425 111 058 11 832 25 526 7 897 12 746 5 271 54 578 71 231 228 1 220 武汉 165 610 142 954 5 463 9 724 7 241 9 631 1 317 122 373 10 335 48 2 416 
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表 2 城市常规公交与轨道交通客流量交互胁迫双指数函数方程
Table 2. Double exponential function for interactive stress effect on passenger flows of urban bus and rail transport system
城市 交互胁迫函数关系式 m n b a p R2 北京 z=0.993 3-0.171 6(10(y-0.824 6)/0.613 5-0.749 8)2 0.993 3 0.171 6 0.824 6 0.613 5 0.749 8 0.927 2 长春 z=0.026 4-5.830 2 (10(y-0.794 2)/0.278 9-0.630 9)2 0.026 4 5.830 2 0.794 2 0.278 9 0.630 9 0.210 9 重庆 z=0.457 6-0.975 8(10(y-0.584 3)/0.289 0-1.427 1)2 0.457 6 0.975 8 0.584 3 0.289 0 1.427 1 0.944 4 大连 z=0.174 0-0.909 0(10(y-0.663 7)/0.433 2-0.213 6)2 0.174 0 0.909 0 0.663 7 0.433 2 0.213 6 0.621 7 广州 z=0.423 5-0.016 7(10(y-0.679 3)/0.337 9-1.970 4)2 0.423 5 0.016 7 0.679 3 0.337 9 1.970 4 0.767 3 南京 z=0.104 5-0.032 4(10(y-0.476 3)/0.375 9-0.477 2)2 0.104 5 0.032 4 0.476 3 0.375 9 0.477 2 0.846 1 上海 z=0.479 3-0.021 4 (10(y-0.751 3)/0.492 2-0.523 9)2 0.479 3 0.021 4 0.751 3 0.492 2 0.523 9 0.950 0 深圳 z=0.357 7-0.232 3(10(y-0.741 9)/0.540 7-0.231 6)2 0.357 7 0.232 3 0.741 9 0.540 7 0.231 6 0.853 1 天津 z=0.175 6-1.565 0(10(y-0.633 4)/0.187 0-1.458 0)2 0.175 6 1.565 0 0.633 4 0.187 0 1.458 0 0.803 7 武汉 z=0.175 6-3.070 9(10(y-0.856 7)/0.156 7-1.040 8)2 0.175 6 3.070 9 0.856 7 0.156 7 1.040 8 0.577 9
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表 3 城市常规公交与轨道交通运营线路长度交互胁迫双指数函数方程
Table 3. Double exponential function for interactive stress effect on the length of operating lines of urban bus and rail transport system
城市 交互胁迫函数关系式 m n b a p R2 北京 z=1.349 0-0.511 5(10(y-1.114 7)/1.234 4-1.513 3)2 1.349 0 0.511 5 1.114 7 1.234 4 1.513 3 0.947 1 长春 z=1.116 0-1.055 7(10(y-0.647 6)/2.596 7-1.104 1)2 1.116 0 1.055 7 0.647 6 2.596 7 1.104 1 0.592 7 重庆 z=1.506 0-1.209 7(10(y-0.591 7)/1.513 3-1.211 9)2 1.506 0 1.209 7 0.591 7 1.513 3 1.211 9 0.827 2 大连 z=0.689 2-0.891 0(10(y-0.768 0)/1.323 4-1.003 1)2 0.689 2 0.891 0 0.768 0 1.323 4 1.003 1 0.753 7 广州 z=6.349 1-2.382 5(10(y-0.823 2)/14.205 2-1.585 1)2 6.349 1 2.382 5 0.823 2 14.205 2 1.585 1 0.600 1 南京 z=0.387 3-0.781 2(10(y-0.789 0)/0.557 0-0.910 2)2 0.387 3 0.781 2 0.789 0 0.557 0 0.910 2 0.822 1 上海 z=0.934 3-0.213 1 (10(y-1.317 0)/1.841 5-0.984 7)2 0.934 3 0.213 1 1.317 0 1.841 5 0.984 7 0.942 2 深圳 z=0.745 9-1.641 3(10(y-0.738 9)/0.396 4-1.051 1)2 0.745 9 1.641 3 0.738 9 0.396 4 1.051 1 0.751 7 天津 z=1.592 0-1.469 5(10(y-0.579 9)/1.187 1-1.192 8)2 1.592 0 1.469 5 0.579 9 1.187 1 1.192 8 0.906 3 武汉 z=0.351 7-0.498 4(10(y-0.935 2)/0.679 1-0.782 7)2 0.351 7 0.498 4 0.935 2 0.679 1 0.782 7 0.824 5
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表 4 城市常规公交与轨道交通运营车辆交互胁迫双指数函数方程
Table 4. Double exponential function for interactive stress effect on the number of operating vehicles of urban bus and rail transport system
城市 交互胁迫函数关系式 m n b a p R2 北京 z=0.887 8-4.965 2(10(y-0.981 9)/0.818 4-1.137 8)2 0.887 8 4.965 2 0.981 9 0.818 4 1.137 8 0.669 3 长春 z=4.106 2-1.907 1(10(y-0.657 8)/11.448 1-1.468 7)2 4.016 2 1.907 1 0.657 8 11.448 1 1.468 7 0.502 2 重庆 z=0.484 8-1.466 6(10(y-0.638 4)/0.400 3-1.067 4)2 0.484 8 1.466 6 0.638 4 0.400 3 1.067 4 0.884 3 大连 z=0.084 6-0.768 1(10(y-0.762 3)/0.389 9-0.718 6)2 0.084 6 0.768 1 0.762 3 0.389 9 0.718 6 0.773 5 广州 z=0.534 0-0.877 7(10(y-0.787 2)/0.579 5-0.877 0)2 0.534 0 0.877 7 0.787 2 0.579 5 0.877 0 0.980 6 南京 z=0.225 5-1.254 5(10(y-0.654 3)/0.410 7-0.906 3)2 0.225 5 1.254 5 0.654 3 0.410 7 0.906 3 0.922 4 上海 z=0.746 2-1.515 2(10(y-2.408 9)/0.341 9-0.364 2)2 0.746 2 1.515 2 2.408 9 0.341 9 0.364 2 0.563 9 深圳 z=0.603 4-0.935 5(10(y-0.737 0)/0.512 4-0.864 5)2 0.603 4 0.935 5 0.737 0 0.512 4 0.864 5 0.914 1 天津 z=0.432 1-2.724 1(10(y-0.602 0)/0.376 7-1.032 1)2 0.432 1 2.724 1 0.602 0 0.376 7 1.032 1 0.835 6 武汉 z=0.258 4-0.694 1(10(y-0.785 1)/0.457 6-0.774 7)2 0.258 4 0.694 1 0.785 1 0.457 6 0.774 7 0.976 9
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表 5 拐点分类情况
Table 5. Classification of inflection points
分类情况 城市 拐点较早 拐点较晚 未有拐点 Ⅰ 北京 客运总量 运营车辆 线路长度 南京 客运总量 运营车辆 线路长度 天津 客运总量 运营车辆 线路长度 Ⅱ 大连 客运总量 线路长度、运营车辆 广州 客运总量 线路长度、运营车辆 上海 客运总量 线路长度、运营车辆 Ⅲ 武汉 客运总量 线路长度、运营车辆 长春 客运总量 线路长度、运营车辆 Ⅳ 深圳 客运总量 线路长度 运营车辆 Ⅴ 重庆 客运总量、线路长度、运营车辆
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