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摘要:
城市交通出行选择多模式条件下多种出行方式之间的出行需求关系密切，为了探讨城市居民对地铁、公交车、私家车3种出行方式出行需求之间的耦合关系及时变特征，采用时变参数向量自回归（TVP-VAR）模型，使用道路拥堵指数近似代表私家车出行量，对2个小区一个月内工作日时间地铁出行量、公交车出行量和道路拥堵指数三者的平均值进行分析。研究结果表明：地铁出行量、公交车出行量、私家车出行量三者相互作用关系针对不同的用地类型，整体影响趋势不会变化太大，影响大小在时间上存在差异；城市交通小区内居民对地铁、公交车、私家车3种出行方式的出行需求之间全天存在相互作用关系，地铁出行需求的增加会减少公交车出行需求，公交车出行需求的增加会增加地铁出行需求，而私家车出行需求的增加会增加地铁出行需求并减少公交车出行需求。研究结果有助于深入认识现阶段中国城市多模式交通出行方式之间的耦合关系，以更好地应对城市交通拥堵问题。
Abstract:Under the condition of multimodal travel choices in large cities, there is a close relationship of traffic demands among subway, buses and private cars. In order to explore the coupling relationship and time-varying feature among multimodal traffic demands, with road congestion index representing the traffic demands of private cars, this paper adopts the time-varying parameter vector autoregressive (TVP-VAR) model to analyze one-month travel demands of subway, buses and road congestion index on weekdays. The empirical results of two traffic zones demonstrate that:Depending on different land use types, the relationship among subway demand, bus demand and private car demand does not significantly change in trend, but varies in time scale; In urban traffic zones, there is an interactive relationship among traffic demands of subway, buses and private cars. As subway demand increases, less passengers will switch to buses. When bus demand increases, subway demand will increase correspondingly. The increase of private car travel demand will increase the subway travel demand but reduce the bus travel demand. The research is helpful to understand the coupling relationship among different travel modes in our country at the present stage, and thus to better cope with the issue of urban traffic congestion.
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Key words:
- traffic demands /
- multimodal travel choices /
- time-varying analysis /
- coupling influence /
- TVP-VAR model
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图 5 小区a不同滞后时间下的时变脉冲响应
Figure 5. Time-varying impulse response of Zone a for different lag time
图 6 小区b不同滞后时间下的时变脉冲响应
Figure 6. Time-varying impulse response of Zone b for different lag time
表 1 小区a的单位根检验
Table 1. Unit root test of Zone a
差分阶数 1%显著水平下的临界值 ADF检验值 地铁出行量 公交车出行量 道路拥堵指数 原始水平 -3.540 198 -1.744 661 -1.922 983 -2.712 431 一阶差分 -3.544 063 -3.468 943 -2.889 990 -3.305 034 二阶差分 -3.546 099 -6.302 398 -5.330 916 -6.655 348 
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表 2 小区b的单位根检验
Table 2. Unit root test of Zone b
差分阶数 1%显著水平下的临界值 ADF检验值 地铁出行量 公交车出行量 道路拥堵指数 原始水平 -3.541 098 -3.300 278 -3.174 048 -3.350 398 一阶差分 -3.544 210 -3.075 129 -2.977 441 -2.704 181 二阶差分 -3.542 079 -6.862 239 -6.738 689 -13.140 510
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表 3 小区a的协整关系检验
Table 3. Co-integration test of Zone a
协整关系方程个数 特征根 迹统计量 最大特征值统计量 检验值 5%显著水平下的临界值 P值 检验值 5%显著水平下的临界值 P值 无 0.320 949 34.334 680 29.797 070 0.014 0 24.384 740 21.131 620 0.016 8 至多1个 0.107 859 9.949 939 15.494 710 0.284 6 7.190 264 14.264 600 0.466 9 至多2个 0.042 859 2.759 675 3.841 466 0.096 7 2.759 675 3.841 466 0.096 7
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表 4 小区b的协整关系检验
Table 4. Co-integration test of Zone b
协整关系方程个数 特征根 迹统计量 最大特征值统计量 检验值 5%显著水平下的临界值 P值 检验值 5%显著水平下的临界值 P值 无 0.775 583 117.285 700 29.797 070 0.000 0 94.137 620 21.131 620 0.000 0 至多1个 0.285 834 23.148 040 15.494 710 0.002 9 21.208 330 14.264 600 0.003 4 至多2个 0.030 320 1.939 713 3.841 466 0.163 7 1.939 713 3.841 466 0.163 7
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