公共交通票价对通勤走廊出行结构的影响

姚恩建; 张茜; 张锐

公共交通票价对通勤走廊出行结构的影响

姚恩建^{1, 2,},
张茜²,
张锐²

1.
北京交通大学城市交通复杂系统理论与技术教育部重点实验室, 北京 100044
2.
北京交通大学交通运输学院, 北京 100044

基金项目:

北京市自然科学基金项目 8171003

中央高校基本科研业务费专项资金项目 2016YJS090

详细信息

作者简介:
姚恩建(1971-), 男, 贵州遵义人, 北京交通大学教授, 工学博士, 从事交通运输规划与管理研究

中图分类号: U491
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出版历程
- 收稿日期: 2017-07-18
- 刊出日期: 2017-12-25

Impact of public transport fare on travel mode structure of commuting corridor

1.
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
2.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

More Information

Author Bio:
YAO En-jian(1971-), male, professor, PhD, enjyao@bjtu.edu.cn

摘要

摘要: 针对通勤走廊构建交通方式选择模型, 分析公共交通票价对通勤走廊出行结构的影响; 考虑公共交通多种末端衔接方式, 针对有车和无车通勤者, 分别构建了包含组合出行模式的出行方式选择NL模型; 以北京市地铁5号线沿线通勤走廊为例, 结合实际数据对NL模型精度进行检验; 在考虑公共交通运能约束的基础上, 应用NL模型分析不同单一交通方式票价、组合票价下, 各出行方式分担率与公共交通满载率的变化, 从公共交通运能利用率和服务水平的角度研究不同票价下出行结构的合理性。研究结果表明: 北京市票价调整政策可以提高地面公交运能利用率, 但对降低地铁满载率的作用有限; 若将地面公交票价调整为0.01~0.06元·km^-1或地铁票价调整为0.32~0.42元·km^-1时, 通勤走廊内地面公交的高峰小时平均满载率为60%~65%, 北京市地铁5号线高峰小时满载率为86%~100%, 公共交通运能利用率保持在一定水平的同时, 提高了公共交通服务水平, 且小汽车的分担率基本保持现状, 未造成小汽车出行量的激增; 当地面公交和地铁票价同时满足限制条件时, 通勤走廊内地面公交的高峰小时平均满载率为58%~80%, 北京市地铁5号线高峰小时满载率为86%~100%, 小汽车分担率的增幅在5%以下, 票价调整可以改善通勤走廊的出行结构。
- 交通规划 /
- 出行结构 /
- NL模型 /
- 公共交通票价 /
- 通勤走廊 /
- 满载率 /
- 分担率
Abstract: Traffic mode choice models for commuting corridor were established, and the impact of public transport fare on the travel mode structure of commuting corridor was analyzed.Considering the multiple end access modes of public transport, the travel mode choice NL models containing combined travel mode were proposed respectively for commuters with or without cars.The commuting corridor along Subway Line 5 in Beijing was taken as an example, and the precisions of NL models were examined with actual data.On the basis of the consideration of public transport capacity constraint, the travel mode share rate and public transport load factors with different single traffic mode fares and combined fares were analyzed by using NL models, the rationalities of travel mode structure under different fares were evaluated according to capacityutilization rate and service level of public transport.Analysis result shows that the fare adjustment policy in Beijing can improve the capacity utilization rate of bus, but the effect on load factors of subway is limited.When the bus fares are 0.01-0.06 yuan·km^-1, or the subway fares are 0.32-0.42 yuan·km^-1 in the commuting corridor, the average peak hour load factors of bus are 60%-65%, and the peak hour load factors of Subway Line 5 in Beijing are 86%-100%, which shows that while the capacity utilization rate of public transport is kept in a certain level, the service level of public transport is improved, the share rate of car remains the status quo, and car trip volumes don't increase greatly.When the constraints of bus fare and subway fare are satisfied in the commuting corridor, the average peak hour load factors of bus are 58%-80%, the peak hour load factors of Subway Line 5 in Beijing are 86%-100%, and the growth rate of share rate of car is less than 5%.The fare adjustment can improve the travel mode structure of commuting corridor.
- traffic planning /
- travel mode structure /
- NL model /
- public transport fare /
- commuting corridor /
- load factor /
- share rate

HTML全文

图 1 NL模型结构

Figure 1. Structure of NL models

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图 2 通勤走廊吸引范围

Figure 2. Attracting scope of commuting corridor

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图 3 交通小区划分

Figure 3. Division of traffic zones

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图 4 不同地面公交票价下分担率变化曲线

Figure 4. Changing curves of share rates under different bus fares

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图 5 不同地铁票价下分担率变化曲线

Figure 5. Changing curves of share rates under different subway fares

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图 6 不同地面公交票价下高峰小时满载率变化曲线

Figure 6. Changing curves of peak hour load factors under different bus fares

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图 7 不同地铁票价下高峰小时满载率变化曲线

Figure 7. Changing curves of peak hour load factors under different subway fares

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图 8 地铁分担率

Figure 8. Share rate of subway

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图 9 地面公交分担率

Figure 9. Share rate of bus

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图 10 小汽车分担率

Figure 10. Share rate of car

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图 11 地铁高峰小时满载率

Figure 11. Peak hour load factor of subway

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图 12 地面公交高峰小时平均满载率

Figure 12. Average peak hour load factor of bus

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表 1 NL模型参数标定结果

Table 1. Parameter calibration result of NL models

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表 2 地铁高峰小时满载率

Table 2. Peak hour load factors of subway

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表 3 各主要交通方式分担率

Table 3. Share rates of main traffic modes

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表 4 地铁、地面公交满载率

Table 4. Load factors of subway and bus %

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表 5 拥挤程度评价原则

Table 5. Evaluation principle of congestion degree

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