城市轨道交通车厢内拥挤成本的估计方法

房德威; 何东坡; 王立峰; 陈曦; 孙祥龙

doi:10.19818/j.cnki.1671-1637.2018.06.013

城市轨道交通车厢内拥挤成本的估计方法

doi: 10.19818/j.cnki.1671-1637.2018.06.013

东北林业大学土木工程学院,黑龙江哈尔滨 150040

基金项目:

国家重点研发计划项目 2017YFC0803901

中央高校基本科研业务费专项资金项目 2572018BJ09

详细信息

作者简介:
房德威 (1978-) , 男, 黑龙江哈尔滨人, 东北林业大学讲师, 工学博士, 从事城市交通规划研究

通讯作者:
何东坡 (1962-) , 男, 黑龙江哈尔滨人, 东北林业大学教授

中图分类号: U293.6
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出版历程
- 收稿日期: 2018-07-28
- 刊出日期: 2018-12-25

Estimation method of crowding cost in urban rail transit carriages

School of Civil Engineering, Northeast Forestry University, Harbin 150040, Heilongjiang, China

More Information

Author Bio:
FANG De-wei(1978-), male, lecturer, PhD, fdw@nefu.edu.cn

HE Dong-po(1962-), male, professor, hdp@nefu.edu.cn

Article Text (Baidu Translation)

摘要

摘要: 应用离散决策分析法与条件价值法, 设计了双边界二分式调研问卷, 计算了考虑拥挤度的轨道交通出行总成本;通过延长车厢内乘客出行时间的方式来换取车厢内乘客密度的减少, 从而得到车厢内不同拥挤度下的等价出行效能;通过调研得到乘客对两轮投标的选择概率;分别采用双变量Probit估计量和随机效用估计量来推断时间边际负效用的标准化值, 得到时间乘数, 进而估计乘客的延时意愿和支付意愿;基于2015~2016年北京地铁1号线和5号线具有代表性的15个车站站台调查获得的数据, 对轨道交通车厢内6种拥挤度下的时间乘数进行线性回归分析。研究结果表明:车厢内的乘客密度和时间乘数存在线性关系, 而改善后的车厢内拥挤度和广义成本呈反比例关系;出行者支付意愿随车厢拥挤度的减少而增加, 当车厢拥挤度由5人·m^-2改善为4人·m^-2时, 早、晚高峰时段的支付意愿分别为1.58元和3.02元, 当车厢拥挤度由5人·m^-2改善为3.5人·m^-2时, 早、晚高峰时段的支付意愿分别为2.47元和4.99元, 因此, 晚高峰时段出行者的平均支付意愿达到早高峰时段的2倍左右, 不同时段出行者对改善拥挤度的支付意愿存在显著差异。
- 轨道交通 /
- 条件价值法 /
- 拥挤度 /
- 拥挤成本 /
- 时间乘数 /
- 出行时间 /
- 支付意愿
Abstract: The discrete choice analysis and contingent valuation method were used to design a double-bounded dichotomous questionnaire to calculate the total generalized cost of rail transit, including the crowding degrees.The passenger densities in the carriage was reduced by extending the travel time of passengers in the carriage, and the equivalent travel efficiencies were obtained under different crowding degrees in the carriage.The passengers' choice probability for two rounds of bidding was obtained from the questionnaire feedback.The bivariate probit estimators and random utility estimators were used to deduce the standardized value of the time marginal disutility, and then the time multiplier was obtained to estimate the passengers' willingness to travel longer and the willingness to pay.Based on the data obtained from the survey of 15 representative platforms of Beijing Metro Lines 1and 5in 2015-2016, a linear regression analysis was made for the time multiplier under six crowding degrees in the rail transit carriage.Analysis result shows that there is a linear relationship between the passenger density in the carriage and the time multiplier, but an inverse proportion between the crowding degrees after improvementand the generalized cost in the carriage.Traveler's willingness to pay increases with the reduction of crowding degree in the carriage.When the crowding degree improves from 5 person·m^-2 to 4 person·m^-2, the willingness to pay for peak hours in the morning and evening is RMB 1.58 and 3.02, respectively.When the crowding degree improves from 5 person·m^-2 to 3.5 person·m^-2, the willingness to pay for peak hours in the morning and evening increase to RMB 2.47 and 4.99, respectively.Therefore, traveler's average payment willingness in the evening peak is approximate twice that in the morning peak, and there are significant differences in the payment willingness for the improved crowding degree at different periods.
- urban rail transit /
- contingent valuation method /
- crowding degree /
- crowding cost /
- time multiplier /
- travel time /
- payment willingness

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图 1 实地调查中使用的卡片

Figure 1. Cards used in field survey

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图 2 投标博弈流程

Figure 2. Flow chart of bidding game

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图 3 出行时间边际负效用

Figure 3. Travel time marginal disutilities

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图 4 时间乘数与乘客密度的关系曲线

Figure 4. Relation curves of time multipliers and passenger densities

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表 1 北京地铁出行特征调研统计结果

Table 1. Survey statistical result of travel characteristics of Beijing metro

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表 2 预期密度比例

Table 2. Expected density proporations

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表 3 密度指标

Table 3. Density indicators

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表 4 对假设场景投标认可度统计

Table 4. Statistics of acceptance rates to bidding on hypothetical scenarios

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表 5 不同拥挤度下出行时间边际负效用的估计值与标准误差

Table 5. Estimators and standard errors of marginal disutility of travel time under different crowding degrees

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表 6 随机效用模型时间乘数估计值

Table 6. Time multiplier estimators of random utility model

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表 7 方差分析结果

Table 7. Results of variance analysis

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表 8 回归模型系数

Table 8. Coefficients of regression model

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表 9 车厢内出行时间与机会成本调查数据

Table 9. Survey data of in-vehicle travel time and opportunity cost

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表 10 0 时间乘数

Table 10. Time multipliers

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表 11 1 密度改善后的延时意愿、支付意愿和公交总成本

Table 11. Delay willingness, payment willingness and total bus costs after density improvement

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