基于MA-CPT模型的出行方式选择模型优化

doi:10.3969/j.issn.0258-2724.20210938

黄奕慧^1,,
杨飞^1,,,
张栋²,
曾优美¹

1.
江南娱乐网页版入口官网下载安装交通运输与物流学院，四川成都 611756
2.
大连理工大学交通运输学院，辽宁大连 116024

基金项目:国家自然科学基金（52072313）

详细信息

作者简介:
黄奕慧（1992—），女，博士研究生，研究方向为交通出行行为，E-mail：huangyihui_jt@163.com

通讯作者:
杨飞（1980—），男，教授，博士，研究方向为交通行为，智能交通技术等，E-mail：yangfei_traffic@163.com

中图分类号:U491.1
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- 被引次数:0
出版历程
- 收稿日期:2021-11-17
- 修回日期:2022-10-26
- 网络出版日期:2022-12-19
- 刊出日期:2022-11-03

Optimization of Travel Mode Choice Based on MA-CPT Model

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
2.
School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China

摘要

摘要:
为提高累积前景理论在出行方式选择建模应用中的准确性，考虑个体对于时间与费用货币态度的不同，优化原始累积前景理论（cumulative prospect theory，CPT）模型的时间值函数和时间权重函数. 首先，针对时间压力下出行方式偏好发生转变的现象，将出行方式分为刚性出行和弹性出行，改进刚性出行情景下的时间值函数形式，并根据出行时间特性求出时间权重函数中吸引力参数的取值范围，构建MA-CPT （mental accounting-cumulative prospect theory）模型；其次，根据实证数据标定时间权重函数中辨别力参数和吸引力参数的取值；最后，标定 MA-CPT模型结果并检验其拟合优度，对比MA-CPT模型和CPT模型的命中率. 实证分析结果表明：刚性出行和单行出行场景下，时间权重函数的吸引力参数值均大于1.00；MA-CPT模型在刚性出行和弹性出行情境下的拟合优度分别为0.17和0.18；相比于CPT模型，MA-CPT模型在弹性出行和刚性出行情景下的命中率分别提高了12.2%和19.8%.
- 城市交通/
- 出行方式选择模型/
- 累积前景理论/
- 心理账户理论/
- 时间权重函数
Abstract:
To improve the accuracy of cumulative prospect theory in modeling travel mode choice, given the differences in individual attitudes toward time and costs, the time value function and time weight function of the original cumulative prospect theory model are optimized. Firstly, in view of the preference reversal under time pressure, the travel mode is divided into rigid travel and flexible travel, the form of the time value function under the rigid travel scenario is improved, and the value range of the attraction parameter in the time weight function is calculated according to the travel time characteristics, and the MA-CPT (mental accounting-cumulative prospect theory) model is constructed. Secondly, the values of discrimination and attractiveness parameters in the time weight function are calibrated according to empirical data. Finally, the MA-CPT model is calibrated and its goodness of fit is tested, and the hit rates of MA-CPT and CPT models are compared. The empirical analysis results show that the attraction parameter value of the time weight function is greater than 1.00 under the scenarios of rigid travel and one-way travel. The goodness of fit of the MA-CPT model under rigid travel and flexible travel scenarios are 0.17 and 0.18, respectively. Compared with the CPT model, the hit rate of the MA-CPT model under flexible travel and rigid travel scenarios has increased by 12.2% and 19.8%, respectively.
- urban traffic/
- travel mode choice model/
- cumulative prospect theory/
- mental accounting theory/
- time weight function

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图 1时间值函数示意

Figure 1.Schematic of time value function

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图 2权重函数示意

Figure 2.Schematic of time weight function

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图 3实际时间权重函数

Figure 3.Actual time weight function

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表 1选择肢属性及属性水平

Table 1.Alternative attributes and attribute levels

选择肢	属性	水平 1	水平 2	水平 3	水平4
方式 1	行程时间/min	25	50
	费用/元	10	30
	时间风险/%	30	40	60	70
方式 2	行程时间/min	30	40
	费用/元	2	5
	时间风险/%	30	40	60	70

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表 2个体属性特征统计

Table 2.Statistics of individual attribute characteristics

个体属性		占比%
性别	女	55
性别	男	45
年龄	< 30 岁	9
	[30 岁，40 岁）	27
	[40 岁，50 岁）	16
	[50 岁，60 岁）	35
	≥ 60 岁	10
受教育程度	高中及以下	43
	专科及本科	45
	硕士及以上	12
月收入	< 2000 元	19
	[2000，4000）元	21
	[4000，6000）元	35
	[6000，8000）元	17
	≥ 8000 元	8

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表 3时间权重函数的参数标定结果

Table 3.Parameter calibration results of time weight function

出行情景	值函数收益、损失情况	δ	α
刚性出行	收益	0.72	1.19
刚性出行	损失	0.76	1.21
弹性出行	收益	0.61	1.67
弹性出行	损失	0.64	1.92

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表 4MA-CPT模型的参数估计结果

Table 4.Parameter estimation results of MA-CPT model

出行情景	选择方式	属性	参数值	T值	拟合优度
刚性出行	方式 1	费用	0.17309	6.98*	0.17
	方式 1	时间	0.01822	3.58*
	方式 2	费用	0.06865	−1.38
	方式 2	时间	0.11086	4.25*
弹性出行	方式 1	费用	0.16611	6.09*	0.18
	方式 1	时间	0.34679	1.24
	方式 2	费用	0.08255	−1.93
	方式 2	时间	0.41851	1.39
注：*表示在10%的水平上显著.

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表 5CPT与MA-CPT命中率对比

Table 5.Comparison of hit rates between CPT and MA-CPT %

出行情景	CPT	MA-CPT
刚性出行	72.6	92.4
弹性出行	64.5	76.7

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