考虑信息影响的理性疏忽出行方式选择行为模型

赵传林; 孙淑敏; 贺少松; 王钰涵

doi:10.3963/j.jssn.1674-4861.2023.06.011

考虑信息影响的理性疏忽出行方式选择行为模型

doi: 10.3963/j.jssn.1674-4861.2023.06.011

赵传林^1, ,,
孙淑敏^{1, 2},
贺少松¹,
王钰涵¹

1.
北京建筑大学土木与交通工程学院北京 102616
2.
山东工业职业学院山东淄博 255000

基金项目:

国家自然科学基金项目 71971014

北京市教委科技计划项目 KM202010016007

北京建筑大学金字塔人才培养工程项目 JDJQ20200301

详细信息

通讯作者:
赵传林（1987—），博士，副教授.研究方向：交通运输规划与管理. E-mail: zhaochuanlin@bucea.edu.cn

中图分类号: U491.1+2
计量
- 文章访问数: 225
- HTML全文浏览量: 121
- PDF下载量: 28
- 被引次数: 0
出版历程
- 收稿日期: 2023-03-14
- 网络出版日期: 2024-04-03

A Model of Rationally Inattentive Travel Mode Choice Behavior Considering the Influence of Information

1.
School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
2.
Shandong Vocation College of Industry, Zibo 255000, Shandong, China

摘要

摘要: 出行方式选择模型多以理性人为假设，但在当前信息爆炸时代，各类信息为出行决策带来便利的同时也带来了干扰，以往研究未能充分揭示人们有限的信息处理能力对于出行方式选择行为的影响。从实验和理论角度展开研究。设计了1个包含多类信息购买决策过程的出行方式选择行为实验，经过30轮重复实验，验证了出行者决策的理性疏忽特性，即被试者并不会关注所有的出行信息，发现被试者对上1轮每种出行方式选择的人数关注度较高，但对下1轮预测性信息关注度较低。考虑出行方式交通状态的随机性和出行者信息处理能力的有限性，定义了每种出行方式的出行成本函数，建立了以信息成本与期望出行成本之和最小为目标的单人出行方式选择模型，模型中采用香农熵描述信息量并引入单位信息成本来表示出行者的信息处理能力。进而给出了出行方式选择的多人博弈均衡条件，采用逐次平均法对模型进行求解，数值算例进一步说明了模型的性质。结果表明：相比于预测性信息，出行者更关注经验性信息；单位信息成本无穷大时，出行者为无信息出行，方式选择倾向于初始偏好；随着单位信息成本的减小，2种出行方式选择概率的差值变大，方式选择倾向更加明确；当某个交通状态发生的概率较小或者较大时，出行者为获取信息所付出的成本均较小。
- 城市交通 /
- 出行方式选择 /
- 理性疏忽 /
- 交通信息 /
- 实验研究
Abstract: Most travel mode choice models are developed based on rational assumptions, but in the current era of information explosion, various types of information bring both convenience and interference to travel decisions. Previous studies have not fully revealed the impact of human's limited information processing capacity on travel mode choice behavior. This research is conducted from both experimental and theoretical perspectives. A travel mode choice behavior experiment is designed including a decision-making process involving multiple types of information. After thirty rounds of repeated experiments, the rational inattention characteristic of traveler decision-making was verified, that is, the subjects did not pay attention to all travel information. It was found that the subjects paid higher attention to the number of people choosing each travel mode in the previous round, but lower attention to the predictive information in the next round. Considering the randomness of transportation status and the limited information processing capacity of travelers, a travel cost function for each travel mode is defined, and a travel mode choice model is established for individual traveler with the goal of minimizing the sum of information cost and expected travel cost. Shannon entropy is used to describe the amount of information and introduces unit information cost to represent the information processing capacity of travelers. Furthermore, the equilibrium conditions of the multiplayer game for travel mode choice were provided, and the model was solved using the method of successive averages (MSA). Numerical examples further demonstrated the properties of the model. The results indicate that travelers pay more attention to empirical information than to predictive information; When the unit information cost is infinite, travelers travel without information, and their choice of mode tends to be based on initial preferences; As the unit information cost decreases, the difference in the probability of choosing two modes becomes larger, and the tendency of mode choice becomes more clear; When the probability of a certain traffic state occurring is smaller or higher, the cost for travelers to obtain information is relatively small.
- urban traffic /
- travel mode choice /
- rational inattention /
- traffic information /
- experimental study

HTML全文

图 1 2种情景下被试人员的个人总出行成本

Figure 1. Total travel cost of each player under two scenarios

下载: 全尺寸图片幻灯片

图 2 出行者选择出行方式的无条件概率

Figure 2. Unconditional probability of travelers' travel modes

下载: 全尺寸图片幻灯片

图 3 系统总成本和个人总出行成本

Figure 3. Total system cost and individual total travel cost

下载: 全尺寸图片幻灯片

图 4 λ = 10时出行者不同道路拥挤概率下的信息成本

Figure 4. Information cost of traveler under different congestion probabilities(λ = 10)

下载: 全尺寸图片幻灯片

表 1 4个时刻交通系统状态对应的概率

Table 1. Probability corresponding to two traffic system states at four different times 单位: %

各时刻交通系统状态	06：00	07：00	08：00	09：00
ω₁	10	40	70	90
ω₂	90	60	30	10

下载: 导出CSV

表 2 λ = 10时出行者出行方式选择概率

Table 2. Probability of traveler's travel mode choice (λ = 10) 单位: %

出发时刻	p(a₁)	p(a₂)
06：00	63	37
07：00	54	46
08：00	49	51
09：00	46	54

下载: 导出CSV

参考文献(21)

[1]	BHAT C R. Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling[J]. Transportation Research Part A: Policy and Practice, 1998, 32 (7): 495-507. doi: 10.1016/S0965-8564(98)00011-1
[2]	BHAT C R, SARDESAI R. The impact of stop-making and travel time reliability on commute mode choice[J]. Transportation Research Part B: Methodological, 2014, 40(9): 709-730.
[3]	LU X S, LIU T L, HUANG H J. Pricing and mode choice based on nested Logit model with trip-chain costs[J]. Transport Policy, 2015, 44: 76-88. doi: 10.1016/j.tranpol.2015.06.014
[4]	郝小妮, 石文瀚, 刘建荣, 等. 基于随机系数Logit模型的城市群城际出行方式选择行为研究[J]. 交通信息与安全, 2022, 40(5): 139-146. doi: 10.3963/j.jssn.1674-4861.2022.05.015 HAO X N, SHI W H, LIU J R, et al. An Analysis of mode choice behavior of inter-city travel in urban agglomeration areas using a random-parameter Logit model[J]. Journal of Transport Information and Safety, 2022, 40(5): 139-146. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.05.015
[5]	MAHMASSANI H S, CHANG G L. On boundedly rational user equilibrium in transportation systems[J]. Transportation Science, 1987, 21(2): 89-99. doi: 10.1287/trsc.21.2.89
[6]	田丽君, 杨茜, 黄海军, 等. 基于累积前景理论的出行方式选择模型及实证[J]. 系统工程理论与实践, 2016, 36(7): 1778-1785. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201607015.htm TIAN L J, YANG Q, HUANG H J, et al. The cumulative prospect theory-based travel mode choice model and its empirical verification[J]. Systems Engineering-Theory and practice, 2016, 36(7): 1778-1785. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201607015.htm
[7]	田丽君, 吕成锐, 黄文彬. 基于累积前景理论的合乘行为建模与研究[J]. 系统工程理论与实践, 2016, 36(6): 1576-1584. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201606023.htm TIAN L J, LYU C R, HUANG W B. Modelling carpooling behavior based on cumulative prospect theory[J]. Systems Engineering-Theory and practice, 2016, 36(6): 1576-1584. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201606023.htm
[8]	张宏刚, 王伟, 潘敏荣, 等. 考虑有限理性和公平性的危险品运输网络优化[J]. 交通信息与安全, 2022, 40(4): 38-45. doi: 10.3963/j.jssn.1674-4861.2022.04.004 ZHANG H G, WANG W, PAN M R, et al. Optimization of the transportation network of hazardous materials considering bounded rationality and equity[J]. Journal of Transport Information and Safety, 2022, 40(4): 38-45. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.04.004
[9]	MATĚJKA F, MCKAY A. Rational inattention to discrete choices: a new foundation for the multinomial Logit model[J]. American Economic Review, 2015, 105(1): 272-298. doi: 10.1257/aer.20130047
[10]	JIANG G, FOSGERAU M, LO H K. Route choice, travel time variability, and rational inattention[J]. Transportation Research Part B: Methodological, 2020, 132: 188-207. doi: 10.1016/j.trb.2019.05.020
[11]	SIMS C A. Implications of rational inattention[J]. Journal of Monetary Economics, 2003, 50: 665-690. doi: 10.1016/S0304-3932(03)00029-1
[12]	石小法, 王炜, 杨东援. 信息对出行者出行行为的影响研究[J]. 中国公路学报, 2002, 15(1): 92-95. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL200201021.htm SHI X F, WANG W, YANG D Y. Study of the effect of information on the travelers' behavior[J]. China Journal of Highway and Transport, 2002, 15(1): 92-95. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL200201021.htm
[13]	李志纯, 黄海军. 多目标路径诱导下平衡市场渗透率的确定[J]. 系统工程理论与实践, 2004, 24(9): 125-130. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL200409021.htm LI Z C, HUANG H J. Determination of equilibrium market penetration under multi-criteria route guidance systems[J]. Systems Engineering-Theory and Practice, 2004, 24(9): 125-130. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL200409021.htm
[14]	李志纯, 黄海军. 先进的旅行者信息系统对出行者选择行为的影响研究[J]. 公路交通科技, 2005, 22(2): 95-99. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200502026.htm LI Z C, HUANG H J. Modeling the impacts of advanced traveler information systems on travelers' travel choice behaviors[J]. Journal of Highway and Transportation Research and Development, 2005, 22(2): 95-99. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200502026.htm
[15]	李嫚嫚, 陆建, 孙加辉. 多类型信息下的网络交通流演化模型[J]. 交通运输系统工程与信息, 2020, 20(4): 97-105. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202004015.htm LI M M, LU J, SUN J H. Day-to-day traffic assignment model with multiple types of Information[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 97-105. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202004015.htm
[16]	ARNOTT R, DE PALMA A, LINDSEY R. Does providing information to drivers reduce traffic congestion?[J]. Transportation Research Part A: General, 1991, 25(5): 309-318. doi: 10.1016/0191-2607(91)90146-H
[17]	LINDSEY R, DANIEL T, GISCHES E, et al. Pre-trip information and route-choice decisions with stochastic travel conditions: theory[J]. Transportation Research Part B: Methodological, 2014, 67(1): 187-207.
[18]	RAPOPORT A, GISCHES E J, DANIEL T, et al. Pre-trip information and route-choice decisions with stochastic travel conditions: Experiment[J]. Transportation Research Part B: Methodological, 2014, 68: 154-172. doi: 10.1016/j.trb.2014.06.007
[19]	刘天亮, 张冲, 王天歌, 等. 朋友圈信息交互对个体出行决策行为的影响研究[J]. 交通运输系统工程与信息, 2013, 13 (6): 86-93. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201306014.htm
[20]	高峰, 王明哲. 诱导信息下的路径选择行为模型[J]. 交通运输系统工程与信息, 2010, 10(6): 64-69. doi: 10.3969/j.issn.1009-6744.2010.06.009 GAO F, WANG M Z. Route choice behavior model with guidance information[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(6): 64-69. (in Chinese) doi: 10.3969/j.issn.1009-6744.2010.06.009
[21]	ZHANG Q R, MA S F, TIAN J F, et al. Mode choice between autonomous vehicles and manually-driven vehicles: An experimental study of information and reward[J]. Transportation Research Part A: Policy and Practice, 2022, 157: 24-29. doi: 10.1016/j.tra.2022.01.011