Route Choice Behavior of Urban Rail Transit Passengers
Based on Guidance Information

doi:10.16097/j.cnki.1009-6744.2022.03.008

Abstract

Abstract: To explore how to provide guidance information for urban rail transit passengers, this paper considered the interaction between path attributes with the time and ways of information provision and update rate, and it proposed a framework for passenger route choice behavior based on guidance information. An improved mixed utility-regret model was established, in which the differences in the wishes of passengers to follow the mixed decision rules and the perception of each attribute are taken into account. The route attributes and guidance information are both incorporated into the scenario design, and the key parameters in the model were estimated based on passengers' route choice behavior survey. The results show that the mixed decision rule has resulted in a high fit degree (where the adjusted goodness ratio reached 0.396). The analysis on passengers' preferences for information provision indicates that, compared to downloading the mobile APP, passengers prefer to receive guidance information through social media. By introducing the time value of the information update rate, it is shown that female, middle- aged, and non- commuter passengers are more likely to accept higher frequency guidance information services. Based on the elastic value of each attribute, it indicates that the way of providing information and the update rate of information should be used as an auxiliary means for managers, and the information provision can play a greater role in specific scenarios (such as peak congestion, emergencies). These research results reveal how the way of guidance information provision affects the route choice of passengers, which is helpful to design accurate and efficient guidance information strategies for the organization and management of passenger flow.

Key words: urban traffic, guidance information, mixed utility- regret model, route choice, time value of information push frequency

摘要： 为研究城市轨道交通诱导信息对乘客的影响，考虑信息提供时间、方式、更新频率及其与路径属性之间的交互关系，提出融合诱导信息的城市轨道交通乘客路径选择行为决策框架。考虑乘客在决策时表现出遵循混合决策规则的倾向差异和不同属性感知差异，建立改进的混合效用-后悔模型。将路径属性与诱导信息同时纳入调研情景设计，基于调研结果标定模型中关键参数，结果表明，基于混合决策规则的模型拟合效果最高(调整优度比达0.396)。进一步分析乘客对信息的偏好：相较于下载手机APP，乘客更喜欢社交媒体推送的形式接收诱导信息；引入信息推送频率时间价值，并对比得出，女性、中老年及非通勤乘客更倾向于接受更高频率的诱导信息推送服务；分析各属性弹性值，表明诱导信息提供方式和更新频率应作为管理者的辅助手段，在特定场景下(例如高峰拥堵和突发事故)诱导信息将会发挥更大的作用。研究揭示了诱导信息提供方式如何影响轨道交通乘客的路径决策机理，有助于设计更加精准和高效的诱导信息策略辅助客流组织管理。

关键词: 城市交通, 诱导信息, 混合效用-后悔模型, 路径选择, 信息提供频率价值

CLC Number:

U121

XU Xin-yue , XIE Lan-shi-yu. Route Choice Behavior of Urban Rail Transit Passengers Based on Guidance Information[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 63-73.

许心越, 谢兰诗雨. 诱导信息下城市轨道交通乘客路径选择行为研究[J]. 交通运输系统工程与信息, 2022, 22(3): 63-73.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.03.008

http://www.tseit.org.cn/EN/Y2022/V22/I3/63

References

[1] ZHAO W, QUDDUS M, HUANG H, et al. Analyzing drivers' preferences and choices for the content and format of variable message signs (VMS) [J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 1-14.

[2] CEDER A A, JIANG Y. Route guidance ranking procedures with human perception consideration for personalized public transport service[J]. Transportation Research Part C: Emerging Technologies, 2020, 118: 102667.

[3] IRAGANABOINA N C, BHOWMIK T, YASMIN S, et al. Evaluating the influence of information provision (when and how) on route choice preferences of road users in Greater Orlando: Application of a regret minimization approach[J]. Transportation Research Part C: Emerging Technologies, 2021, 122: 102923.

[4] ZHU D, NN SZE FENG Z. The trade-off between safety and time in the red light running behaviors of pedestrians: A random regret minimization approach[J]. Accident Analysis & Prevention, 2021, 158(2): 106214.

[5] BEN-ELIA E, AVINERI E. Response to travel information: A behavioural review[J]. Transport Reviews, 2015, 35(3): 352-377.

[6] HAN X, YU Y, GAO Z Y, et al. The value of pre-trip information on departure time and route choice in the morning commute under stochastic traffic conditions[J]. Transportation Research Part B: Methodological, 2021, 152: 205-226.

[7] 贾飞凡, 蒋熙, 李海鹰, 等. 基于强化学习的城轨信息发布策略研究[J]. 交通运输系统工程与信息, 2020, 20 (5): 72- 78. [JIA F F, JIANG X, LI H Y, et al. Information release strategy of urban rail transit based on reinforcement learning[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 72-78.]

[8] MA T Y, PACE R D. Comparing paradigms for strategy learning of route choice with traffic information under uncertainty[J]. Expert Systems with Applications, 2017, 88: 352-367.

[9] CHARONITI E, KIM J, RASOULI S, et al. Intrapersonal heterogeneity in car-sharing decision-making processes by activity-travel contexts: A context-dependent latent class random utility-random regret model[J]. International Journal of Sustainable Transportation, 2021, 15(7): 501-511.

[10] MCFADDEN D. The measurement of urban travel demand [J]. Journal of Public Economics, 1974, 3(4): 303-328.

[11] CHORUS C G, ARENTZE T A, TIMMERMANS H. A random regret-minimization model of travel choice[J]. Transportation Research Part B, 2008, 42(1): 1-18.

[12] WONG S D, CHORUS C G, SHAHEEN S A, et al. A revealed preference methodology to evaluate regret minimization with challenging choice sets: A wildfire evacuation case study[J]. Travel Behaviour and Society, 2020, 20: 331-347.

[13] BOERI M, SCARPA R, CHORUS C G. Stated choices and benefit estimates in the context of traffic calming schemes: Utility maximization, regret minimization, or both?[J]. Transportation Research Part A: Policy and Practice, 2014, 61: 121-135.

[14] CHORUS C G, ROSE J M, HENSHER D A. Regret minimization or utility maximization: It depends on the attribute[J]. Environment and Planning B: Planning and Design, 2013, 40(1): 154-169.

[15] LEONG W, HENSHER D A. Contrasts of relative advantage maximization with random utility maximization and regret minimization[J]. Journal of Transport Economics and Policy (JTEP), 2015, 49(1): 167-186.

[16] CHOWDHURY S, CEDER A A, SCHWALGER B. The effects of travel time and cost savings on commuters' decision to travel on public transport routes involving transfers[J]. Journal of Transport Geography, 2015, 43: 151-159.

[17] KEYA N, ANOWAR S, ELURU N. Freight mode choice: A regret minimization and utility maximization based hybrid model[J]. Transportation Research Record, 2018, 2672(9): 107-119.

[18] BOERI M, SCARPA R, CHORUS C G. Stated choices and benefit estimates in the context of traffic calming schemes: Utility maximization, regret minimization, or both?[J]. Transportation Research Part A: Policy and Practice, 2014, 61: 121-135.

[19] RAMOS G, MAI T, DAAMEN W, et al. Route choice behaviour and travel information in a congested network: Static and dynamic recursive models[J]. Transportation Research Part C: Emerging Technologies, 2020, 114: 681-693.

[20] KUSAKABE T, IRYO T, ASAKURA Y. Estimation method for railway passengers' train choice behavior with smart card transaction data[J]. Transportation, 2010, 37 (5): 731-749.

[21] 孙妍. 考虑有限理性的城市轨道交通通勤客流路径选择研究[D]. 成都: 西南交通大学, 2019. [SUN Y. Study on the path selection of urban rail transit considering the finite rationality [D]. Chengdu: Southwest Jiaotong University, 2019.]

[22] TIRACHINI A, HURTUBIA R, DEKKER T, et al. Estimation of crowding discomfort in public transport: Results from Santiago de Chile[J]. Transportation Research Part A: Policy and Practice, 2017, 103: 311- 326.

[23] AGHABAY K, ESMAILPOUR J, SHIWAKOTI N. Effects of COVID-19 on rail passengers' crowding perceptions[J]. Transportation Research Part A: Policy and Practice, 2021, 154: 186-202.

[24] 刘羽. 突发大客流下城市轨道交通信息服务频率研究 [D]. 北京: 北方工业大学, 2019. [LIU Y. Frequency of urban rail transit information service under outburst mass passenger flow[D]. Beijing: North China University of Technology, 2019.]

[25] SUN H. Observations and analysis of pedestrian walking microparameters in urban rail transit station passageway [J]. Technology & Economy in Areas of Communications, 2017, 19: 33-38.

[26] LI B, YAO E, YAMAMOTO T, et al. Exploring behavioral heterogeneities of metro passenger's travel plan choice under unplanned service disruption with uncertainty[J]. Transportation Research Part A: Policy and Practice, 2020, 141: 294-306.

[27] 王兴川. 基于出行行为分析的突发事件下城轨客流拥挤传播研究[D]. 北京: 北京交通大学, 2020. [WANG X C. Research on passengers' congestion propagation process under urban rail transit emergencies based on travel behavior analysis[D]. Beijing: Beijing Jiaotong University, 2020.]

[28] 王晓易. 调查发现女性玩手机比男性更上瘾 [EB/OL]. (2014-03-06) [2021-12-15]. https://www.163.com/tech/ article/9ML5AG2J000915BE.html. [WANG X Y. The survey found that women are more addicted to playing mobile phones than men [EB/OL]. (2014-03-06) [2021- 12- 15]. https: //www. 163. com/ tech/ article/ 9ML5AG2J000915BE.html.]

[29] ABOLHASSANI N, SANTOS-EGGIMANN B, CHIOLERO A, et al. Readiness to accept health information and communication technologies: A population-based survey of community-dwelling older adults[J]. International Journal of Medical Informatics, 2019, 130: 103950.

[30] 兆雪, 赵文静, 马壮林, 等. 基于VMS信息差异的驾驶人路径选择行为[J]. 长安大学学报(自然科学版), 2020, 40(3): 91-99. [ZHAO X, ZHAO W J, MA Z L, et al. Driver's choice behavior based on variable message sign (VMS) information differences[J]. Journal of Chang'an University (Natural Science Edition), 2020, 40(3): 91-99.]

[31] WANG Z, AN S, WANG J, et al. Evacuation travel behavior in regret minimization or utility maximization rules? Evidence from emergency context[J]. KSCE Journal of Civil Engineering, 2017, 21(1): 440-446.

[32] HENSHER D A, GREENE W H, CHORUS C G. Random regret minimization or random utility maximization: An exploratory analysis in the context of automobile fuel choice[J]. Journal of Advanced Transportation, 2013, 47 (7): 667-678.

[33] ZHANG G, ZHONG W, PERSAD K, et al. Enhanced traffic information dissemination to facilitate toll road utilization: A nested Logit model of a stated preference survey in Texas[J]. Transportation, 2014, 41(2): 231-249.

[34] KHOO H L, ASITHA K S. User requirements and route choice response to smart phone traffic applications (apps) [J]. Travel Behaviour and Society, 2016, 3: 59-70.

Route Choice Behavior of Urban Rail Transit Passengers Based on Guidance Information

诱导信息下城市轨道交通乘客路径选择行为研究

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