交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (3): 327-337.DOI: 10.16097/j.cnki.1009-6744.2026.03.029

• 系统工程理论与方法 • 上一篇    下一篇

基于社交媒体数据的需求响应公交公众认知与态度研究

胡三根1 ，文炫淇1 ，巫威眺*2 ，李满琳1 ，韩霜1   

  1. 1. 广东工业大学，土木与交通工程学院，广州510006；2.华南理工大学，土木与交通学院，广州510641
  • 收稿日期:2025-10-17 修回日期:2026-03-06 接受日期:2026-04-24 出版日期:2026-06-25 发布日期:2026-06-23
  • 作者简介:胡三根（1989— ），男，江西乐平人，讲师，博士。
  • 基金资助:
    广东省基础与应用基础研究基金(2022A1515110281, 2025B1515020056)。

Public Attention and Attitudes Towards Demand Responsive Transit: AStudy Based on Social Media Data

HU Sangen1, WEN Xuanqi1, WU Weitiao*2, LI Manlin1, HAN Shuang1   

  1. 1. Department of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China; 2. Department of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
  • Received:2025-10-17 Revised:2026-03-06 Accepted:2026-04-24 Online:2026-06-25 Published:2026-06-23
  • Supported by:
    Natural Science Foundation of Guangdong Province,China(2022A1515110281, 2025B1515020056)。

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摘要： 为应对城市交通拥堵与公众出行需求多样化的双重挑战，需求响应公交（Demand Responsive Transit, DRT）作为一种灵活和高效的出行方式备受关注，但其推广受制于公众复杂且不确定的认知。本文旨在剖析公众对DRT认知的核心议题与情感归因。收集抖音和B站两大社交平台2022年7月—2025年7月间的相关文本数据25831条，采用先验框架与吉布斯采样狄利克雷多项式混合模型进行层次化主题挖掘，并应用大语言模型进行情感分析。主题建模识别出“服务体验”“运营效率”“经济性”“其他”四大核心议题及其下8个子主题。结果显示，公众对DRT的整体情感得分为0.37，其中，负面情感分布于服务体验和运营效率等现实运营问题，积极情感主要是对其概念价值的认同。进一步发现，公众情感的区域异质性与DRT在地方交通系统中的角色定位紧密相关。在部分交通发达地区，DRT被视为有益的补充，评价较高；而在以弥补现有公交网络服务短板的地区，其运营表现与高期望之间的显著落差，是导致负面情感更为集中的核心原因。研究结论表明，DRT的推广必须摒弃“一刀切”模式，转向基于地方交通系统差异的精细化战略，并将解决运营层面的核心瓶颈作为其赢得公众信任的首要任务。

关键词: 城市交通, 公众认知, GSDMM主题建模, 需求响应公交, 社交媒体, 大语言模型

Abstract: Demand-responsive transit (DRT) has emerged as a promising solution to address the dual challenges of urban congestion and diverse mobility needs. However, its widespread adoption is constrained by complex and uncertain public perceptions. This study aims to dissect the core themes of public discourse on DRT and attribute the underlying sentiment drivers. A dataset of 25 831 text entries was collected from two major social media platforms, Douyin and Bilibili, spanning from July 2022 to July 2025. The study uses a hierarchical topic modeling approach, integrating a priori framework with the Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM) topic modeling, and leveraged a Large Language Model (LLM) for sentiment analysis. The analysis identified four primary themes: "travel experience," "operational efficiency," "economy," and "others," which were further categorized into eight distinct sub-topics. The results reveal an overall public sentiment score of 0.37, indicating a slightly negative tendency. Positive sentiments were predominantly linked to "travel experience," reflecting an appreciation for DRT's core value proposition. Conversely, a significant volume of negative feedback was concentrated on tangible operational issues, such as booking procedures and routing inefficiencies. Crucially, this study uncovered a strong correlation between regional sentiment heterogeneity and DRT's functional role within local transport ecosystems. In some developed metropolitan areas, DRT is perceived as a beneficial complement and receives higher ratings. However, in regions where DRT has been extensively deployed to compensate for service gaps in the existing public transit network, a significant disparity between high public expectations and actual operational performance emerges as the primary driver of concentrated negative sentiment. This study provides timely public opinion intelligence for DRT stakeholders. The study concludes that the widespread adoption of DRT must abandon the "one-size-fits-all" model and instead shift towards a refined strategy tailored to the differences in local transportation systems. Furthermore, addressing core operational bottlenecks must be its foremost task to win public trust.

Key words: urban transportation, public attention, GSDMM topic modeling, demand-responsive transit, social media, Large Language Model (LLM)

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