Conventional methods to understand urban transportation mode choice primarily revolve around assessing the relation costs/benefits among the different mobility alternatives. However, emerging research has emphasized the significance of comprehending intricate social processes that shape decision-making in urban mobility. This study delves into the impact of social networks on aggregated travel behavior, using a comprehensive dataset encompassing multiple data sources such as Twitter/X messages, bicycle sharing system (BSS) and traffic counts, weather and socio-demographic information. Focusing specifically on the city of Madrid, Spain, the dataset covers an extensive period, capturing daily data from 2018 to 2021. To gain deeper insights into the underlying influences, a combination of panel regression models and a topic modeling approach were employed for analysis. The study’s findings highlight the significant impact of social media communication on transportation behavior, revealing a positive correlation between higher social media message volume and increased usage of public and sustainable transportation options like subways and BSS, while private car usage decreased. Although there is message salience, i.e., a sudden surge in tweet numbers leads to a temporary shift in behavior, the findings suggest that municipalities can effectively influence transportation behavior by strategically communicating messages related to sustainable transportation through social networks.

了解城市交通方式选择的传统方法主要围绕评估不同出行方式之间的成本/收益关系。然而，新兴研究强调了理解影响城市交通决策的复杂社会过程的重要性。本研究使用包含 Twitter/X 消息、自行车共享系统 (BSS) 和交通计数、天气和社会人口统计信息等多个数据源的综合数据集，深入研究社交网络对总体出行行为的影响。该数据集专门针对西班牙马德里市，涵盖了广泛的时期，捕获了2018年至2021年的每日数据。为了更深入地了解潜在影响，采用面板回归模型和主题建模方法相结合进行分析。该研究的结果强调了社交媒体传播对交通行为的重大影响，揭示了较高的社交媒体消息量与地铁和 BSS 等公共和可持续交通选择的使用增加之间的正相关性，而私家车的使用量则减少。尽管存在信息显着性，即推文数量突然激增导致行为暂时转变，但研究结果表明，市政当局可以通过社交网络战略性地传达与可持续交通相关的信息，从而有效影响交通行为。