Bus priority has been one of the most essential measures in achieving carbon peaking and carbon neutrality. However, in some cities, while the bus system is improving, the bus passenger volume is either growing slowly or declining. This opposite trend suggests that the behavioural loyalty of bus passengers has not been ensured despite improved bus facilities. This paper aims to enhance bus travel intention by addressing varied behavioural loyalty through a combination of survey and smart card data. The travel characteristics of bus passengers are clustered into behavioural loyalty and disloyalty using the K-means++ algorithm, and the factors influencing travel intention for these two types are analyzed using the Multiple Indicators and Multiple Causes (MIMIC) model. The findings regarding travel characteristics indicate that travel frequency for all passenger types decreases gradually as travel distance increases. Notably, long travel distances have negative effects on passenger loyalty. In terms of departure time distribution, loyal passengers exhibit more pronounced morning and evening peak periods, while disloyal passengers show less distinct evening peaks. Additionally, a significant proportion of loyal passengers engage in activities lasting 8–12 h on both weekdays and weekends. Meanwhile, the MIMIC model results indicate that both punctuality and attitude have significant positive impacts on both loyal and disloyal passengers. Loyal passengers prefer efficient and comfortable bus service, while bus travel speed, metro availability, and perceived behavioural control have positive impacts on disloyal passengers. Specific strategies are proposed based on the results of travel characteristics and the MIMIC model. These results can inform special strategies for improving bus travel intention and loyalty.

中文翻译：

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使用多源数据融合对公交乘客进行行为忠诚度分析


公交优先一直是实现碳达峰、碳中和的最重要措施之一。然而，在一些城市，虽然公交系统正在改善，但公交客运量要么增长缓慢，要么下降。这种相反的趋势表明，尽管公交设施得到了改善，但公交乘客的行为忠诚度并未得到保证。本文旨在通过结合调查和智能卡数据来解决不同的行为忠诚度问题，从而提高公交出行意愿。使用 K-means++ 算法将公交乘客的出行特征归类为行为忠诚度和不忠诚度，并使用多指标和多原因 （MIMIC） 模型分析影响这两种类型的出行意愿的因素。关于旅行特征的结果表明，随着旅行距离的增加，所有乘客类型的旅行频率逐渐降低。值得注意的是，长途旅行会对乘客忠诚度产生负面影响。在发车时间分布方面，忠诚乘客表现出更明显的早晚高峰时段，而不忠诚乘客表现出不太明显的晚高峰。此外，很大一部分忠实乘客在工作日和周末都参与持续 8-12 小时的活动。同时，MIMIC 模型结果表明，守时率和态度对忠诚和不忠诚的乘客都有显著的积极影响。忠诚的乘客更喜欢高效舒适的公交服务，而公交车的行驶速度、地铁可用性和感知的行为控制对不忠诚的乘客有积极影响。根据出行特征和 MIMIC 模型的结果提出了具体策略。 这些结果可以为提高公交出行意愿和忠诚度的特殊策略提供信息。