The sustainable development of urban transportation is facing various challenges, including traffic congestion, increasing carbon emissions, and diversified travel demands. New concepts of mobility and business models are emerging to address these challenging conditions, such as mobility as a service (MaaS). As a new paradigm of travel services, users’ recognition, acceptance, and continuous use of MaaS are prerequisites for its survival and development. Hence, to ensure the successful implementation of MaaS, it is crucial to precisely identify the key factors influencing individuals’ willingness to use MaaS. In order to analyze the mechanisms that influence individuals’ willingness to use MaaS, this study first conceptualized a behavioral model by drawing on the modified Technology Acceptance Model (TAM) and the fundamental characteristics of MaaS. Based on the behavioral model, a structured questionnaire consisting of eight sections and thirty-three questions was designed and conducted online in Beijing, China. A total of 1260 valid questionnaire data were collected, and a descriptive analysis was conducted on the collected data, including the frequency distribution and intention to use MaaS based on the socioeconomic and mobility characteristics. Then, reliability and validity analyses were conducted on the questionnaire data using Cronbach’s alpha coefficient method and the Confirmatory Factor Analysis (CFA) method, respectively. Finally, the behavioral model was analyzed quantitatively using the Structural Equation Model (SEM). The results show that 77.62% of the respondents are willing to use MaaS after it is implemented, and 44.29% of them strongly agree to using it, while 2.06% of them strongly disagree to using it. Travel philosophy, travel preference, and perceived usefulness have positive impacts on individuals’ behavioral intention to use MaaS, while perceived usefulness exerts the greatest influence, with a coefficient of 0.364. Meanwhile, the latent variable of perceived risk has a significantly negative impact on behavioral intention, with a coefficient of −0.141. From the perspective of observed variables, convenience and efficiency are the most important factors affecting intention to use MaaS, while environment protection is the least influential factor. The results of this study can provide a decision-making basis for transportation planners, MaaS service providers, and policymakers, enhancing the level of sustainable development of urban transportation.

In the rapid process of urbanization, urban agglomerations have become a key driving factor for regional development and spatial reorganization. The formation and development of urban agglomerations rely on communication between cities. However, the spatiotemporal characteristics of intercity travelers are not fully grasped throughout the entire trip chain. This study proposes a spatiotemporal analysis method for intercity travel in urban agglomerations by constructing origin-to-destination (OD) trip chains using smartphone data, with the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employed Cramer’s V and Spearman correlation coefficients for multivariate feature selection, identifying 12 key variables from an initial set of 20. Then, optimal cluster configuration was determined via silhouette analysis. Finally, the K-prototypes algorithm was applied to cluster 161,797 intercity trip chains across six transportation corridors in 2019 and 2021, facilitating a comparative spatiotemporal analysis of travel patterns. Results show the following: (1) Intercity travelers are predominantly males aged 19–35, with significantly higher weekday volumes; (2) Modal split exhibits significant spatial heterogeneity—the metro predominates in Beijing while road transport prevails elsewhere; (3) Departure hubs’ waiting times increased significantly in 2021 relative to 2019 baselines; (4) Increased metro mileage correlates positively with extended intra-city travel distances. The results substantially contribute to transportation planning, particularly in optimizing multimodal hub operations and infrastructure investment allocation.