Analysis and modeling of residents travel behavior under MaaS

The swift advancement of urban motorization has resulted in a disparity between the supply and demand of traffic services. The conventional travel service framework has proven inadequate in facilitating a seamless integration of various transportation modes, thereby rendering travel inconvenient for urban residents. Mobility as a Service (MaaS) represents an innovative transportation solution that offers quicker, more efficient, and cost-effective travel alternatives. Nonetheless, the effectiveness of the MaaS system is contingent upon travelers’ acceptance and their perceived preferences regarding MaaS. Consequently, this study categorizes travel choice modes within the context of MaaS and develops multinomial logit (MNL) models. Acknowledging the limitations inherent in MNL models, this research proposes enhancements by incorporating random parameters to formulate a mixed logit (MXL) model. A comparative analysis of the parameter estimation outcomes from both the MNL and MXL models reveals an improved interpretative capacity when accounting for heterogeneity. Furthermore, the findings derived from the MXL model indicate that latent influencing factors, such as punctuality, significantly impact travel choices within the MaaS framework. Theoretically, this study offers a valuable reference for quantifying the potential perceptions associated with MaaS travel modes. Practically, the classification of potential travelers aids in identifying early adopters of MaaS and facilitates targeted promotion of MaaS to specific demographic groups, thereby mitigating resistance to its implementation.