Shared Automated Vehicles (SAVs) hold significant potential to redefine urban mobility services. However, the applicability and optimization of their operational strategies in diverse urban contexts remain unclear, particularly concerning the complex interactions with existing public transport systems. This study systematically investigates how urban heterogeneity, including city scale, network topology, and demand patterns, modulates SAV fleet management strategies to balance operator costs and user utility within a multi-modal transportation context.

To this end, this study develops a comprehensive simulation framework that combines an advanced ride-pooling candidate generation algorithm (ExMAS) with an innovative two-stage vehicle assignment optimization process, and integrates a nested Logit model to quantify the competition structure with public transport. This framework is applied to 37 Dutch cities of varying scales to derive generalizable findings.

The study finds that population scale is a fundamental determinant of the required fleet size, exhibiting a strong linear relationship, especially in large cities. The key to enhancing operational efficiency, however, lies in more nuanced urban structure metrics. In large cities, longer commuting distances combined with more complex networks (higher node degrees) foster ride-pooling potential. In contrast, for small cities, the local compactness of the network (higher clustering coefficient) and demand density are critical for improving vehicle turnover efficiency. Furthermore, the research confirms that a uniform pricing strategies are unlikely to achieve best performance on pooling efficiency, highlighting the necessity of implementing differentiated pricing based on user preferences and city scale. In competition with public transport, the study's Public Transport Competitiveness Index reveals a non-monotonic relationship with travel distance. A state of competitive balance is observed for short-distance trips, while PT holds a distinct advantage in the medium-distance range. For long-distance trips, the inherent speed advantage of SAVs allows them to become the more competitive option. This external competition also structurally increases the internal pooling rate of the SAV system.

The findings of this study provide critical strategic insights for SAV operators and policymakers, emphasizing the critical importance of adjusting fleet management, service design, and pricing strategies according to specific urban characteristics and the competitive environment, thereby providing decision support for achieving an efficient and sustainable urban transportation system.