Abstract
Abstract
Autonomous modular vehicles create new possibilities within public transportation services, responding to growing needs for flexible and sustainable transport. Modularity allows for vehicle configurations with standardised load units, enabling multimodal (i.e., interoperable across multiple modes, such as road and rail) and co-modal (i.e., integration of both passenger and goods) transportation systems. Although Demand Responsive Transit (DRT) systems see a substantial body of literature, this is the first comprehensive review focusing on modularity, multimodality, and co-modality in public transportation. This review synthesises 45 peer-reviewed articles, identified through a systematic Scopus search and enriched by snowball sampling, across three dimensions: transport system design, modularity implementation, and optimisation methodologies. The results show a concentration of research on road-based systems, whereas modular systems across multiple transportation modes remain largely underexplored. Similarly, most research attention is given to line- or corridor-based systems, highlighting the need to further explore systems with a more flexible network design to leverage the benefits that modular vehicles offer. Co-modality receives growing interest, but has not yet been systematically analysed from a modular perspective. Methodologically, most studies rely on analytical methods to optimise planning and operations, with less attention to simulation-based methods, which may better include real-world uncertainties, and Artificial Intelligence-based (AI-based) methods, which offer promising opportunities for more robust, flexible and scalable modular transport systems. This review contributes to the field of POD-based multimodal transportation as the first structured overview of multimodal and co-modal aspects in modular transport systems, identifying key research gaps and outlining directions for future research.