Service area identification for robotaxi deployment: A GIS-based spatio-temporal multi-criteria decision support framework

The rapid commercialization of robotaxi services presents significant opportunities for market players and city managers. However, a critical challenge remains: identifying high-potential service areas within urban environments. This study addresses an important research gap by developing a dynamic spatio-temporal multi-criteria assessment framework that integrates Geographic Information Systems (GIS) and the Stratified Best-Worst Method (S-BWM) for robotaxi service area identification. The framework serves as a crucial initial planning tool for identifying candidate zones prior to detailed feasibility studies, thereby filling a critical gap in the literature. Through an empirical application in Wuhan, an emerging hub for commercial robotaxi services in China, the framework demonstrates its practical applicability. A comparative analysis reveals that several existing service areas in Wuhan fall within zones deemed unsuitable based on GIS assessment criteria, highlighting opportunities for strategic improvement. The findings provide valuable insights into the integration of robotaxis within urban transit networks. By incorporating spatio-temporal interaction maps and stratified decision-making methods, this study develops a strategic decision-support tool to assist stakeholders in operational management and policy formulation during the initial planning phase.