Modular system for corporate mobility management

Corporate mobility management has traditionally been approached through fragmented solutions, with companies implementing separate tools like carpooling, public transport incentives, and financial reimbursements without an overarching framework to connect these elements. This dissertation presents the Mobility Company Platform (MCP), a comprehensive, modular corporate mobility system—delivered as a mobile application—that integrates multiple transport services into a unified digital ecosystem. Over the course of this research, MCP was developed to offer a flexible, data-driven, and highly customizable mobility solution, enabling companies to adapt their mobility offerings based on employee needs, corporate sustainability goals, and cost efficiency. A core focus of this work has been the cross-modular nature of MCP, which allows organizations to selectively enable different combinations of mobility modules—including carpooling, MaaS services, financial perks, and multimodal travel support—tailored to their workforce. By analysing user behaviour across these integrated services, this thesis demonstrates how a single platform capturing diverse mobility interactions can provide a deeper understanding of commuting patterns, enhancing both employee satisfaction and operational efficiency. The research employs clustering analysis to illustrate how users engage with various services, revealing distinct commuter profiles and highlighting the value of interconnected data streams in mobility planning. Unlike conventional mobility management tools, MCP provides an evidence-based framework for optimizing mobility benefits, offering companies a data-driven method to increase service adoption, refine financial incentives, and promote sustainable commuting practices. Beyond its corporate applications, MCP introduces a new paradigm for mobility research, offering one of the first datasets that links multimodal user behaviours, financial incentives, and real-world transport interactions, including non-trackable commute options such as walking and cycling. Through the Activities module, MCP enables tracking and incentivization of these modes, which are typically overlooked by conventional mobility services reliant on ticketing systems. This research underscores the potential of such platforms in supporting AI-driven mobility modelling, predictive analytics, and urban transportation planning, providing a scientific foundation for future innovations in corporate mobility management. The findings of this dissertation demonstrate that a modular, data-rich corporate mobility ecosystem not only improves the commuting experience for employees but also enhances scientific research and policy development in transportation planning. Future advancements in MCP will focus on AI-driven mode detection, enhanced route-matching algorithms, and deeper integration with urban mobility infrastructures, further strengthening its role as a leading-edge corporate mobility solution.