Efficient and user-centric mobility systems is not only crucial for ensuring seamless operations and catering to the diverse mobility demands of the population, but also can advance the Sustainable Development Goals (SDGs), particularly SDG 11: Sustainable Cities and Communities.
Alice Xi

Using millions of smart card data including buses, ferries, trams, and trains, my research aims to 1) unveil valuable insights into user travel behaviour and preferences; 2) transform raw dataset into actionable business intelligence by employing robust business analytics techniques including data mining, predictive modelling, statistical analysis, and machine learning. The obtained insights will equip transport authorities with the crucial information necessary for making Intelligent decisions towards enhancing mobility service delivery and ameliorating the overall user experience.

Predictive Analysis

Utilising advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms, my research aims to forecast peak travel times, potential bottlenecks, and service disruptions. This predictive capability enables the implementation of proactive measures to ensure uninterrupted mobility services. Further, this predictive analysis can not only contribute to enhancing user satisfaction and mobility experience but also facilitate the strategic allocation of resources, optimising operational efficiency, and fostering a more resilient mobility system during disasters such as extreme weather events and the COVID-19 outbreak.

Intelligent Decision Making

The insights obtained from data analytics will inform actionable transport scheduling, route optimisation, and infrastructure investments. Further, leveraging advanced ML and AI algorithms, mobility systems could dynamically adjust service schedules and resource allocations based on real-time data and prevailing conditions, ensuring an optimal balance between service availability and operational efficiency.

Customised User Services (Mobility-as-a-Service)

By understanding individual travel habits and preferences through data mining, my research aims to revolutionise traditional mobility services by offering personalised weekly and monthly subscription bundles. These customised bundles encompass a diverse range of mobility services including, but not limited to, bus, ferry, tram, and train services, seamlessly integrated with an array of non-mobility options such as restaurant coupons, delivery service discounts, cinema tickets, and fitness memberships. By integrating these offerings into a comprehensive subscription, we aim to redefine the ecosystem, making mobility services more customised, accessible, rewarding, and pleasant.

Data-driven optimisation

Data-driven optimisation refers to the strategic process of using data analytics to inform and enhance decision-making for the purpose of improving operational performance, user experiences, and business outcomes. This method relies heavily on the collection, analysis, and application of data to identify the most efficient and effective ways to achieve objectives within transport systems. The continuous cycle of data analysis and application of insights ensures that the optimisation process is dynamic and evolves with changing data and operators’ goals.

The significance my research aligns with the Newcastle Business School's research theme of digital transformation, showcasing the potential to redefine mobility systems that dynamically adapt and evolve in response to user behaviour. The managerial insights, derived through a lens of digital innovation, are the catalysts for transformative solutions that promise not only enhanced user experiences but also a significant stride towards a greener, more inclusive, and sustainable future.

Dr Alice Xi

Dr Haoning (Alice) Xi

As a Lecturer in Business Analytics at the Newcastle Business School, University of Newcastle, Dr. Haoning (Alice) channels her passion for education and innovation into nurturing the next generation of thinkers and leaders.