Personalised uncertainty-aware multi-criteria journey planning in multi-modal transportation network
Creators: name: HAQQANI, Mohammad
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 460203
subject: Evolutionary computation

description: Multi-modal travelling, in increasingly complex transport networks, is nowadays an essential part of human lives in modern societies. Finding the best travel option in this complex transport network, however, is a challenging task for passengers as the vast amount of information can easily overwhelm them when choosing between several possibilities to travel. For the sake of helping people to efficiently navigate through this intricate transportation scheme, it is highly critical to create an effective journey planning system, in which passengers can find optimal itineraries. 


The term `optimal itineraries' should be carefully defined in the context of multi-modal travelling. Indeed, passengers do not only seek short-time travels, but they also consider several other criteria, such as comfort and reliability. Therefore, an efficient journey planning system should take into account the various needs and preferences of each passenger. Moreover, travelling in multi-modal transport carries uncertainty. Thus, handling uncertainty is also an essential aspect of practical journey planning systems. It is vital for the recommended multi-modal journey plans to be robust to the dynamic and stochastic variations of the transport network.


 This thesis focuses on designing models and algorithms for personalised and multi-criteria journey planning problem in the multi-modal transport network. The main objective of this research is to develop a framework that is capable of planning high-quality multi-criteria journey plans from an origin to a destination that respects user preferences and accounts for the dynamic and the stochastic nature of the multi-modal transportation network. Integrating all the above characteristics (i.e., finding multi-modal, multi-criteria, uncertainty-aware, and personalised journey plans) into one framework is one of the most challenging tasks in building a journey planning system, and to the best of our knowledge, it is the first time that such a comprehensive framework is developed. Such a framework can provide passengers with intelligent tools which can help them to discover journeys that best suit their transport needs and, consequently, to facilitate the much-needed shift towards sustainable mobility. 
 
The findings of this research have important contributions in the area of the journey planning problem. In a nutshell, we propose a formal multi-modal modelling technique that enables us to represent multi-modal transport networks efficiently. The created multi-modal model enables us to integrate several modes of transportation into a single homogeneous representation and is capable of effectively handling the dynamic and stochastic nature of the transportation network.  Besides, based on the proposed modelling technique, we propose efficient routing algorithms focusing on solving mono to many-criteria journey planning problems, both in dynamic and stochastic environments. Additionally, in order to provide personalised information to the passengers, we propose two passive preference learning algorithms to estimate the passengers' preferences based on their historical interactions with the system.
 
 The performance of the proposed framework has been assessed by developing a real-world journey planning system, and solving real-life planning queries defined on the transport network of the city of Melbourne and its suburbs. The experimental results indicate that the proposed system is capable of providing high-quality solutions for basic and complex queries within a reasonable amount of time.