The main motivation for the proposed research is to explore alternative approaches of designing and managing public transportation which is flexible, robust, smart, agile and more responsive to the aforementioned changes within an urban ecosystem. To that end, we aim to explore the potential advantages of adopting a de-centralised approach to the design and management of public transportation through the adoption of the principles of “swarm intelligence”.
A majority of people in an urban environment use public transportation services for work or leisure. Despite the merits of the state-of-art transportation mode are well reported in relation to reduced carbon emissions, increased safety, and reduced congestion, many people still do not prefer public transportation, in particular buses, due to the lack of flexibility and unreliability (e.g. traffic jams, weather conditions) and uncertainty (disruptions to the services). Although new technologies are gradually being adopted, mainly to improve booking or accessibility and for communication purposes, the main operational principle of public transportation has remained “unchanged” for over a century.
Many of these negative perceptions towards the current bus system are not necessarily caused by the nature of public transportation per se, but due to the ground on which this service was designed, planned and managed: the current system is centralised rather than distributed, static rather than dynamic. This means buses would follow a priori set routes within a system which is not responsive to the changes within or around that system, such as weather conditions, road conditions, user demand, use patterns, and temporary obstructions caused by various factors. When there is an outrage or latency in communication, it will cause problems as the system is not adapted to changes. To address this issue, we propose a self-organising and decentralised framework that can be implemented within a public transportation system. When there is a communication breakdown, for example, the de-centralized and distributed system will continue to operate in an intelligent way.
Project Team (Deakin):
Tuba Kocaturk
Jan Carlo Barca
Seng Loke
Rui Wang
Sofija Kaljevic