Abdallah Abuaisha1
1Monash University
Journey planning applications for both public transport and road networks have become an essential part of people’s everyday life. The widespread use of such applications would not have been possible without the remarkable advances achieved in the route planning algorithms in the recent few decades. The main objective for such algorithms is to answer as many online queries as possible in the shortest amount of time. However, Journey planning algorithms for public transport still cannot yield similar query times for comparable road network sizes. Things become even harder when timetable-based systems are combined with unrestricted systems such as walking and driving, when multiple criteria have to be optimised besides the earliest arrival time, or even when dynamic scenarios dealing with delays and congestion are considered. In such conditions, the current algorithms suffer from poor query performance, consider limited transport modes, or provide limited user preferences.
In this study, we aim to address these challenges by developing an algorithm to solve the routing problem for transport systems, that is multimodal, personalised, multicriterial, dynamic, and fast. We started by developing a novel algorithm for solving the earliest arrival problem for public transport networks, which is inspired by the Compressed Path Databases method (CPD). We tested this algorithm on the trains network in the Melbourne Metropolitan Area. The initial findings show promising average query time as well as preprocessing time. Nevertheless, the algorithm needs to be further tested on larger networks, especially when other modes of transport are considered.