Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques,...

Where shall we sync? Clustering passenger flows to identify urban public transport hubs and their key synchronization priorities

Minimizing passenger transfer times through public transport (PT) transfer synchronization is important during tactical planning and real-time control. However, there are computational challenges for solving this Timetable Synchronization Problem (TSP) for large, real-world urban PT networks. Hence, in this study we propose a data-driven,...

Integrating network science and public transport accessibility analysis for comparative assessment

Network science offers powerful concepts and methods for studying complex systems, such as public transport networks. However, many existing studies on complex network analysis of public transport networks were primarily motivated to test network science concepts using real-life networks. Consequently, important properties related to public...

Unsupervised approach to bunching swings phenomenon analysis

We perform analysis of public transport data from March 2015 from The<br/>Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location (AVL) and automated fare collection (AFC) data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised...

Can Passenger Flow be Explained by Network Topology in Public Transport?

It has been rarely investigated in the field of public transport whether passenger ow can be explained by network topology. Based on the rich data sets from The Hague, The Netherlands, we conduct this study try- ing to shed light upon this question. The relation between passenger ow and topological measures in different public transport network...

Supervised learning: Predicting passenger load in public transport

In this extended abstract, we show the supervised learning approach to<br/>predicting passenger load of trams, based on historical passenger load patterns. We look at two different cases: predicting long-term passenger load of any given day and time, and predicting short-term passenger load at a particular public transport vehicle.