Evaluating the impact of waiting time reliability on route choice using smart card data

Unreliable waiting times may cause frustration and anxiety amongst public transport travellers. Although the effect of travel time reliability has been studied extensively, most studies have used stated preferences which have disadvantages, such as an inherent hypothetical bias, or have analysed revealed preferences for road traffic. Here, we...

Modelling growth principles of metropolitan public transport networks

The development of metropolitan public transport networks often involves choosing between investing in extending radial lines or constructing ring connections. While the former enlarges network coverage the latter enhances network connectivity and reduces the need to perform detours. Moreover, investments might be better directed at...

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,...

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...

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.