Voting with one's feet: Unraveling urban centers attraction using visiting frequency

Urban and regional areas worldwide exhibit a complex and uneven distribution of activities with certain areas attracting more people during different time periods. In this study we systemically classify different parts of the urban area which are most attractive as measured by their ability to attract visitors. A weekly visiting profile is...

Unravelling individual mobility temporal patterns using longitudinal smart card data

The increasing availability of longitudinal individual human mobility traces enables the disaggregate analysis of temporal properties of mobility patterns. The objective of this study is to identify distinctive market segments in terms of habitual temporal travel patterns of public transport users. First, travel patterns are clustered using a...

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,...

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...