“Who can I ask for help?”: Mechanisms behind digital inequality in public transport

Digitalisation in public transport has become pervasive over the past decade, especially in urban areas. While it benefits many, it also leaves some behind. Previous research shows that older adults, people with a lower education level, people with impairments and people with a migration background are more likely to be negatively impacted by...

Fostering an inclusive public transport system in the digital era: An interdisciplinary approach

As digitalisation is making its way into public transport (PT) services, policy approaches to ensure that such services remain inclusive are at best fragmented, at worst inexistant. This study pieces together existing initiatives and lessons learnt in the transport sector itself, and takes inspiration from other fields with a more mature...

Validation of a multi-modal transit route choice model using smartcard data

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction...

Shared micromobility and public transport integration - A mode choice study using stated preference data

This paper uses stated preference data collected in the city of Rotterdam and discrete choice modelling techniques to study the relationship between public transport and shared micromobility. It assumes a hypothetical condition of integrated systems and studies the relationships of complement and competition between these modes. The findings...

Effectiveness of trip planner data in predicting short-term bus ridership

Predictions on Public Transport (PT) ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. On an operational level, this relates to short-term predictions with lead times of less than an hour. Where conventional data sources on ridership, such as Automatic Fare Collection (AFC) data, may have longer...

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...

Quantification and control of disruption propagation in multi-level public transport networks

Due to the multi-level nature of public transport networks, disruption impacts may spill-over beyond the primary effects occurring at the disrupted network level. During a public transport disruption, it is therefore important to quantify and control the disruption impacts for the total public transport network, instead of delimiting the...

Perception of overlap in multi-modal urban transit route choice

Capturing unobserved correlation between overlapping routes is a non-trivial problem in route choice modelling. For urban transit networks, research so far has been inconclusive on how this overlap is perceived by travellers. We estimate a series of path size correction logit (PSCL) models to account for alternative specifications of route...

Public transport fare elasticities from smartcard data: Evidence from a natural experiment

This paper develops a method for analysing the elasticity of travel demand to public transport fares. The methodology utilizes public transport smartcard data for collecting disaggregate full population data about passengers’ travel behaviour. The study extends previous work by deriving specific fare elasticities for distinct socioeconomic (e...

Integrated route choice and assignment model for fixed and flexible public transport systems

The recent technological innovations in various ICT platforms have given rise to innovative mobility solutions. Such systems could potentially address some of the inherent shortcomings of a line/schedule based public transport system. Previous studies either assumed that flexible on-demand services are used as an exclusive door-to-door...

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,...

AV meets PT: the future of automated vehicles in public transport

This paper gives an overview of the projects and pilots with automated vehicles as public transport in the Netherlands as well as the remaining research questions and preliminary results of passenger related studies regarding expected ridership.

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,...

Improving predictions of public transport usage during disturbances based on smart card data

The availability of smart card data from public transport travelling the last decades allows analyzing current and predicting future public transport usage. Public transport models are commonly applied to predict ridership due to structural network changes, using a calibrated parameter set. Predicting the impact of planned disturbances, like...

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...

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.