In public transport, smartcards are primarily used for automatic fare collection purpose,
which in turn generate massive data. During the last two decades, a tremendous amount of …

The planning of attractive and cost efficient public transport schedules, ie, timetables and
corresponding vehicle schedules is a highly complex optimization process involving many …

Population growth in urban cities has created transport problems such as traffic congestion,
pollution, accidents, and infrastructure failures. These problems often lead to disruption of …

We assess the demand effects of discounts on train tickets issued by the Swiss Federal
Railways, the so-called 'supersaver tickets', based on machine learning, a subfield of …

Short-term passenger flow prediction under planned events is important to reduce
passenger delay and ensure operational safety in metro systems. However, most studies …

The transportation system is often considered a critical infrastructure as the failure of its
components can have a substantial impact on the economy and social well-being of a …

Public transport networks are affected daily by disturbances with different entities. While big
service disturbances (or disruptions) are rare, delays and cancelled runs are more frequent …

The occurrence of incidents seriously affects the operation of the whole urban railway
system and passengers' travel experience. Accurate delay prediction is important for traffic …

Public transportation networks are vulnerable to uncertainty, which manifests in various
forms, disrupting their operations and leading to delays and passenger dissatisfaction …

Public transit has been highly impacted by coronavirus disease 2019 (COVID‐19), as well
as transport demand in general. Using high‐resolution data from smart card systems, this …