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

Predictions on public transport ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. At 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 lag...

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...

Predicting bus ridership in the Netherlands

The Netherlands suffers from a traffic congestion problem, which is a cause of both economic damages and high NOx and CO2 emissions. Today, the bus accounts for only a small percentage of all trips made in the Netherlands (less than 4%) but the bus plays an important role as an access and egress mode for train travelers. Short access and egress...

Bus ridership prediction: Development of a framework

During tenders in public transport, the bus network is reconsidered and adjusted. An accurate prediction of the effect on ridership is required. Due to the tender environment, time and input data are limited. This research focuses on deriving a relation between level-of-service (LOS) and ridership, and implementing this in a model suitable for...