Data driven enhancement of public transport planning and operations: Service reliability improvements and ridership predictions

Automatic Vehicle Location (AVL) and smartcard data are of great value in planning, design and operations of public transport. We developed a transport demand model, which utilizes smartcard data for overall and what-if analyses, by converting these data into passengers per line and OD-matrixes and allowing network changes on top of a base...

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...

Predicting Short-term Bus Ridership with Trip Planner Data: A Machine Learning Approach

To address the increasing passenger demand in the coming years and make public transport less crowded and delayed, insights into predicted passenger flow are needed. A wide range of studies has used and validated that smart card data can be one of the sound bases for predicting short-term passenger demand. However, it also has several...

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...