Dynamic Multi-interval Bus Travel Time Prediction Using Bus Transit Data
infrastructure - stop, ridership - forecasting, ridership - forecasting, mode - bus
Travel time, Travel behavior, Stop (Public transportation), Seoul (Korea), Scenarios, Projections, Origin and destination, O&D, Journey time, Intracity bus transportation, Forecasting, Bus usage, Bus travel, Bus transit, Bus stops
The aim of this research is to develop a dynamic model to forecast multi-interval path travel times between bus stops of origin and destination. The research also intends to test the proposed model using real-world data. This research was brought about by the shortcomings of the existing real-time based short-term-prediction models, which have been widely utilized for single interval predictions. The developed model is based on the Nearest Neighbor Non-Parametric Regression using historical and current data collected by the Automatic Vehicle Location technology. In a test with real-world bus data in Seoul, Korea, the proposed multi-interval-prediction model performed effectively in terms of both prediction accuracy and computing time.
Chang, Hyunho, Park, Dongjoo, Lee, Seungjae, Lee, Hosang, Baek, Seungkirl. (2010). Dynamic Multi-interval Bus Travel Time Prediction Using Bus Transit Data, Transportmetrica, Volume 6, Issue 1, pp 19-38.