In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction algorithms, each of which is characterized by a small number of adjustable parameters. Adaptability means that parameters of the constructed combination depend on a number of control parameters of the model, which includes the following factors: weather conditions, traffic density, driving dynamics, prediction horizon, and others. Adaptability is achieved by the use of a hierarchical regression (similar to a regression tree). The proposed arrival prediction algorithm has been tested with the data of all the public transport routes in Samara, Russia.
An Adaptive Algorithm for Public Transport Arrival Time Prediction Based on Hierarhical Regression
2015-09-01
196098 byte
Conference paper
Electronic Resource
English
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