A prescription for transit arrival/departure prediction using automatic vehicle location data

摘要

In this paper we present a general prescription for the prediction of transit vehicle arrival/departure. The prescription identifies the set of activities that are necessary to preform the prediction task, and describes each activity in a component based framework. We identify the three components, a Tracker, a Filter, and a Predictor, necessary to use automatic vehicle location (AVL) data to position a vehicle in space and time and then predict the arrival/departure at a selected location. Data, starting as an AVL stream, flows through the three components, each component transforms the data, and the end result is a prediction of arrival/departure. The utility of this prescription is that it provides a framework that can be used to describe the steps in any prediction scheme. We describe a Kalman filter for the Filter component, and we present two examples of algorithms that are implemented in the Predictor component. We use these implementations with AVL data to create two examples of transit vehicle prediction systems for the cities of Seattle and Portland.