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原文传递 Use of Neural Network/Dynamic Algorithms to Predict Bus Travel Times Under Congested Conditions. Final rept.

题名：	Use of Neural Network/Dynamic Algorithms to Predict Bus Travel Times Under Congested Conditions. Final rept.
作者：	CHIEN, M.; CHIEN, S. I.; LIU, X.
关键词：	Travel-time; Dynamic-models.;Schedules-; Reliability-; Software-; Patterns-; Data-collection; Literature-reviews; Model-development; Time-points; Prediction-.
摘要：	Automatic Passenger Counter (APC) systems have been implemented in various public transit systems to obtain various types of real-time information such as vehicle locations, travel times, and occupancies. Such information has great potential as input data for a variety of applications including performance evaluation, operations management, and service planning. In this study, a dynamic model for predicting bus arrival times is developed using data collected by a real-world APC system. The model consists of two major elements. The first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-of-week, and weather condition. The second one is a Kalman filter based dynamic algorithm to adjust the arrival time prediction using up-to-the-minute bus location (operational) information. Test runs show that the developed model is quite powerful in dealing with variations in bus arrival times along the service route.
报告类型：	科技报告