Real-Time Traffic Data Acquisitions and Fusion Techniques (II)
Date Issued
2004
Date
2004
Author(s)
張堂賢
DOI
922211E002079
Abstract
Abstract
In order to achieve advanced traffic management system (ATMS), This study
proposes a real-time traffic data acquisition system. The framework of system has its
basis of taxi fleets as probe vehicle system, and combines roadside detectors to
collect traffic data from urban network extensively. According to the physical
architectural, the study builds the mathematic models of “travel time prediction for
road section” and “dynamic OD estimation”. The algorithms are based on
generalized least squares (GLS) and extended Kalman filter (EKF) repectively.
Through the prediction model, the system state of traffic flow is predicted to support
decision-making of traffic control and management.
To verify the models, for travel time prediction for road section, this study
analyzes the prediction results of grid network simulation from Paramics, and
evaluates prediction ability by indices of precision, robustness and stability. It proves
that well prediction results are obtained through calibration and data processing. For
dynamic OD estimation, the study calculates traffic count prediction from estimated
OD flows, which shows the mean error is within 10%. Therefore, it concludes the
model is reasonable. Also, considering the represenstative of probe vehicles to traffic
flow, the model has well prediction ability, when the penetration of probe vehicles is
above 5%.
Subjects
Traffic Data Acquisitions
Probe Vehicle
Travel Time Prediction
Dynamic OD
Publisher
臺北市：國立臺灣大學土木工程學系暨研究所
Type
report
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