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- Title
A Bayesian Combined Model for Time-Dependent Turning Movement Proportions Estimation at Intersections.
- Authors
Pengpeng Jiao; Tuo Sun; Lin Du
- Abstract
Time-dependent turning movement flows are very important input data for intelligent transportation systems but are impossible to be detected directly through current traffic surveillance systems. Existing estimation models have proved to be not accurate and reliable enough during all intervals. An improved way to address this problem is to develop a combined model framework that can integrate multiple sub models running simultaneously. This paper first presents a back propagation neural network model to estimate dynamic turning movements, as well as the self-adaptive learning rate approach and the gradient descent with momentum method for solving. Second, this paper develops an efficient Kalman filtering model and designs a revised sequential Kalman filtering algorithm. Based on the Bayesian method using both historical data and currently estimated results for error calibration, this paper further integrates above two sub-models into a Bayesian combined model framework and proposes a corresponding algorithm. A field survey is implemented at an intersection in Beijing city to collect both time series of link counts and actual time dependent turning movement flows, including historical and present data. The reported estimation results show that the Bayesian combined model is much more accurate and stable than other models.
- Subjects
BAYESIAN analysis; ESTIMATION theory; INTERSECTION theory; INTELLIGENT transportation systems; TRAFFIC monitoring; BACK propagation
- Publication
Mathematical Problems in Engineering, 2014, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2014/607195