Traffic Control System Utilizing Prove Vehicle Position Data

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1)Development of a Feedback Estimate of Traffic States Combining Probe Vehicle Date with Detector DataIntegration of probe vehicle data into conventional detector data made it possible to estimate of traffic density, space mean speed, and travel time on real time basis more accurately. In the estimation model, A Kalman filter, adopting a high-order macroscopic traffic flow model as state equation and both detector data and probe data as observation equation, was used as a feedback estimate technique. The model was effective for extensive traffic situations from free flow states to congested flow states, including incident states due to sudden change in traffic flow. From numerical experiments, it was identified that at least 4 to - 5% of market penetration and 10 to 30 second of sampling interval are required to estimate traffic states accurately.2)Estimate of O-D Flow and Turning Movements from Traffic Count DataIn order to estimate turning movements at intersections, a logit based st … More ochastic user equilibrium (SUE) model was integrated into a genetic algorithm (GA). Three derivative models were developed : 1) a doubly constrained estimator, 2) a singly constrained estimator, and 3) an OD-based estimator. In the first and second estimators, the SUE model was formulated as a joint trip distribution and traffic assignment (TD/TA) program, whereas in the third estimator, the SUE model was based on a standard origin-destination (OD) distribution program. At first, these turning movement estimators (TME) models were examined by applying them to various road networks, covering a virtual road network with simulated data to a large city-size network in real field. It was concluded that except a small road network, the doubly constrained model showed the best performance in terms of the estimation of link volume and turning ratios.3)Transformation between Uninterrupted and Interrupted Speed for Urban Road ApplicationsUnlike freeway, urban traffic stream is generally interrupted by signal, and thus observed traffic data may differ from the true (uninterrupted) flow characteristic. The transformation between interrupted and uninterrupted speed on urban road with signalized intersection is necessary before using detector data. Conventional shock wave model and two modified versions of shock wave boundary that address for the unrealistic characteristics of the conventional shock wave are developed. A method of integrating probes and detector data for speed transformation is also discussed. The proposed methods are tested with a virtual isolated signalized intersection by varying the approach demand. The numerical results suggest that all models can significantly reduce the difference between the actual and the estimated uninterrupted (or interrupted) speed compared to the case of no conversion. Less