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Figure 6 The predictive traffic state vs. actual density.
Figure 5 The predictive traffic state vs. actual speed.
This property sheds light on the road pricing design based on the MFD framework by minimizing the gap between the actual traffic state and the desired traffic state.
Finally, we choose the speed and traffic flow to express the metrics of traffic state and use the fuzzy reasoning rules to give out the determined traffic state.
The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results.
Real urban traffic state is considered when the lane reservation strategy is implemented.
The framework is applied in two case studies, namely queue-end detection and traffic state estimation.
Secondly, a newly traffic state estimation (TSE) algorithm is designed to obtain the queue's analytical statistics.
This assumes vehicle communications are aware of the traffic state along the prescribed delivery route.
We consider the stochasticity in traffic state estimation and the shape of the MFD.
Firstly, the impacts of passenger demand and network traffic state on bus operational performance are analyzed.
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