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This paper presents a method of estimating traffic density on a highway/freeway from cellular network data. The speed of the traffic is calculated/ measured from the cellular network communication between the vehicles and the network. The model for estimating the density from the speed is developed from the cellular data. The results are then validated through the one-equation partial differential...
Multi-Resolution traffic flow schemes, coupling a microscopic (vehicle based), mesoscopic (cell based) and macroscopic (flow based) representations of traffic flow may be a useful tool to better understand and utilize the relationships between the various types of representation. The paper proposes a new classification of traffic model which is according to the representation scale and the behavioural...
The LWR model's proposition has played an important part in the traffic flow study history. In this paper, we advance an extension model from the origin LWR model. The model takes slope and heterogeneous drivers into account according to an increased speed caused by gravity. We separate heterogeneous drivers with different free flow speed on the flat road and take their interaction into consideration...
Traffic state estimation is important input to traffic information and traffic management systems. A wide variety of traffic state estimation methods exist, either data-driven or model-driven. In this paper a model-driven approach is used: the LWR model solved by the Godunov scheme. The most widely applied method to combine this model with real-time data is the Extended Kalman Filter (EKF). A large...
A numerical simulation method for a multi-class LWR model (MCLWR model) with heterogeneous drivers is presented. The MCLWR model can remedy some deficiencies of LWR model including the two-capacity phenomenon, hysteresis and platoon dispersion. Considering the analytical expressions of the eigenvalues are difficult to obtain for more than four-class of road users, we utilize high-resolution central...
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