Measure distance between points in cst microwave studio

#Measure distance between points in cst microwave studio series

The optimal accumulation states were typically identified by observation. Saberi and Mahmassani conducted a comparison analysis to investigate the capacity drop phenomena using loop detector data from three different freeway networks. However, few studies dedicated to develop methods for the analysis of network traffic flow states, such as the network traffic flow state identification and state evolution pattern recognition. In literature, most studies focused on the estimation of MFD with different sources of data, including loop detector data, probe vehicle data and connected vehicle data. More practically, empirical studies using field data gained more attentions.

Although these studies have revealed important properties of macroscopic network traffic flow states, the theoretical methods were not ready to apply in practice due to limitations such as the overestimation of network capacity. Following their work, Geroliminis and Boyac investigated the effects of different link lengths and signal timing on network capacity. The derived network flow was interpreted as the theoretical capacity of network. Daganzo and Geroliminis developed an analytical approximation method to derive the upper bound of the average flow conditional on average density. Gayah and Daganzo found that the clockwise hysteresis phenomenon during the congestion recovery period was caused by the instability of network traffic flow. In light of their work, a method based on the phase portraits of the dynamic Equations was developed to determine the boundaries of the stable and unstable regions of states in MFD. Daganzo and Gayah illustrated that bifurcations occurred when the network accumulation came up to a certain value. To characterize the network traffic flow states with MFD, analytical methods were proposed in theoretical studies.

To the successes of these applications, one of the important bases is to understand the characteristic of network traffic flow states in MFD, including the state transitions in the temporal evolution process. This property can be used to develop effective applications such as traffic signal gating control, congestion pricing, route guidance and traffic safety analysis. The core property of MFD is that the network flow is maximized when network traffic stays at an optimal accumulation state. Macroscopic Fundamental Diagram (MFD) reveals the relationship between network accumulation and flow at the macroscopic level, and can be readily obtained with traffic flow data from existing detectors (e.g., loop detectors).

The proposed method is helpful to understand the temporal evolution process of network traffic flow and provides potentials for developing more reliable network traffic flow management strategies, such as optimizing traffic signal plans and developing strategies for congestion tooling. An extended application of real-time identification of CTS points was also discussed. The day-to-day pattern of CTS points was investigated by the Gaussian Mixture Model-based clustering model. The identified CTS points distinguished the traffic states between free-flow state and optimal accumulation state, and the optimal accumulation state was characterized. The test results showed that the CTS points can be well captured by the proposed method. The proposed method was tested in a real network of Kunshan City, China.

#Measure distance between points in cst microwave studio series

This study aims to develop a data-driven method based on time series analysis of MFD state points to characterize critical transition state (CTS) of network traffic flow using field data. Although many empirical studies had been conducted in the field of MFD, few studies were dedicated to investigate the network traffic flow states with field data. Theoretical and experimental investigations have provided insights into the dynamics and characters of traffic flow states. The network traffic flow state analysis is a fundamental problem for the MFD-based applications. Macroscopic Fundamental Diagram (MFD) reveals the relationship between network accumulation and flow at the macroscopic level.