Findings on Queue Length Based Macroscopic Fundamental Diagrams with Enhanced Floating Car Estimation Method

Abstract

In recent research, the macroscopic fundamental diagram (MFD) has been proved to be a powerful tool for large urban network modelling and control. This paper proposes a novel concept of queue length based MFD (QMFD) considering the fact that queue length is usually regarded as an important index to evaluate the efficiency of intersections. Compared to traditional MFD, the QMFD can reflect the traffic status more intuitively and can be understood more easily by transportation managers and residents. However, the queue length of some links may not be obtained directly in real situations if no fixed detector is available. To solve this problem, this paper proposes a floating car data (FCD) based method to estimate the QMFD. Firstly, a new queue length estimation method is developed by using BP neural network with different floating car percentage. Secondly, based on the estimated queue length, QMFD is calculated by fitting the relationship between the average queue length and other macroscopic traffic parameters such as average flow at intersections. Finally, the proposed method is verified by the traffic data provided by traffic simulation software VISSIM with the real road networks of Beijing's Second Ring Road. The simulation results demonstrate the effectiveness of the proposed queue length estimation method, and also reveal the existence of QMFD.