Empirical Macroscopic Fundamental Diagrams: Insights from loop detector and floating car data

Abstract

The macroscopic fundamental diagram, relating average flows and densities in an urban network, has been analyzed in some empirical studies and many simulations. It has been shown to be an efficient tool for traffic management and control or the estimation of travel times in a network. However, empirical studies remain scarce and are usually based on one single data source, such as loop detector data (LDD) or floating car data (FCD). In this paper, we analyze an extensive data set based on both, LDD and FCD for the city of Zurich. We show that each source exhibits a well-defined and reproducible MFD. However, they differ from each other, due to limitations of the data sources. We identify a placement bias, and a link selection bias for LDD, which leads to an overestimation of occupancy or density values, respectively. In order to mitigate such biases we develop a methodology accounting for the relative position of a loop detector on links and their frequency at that position. Moreover, we investigate and validate common practices when transforming LDD occupancy and FCD flows, which are the space effective mean length of a vehicle and the probe penetration rate, respectively. We also apply a combination of LDD flows and FCD speeds to estimate the MFD, which partly eliminates key drawbacks of both data sources.