Project I6

Project I6

Macroscopic Fundamental Diagram Estimation using Loop-Detector Data


Jorge Laval, Ph.D., Georgia Institute of Technology
Zijian Ding, Doctoral Student, Georgia Institute of Technology
Garyoung Lee, Doctoral Student, Georgia Institute of Technology

UTC Project Info
Final Report
Technology Transfer Report (posted soon)

What is the issue? This project aims to examine the empirical estimation approach of the Macroscopic Fundamental Diagram (MFD) using loop detector data. The MFD give the network-wide relationship between average traffic variables, and has become an invaluable tool for congestion management on large transportation networks. However, deriving the MFD using the empirical data is challenging since (i) the required loop detector data is not available in most of the cities, (ii) in the networks with available loop detector data, the loop detectors cover only a fraction of streets in the network, and (iii) the data coming from various loop detectors is prone to bias and inaccuracy, which makes the data cleaning and processing cumbersome.

What will this project accomplish? This project will rely on the recently published loop detector data from more than 40 cities over the globe and simulation experiments to investigate three main impacting factors on the network MFD: (i) the distribution of the loop detectors over the network, (ii) the distribution of loop detectors on the links, and (iii) the extent of the coverage area of the loop detectors and its relationship with the accuracy of the resulting MFD. As a result of this project, we aim to develop a robust method to accurately estimate the network MFDs considering the aforementioned impacting factors.