During the 21st IEEE International Conference on Intelligent Transportation Systems who takes place at Maui – Hawai from November 4th to November 7th, Carlos CANUDAS DE WIT presented :
– “2D-LWR in Large-Scale Network with Space Dependent Fundamental Diagram” : Traffic modeling of large-scale urban networks is a challenging task. In the literature, the network is mainly assumed to be homogeneous. However, in a large-scale scenario, it is unlikely that the traffic network characteristics–such as speed limit, number of lanes, or the network geometry–remain constant throughout the network. Therefore, we introduce a two dimensional macroscopic model for large-scale traffic networks where the fundamental diagram is space-dependent and varies with respect to the area considered. We simulate our model and compare the results with those obtained by microsimulation.
A work combined by Stéphane Mollier, Maria Laura Delle Monache & Carlos Canudas de Wit
– “Estimation of Fundamental Diagrams in Large-Scale Traffic Networks with Scarce Sensor Measurements” : The macroscopic fundamental diagram (MFD) relates space–mean flow density and the speed of an entire network. We present a method for the estimation of a “normalized” MFD with the goal to compute specific Fundamental Diagram in places where loop sensors data is no available. The methodology allows using some data from different points in the city and possibly combining several kinds of information. To this aim, we tackle at least three major concerns: the data dispersion, the sparsity of the data, and the role of the link (with data) within the network. To preserve the information we decided to treat it as two-dimensional signals (images), so we based our estimation algorithm on image analysis, preserving data veracity until the last steps (instead of first matching curves that induce a first approximation). Then we use image classification and filtering tools for merging of main features and scaling. Finally, just the Floating Car Data (FCD) is used to map back the general form to the specific road where sensors are missing. We obtained a representation of the street by means of its likelihood with other links within the same network.
A work combined by Olga Lucia Quintero Montoya & Carlos Canudas de Wit