Chao Zhang
Chao Zhang is Research Scientist at Google Research. His research interests concentrate on transportation science, systems modeling, and large-scale simulations. He currently focuses on solving large-scale real-world problems by leveraging techniques from simulation, operations research, transportation & logistics, and machine learning.
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Being widely adopted by the transportation and planning practitioners, the fundamental diagram (FD) is the primary tool used to relate the key macroscopic traffic variables of speed, flow, and density. We empirically analyze the relation between vehicular space-mean speeds and flows given different signal settings and postulate a parsimonious parametric function form of the traditional FD where its function parameters are explicitly modeled as a function of the signal plan factors. We validate the proposed formulation using data from signalized urban road segments in Salt Lake City, Utah, USA. The proposed formulation builds our understanding of how changes to signal settings impact the FDs, and more generally the congestion patterns, of signalized urban segments.
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