  Recent advancements in Connected Vehicle (CV) technology have prompted
research on leveraging CV data for more effective traffic management. Despite
the low penetration rate, such detailed CV data has demonstrated great
potential in improving traffic signal performance. However, existing studies
share a common shortcoming in that they all ignore traffic flow estimation
errors in their modeling process, which is inevitable due to the sampling
observation nature of CVs. This study proposes a CV data-driven robust
optimization framework for traffic signal timing accounting for both traffic
flow variability and estimation errors. First, we propose a general CV
data-driven optimization model that can be widely applied to various signalized
intersection scenarios including under-/over-saturated and fixed-/real-time.
Then, we propose a novel data-driven uncertainty set of arrival rates based on
the bounds information derived from CVs, which circumvents the error-prone
arrival rate estimation process. Finally, a CV data-driven robust optimization
model (CV-RO) is formulated to explicitly handle arrival rate uncertainties. By
means of the robust counterpart approach, this robust optimization problem can
be equalized to a deterministic mixed-integer linear programming problem with
an exact solution. The evaluation results highlight the superior performance of
the CV-RO model compared to the deterministic model and traditional methods
across various scenarios: different penetration rates, traffic demands, and
control types. Notably, the CV-RO model demonstrates its excellence at lower CV
penetration rates and in the presence of different traffic flow fluctuation
levels, affirming its effectiveness and robustness.
