  Time-dependent fixed-time control is a cost-effective control method that is
widely employed at signalized intersections in numerous countries. Existing
optimization models rely on traditional delay models with specific assumptions
regarding vehicle arrivals. Recent advancements in intelligent mobility have
led to development of high-resolution trajectory data of connected vehicles
(CVs), thereby providing opportunities for improving fixed-time signal control.
Taking advantage of CV trajectories, this study proposes a cumulative flow
diagram (CFD)-based signal timing optimization method for fixed-time signal
control at isolated intersections, which includes a CFD model and a
multi-objective optimization model. The CFD model is formulated to profile the
time-dependent vehicle arrival and departure processes under varying signal
timing plans, where the intersection demand is estimated based on a weighted
maximum likelihood estimation method. Then, a CFD-based multi-objective
optimization model is proposed for both undersaturated and oversaturated
traffic conditions. The primary objective is to minimize the exceeded queue
dissipation time, whereas the secondary objective is to minimize the average
delay at the intersection. Considering the data-driven property of the CFD
model, a bi-level particle swarm optimization-based algorithm is then specially
designed to solve the optimal cycle length (and the reference point if it is
considered) and green ratios separately. The proposed method is evaluated based
on simulation data and compared with Synchro. The results indicate that the
proposed method outperforms Synchro under various traffic conditions in terms
of average delay and queue since the time-dependent vehicle arrivals during the
cycle are considered in a CV trajectory data-driven way.
