  Connected Vehicles (CVs) have the potential to significantly increase the
safety, mobility, and environmental benefits of transportation applications. In
this research, we have developed a real time adaptive traffic signal control
algorithm that utilizes only CV data to compute the signal timing parameters
for an urban arterial in the near congested condition. We have used a machine
learning based short term traffic forecasting model to predict the overall
traffic counts in CV based platoons. Using a multi objective optimization
technique, we compute the green interval time for each intersection using CV
based platoons. Later, we dynamically adjust intersection offsets in real time,
so the vehicles in the major street can experience improved operational
conditions compared to loop detector based actuated coordinated signal control.
Using a 3 mile long simulated corridor of US 29 in Greenville, SC, we have
evaluated the performance of our CV based adaptive signal control. For the next
time interval, using only 5% CV data, the Root Mean Square Error of the machine
learning based prediction is 10 vehicles. Our analysis reveals that the CV
based adaptive signal control improves operational conditions in the major
street compared to the actuated coordinated scenario. Also, using only CV data,
the operational performance improves even for a low CV penetration (5% CV), and
the benefit increases with increasing CV penetration. We can provide
operational benefits to both CVs and non CVs with the limited data from 5% CVs,
with 5.6% average speed increase, and 66.7% and 32.4% reduction in average
maximum queue length and stopped delay, respectively, in major street
coordinated direction compared to the actuated coordinated scenario in the same
direction.
