  This paper analyzes the potential effects of connected and automated vehicles
on saturation headway and capacity at signalized intersections. A signalized
intersection is created in Vissim as a testbed, where four vehicle types are
modeled and tested: (I) human-driven vehicles (HVs), (II) connected vehicles
(CVs), (III) automated vehicles (AVs), and (IV) connected automated vehicles
(CAVs). Various scenarios are defined based on different market penetration
rates of these four vehicle types. AVs are assumed to move more cautiously
compared to human drivers. CVs and CAVs are supposed to receive information
about the future state of traffic lights and adjust their speeds to avoid
stopping at the intersection. As a result, their movements are expected to be
smoother with a lower number of stops. The effects of these vehicle types in
mixed traffic are investigated in terms of saturation headway, capacity, travel
time, delay, and queue length in different lane groups of an intersection. A
Python script code developed by Vissim is used to provide the communication
between the signal controller and CVs and CAVs to adjust their speeds
accordingly. The results show that increasing CV and CAV market penetration
rate reduces saturation headway and consequently increases capacity at
signalized intersections. On the other hand, increasing the AV market
penetration rate deteriorates traffic operations. Results also indicate that
the highest increase (80%) and decrease (20%) in lane group capacity are
observed, respectively, in a traffic stream of 100% CAVs and 100% AVs.
