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The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks
Microscopic traffic simulators that simulate realistic traffic flow are
crucial in studying, understanding and evaluating the fuel usage and mobility
effects of having a higher number of autonomous vehicles (AVs) in traffic under
realistic mixed traffic conditions including both autonomous and non-autonomous
vehicles. In this paper, L4-L5 AVs with varying penetration rates in total
traffic flow were simulated using the microscopic traffic simulator Vissim on
urban, mixed and freeway roadways. The roadways used in these simulations were
replicas of real roadways in and around Columbus, Ohio, including an AV shuttle
routes in operation. The road-specific information regarding each roadway, such
as the number of traffic lights and positions, number of STOP signs and
positions, and speed limits, were gathered using OpenStreetMap with SUMO. In
simulating L4-L5 AVs, the All-Knowing CoEXist AV and a vehicle with Wiedemann
74 driver were taken to represent AV and non-AV driving, respectively. Then,
the driving behaviors, such as headway time and car following, desired
acceleration and deceleration profiles of AV, and non-AV car following and lane
change models were modified. The effect of having varying penetration rates of
L4-L5 AVs were then evaluated using criteria such as average fuel consumption,
existence of queues and their average/maximum length, total number of vehicles
in the simulation, average delay experience by all vehicles, total number of
stops experienced by all vehicles, and total emission of CO, NOx and volatile
organic compounds (VOC) from the vehicles in the simulation. The results show
that while increasing penetration rates of L4-L5 AVs generally improve overall
fuel efficiency and mobility of the traffic network, there were also cases when
the opposite trend was observed