The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks

Abstract

With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. 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 AVs, and the non-AVs' 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.

Potential Pollutant Emission Effects of Connected and Automated Vehicles in a Mixed Traffic Flow Context for Different Road Types

“…1 shows the conceptual illustration of the followed methodology, and some assumptions are reported in Table I. Since real-world data related to CAVs are quite scarce, microsimulation appears as a relevant tool to investigate the potential impacts of their driving behavior [50], [18], [35], [44]. The present work relies on a microsimulation traffic platform to explore the impacts of CAVs circulating and sharing the roads with CVs.…”

“…Considerable research efforts have been devoted to understanding how driving behavior parameters of CAVs affect infrastructure capacity, energy consumption, and emissions. Traffic-modeling parameters are rarely tested against real data for two reasons: first, data is not widely available, since car industry manufacturers have very restrictive data sharing policies; and second, technology is still under development [44], [45]. Some specific models have been proposed to reflect CAVs driving behavior.…”

“…The CoExist Project showed this latter approach could be an alternative, which can be easily implemented within the traffic simulator VISSIM to perform scenario-based evaluations to assess variations in parameters under typical traffic situations [18]. In fact, this approach has been followed by various researchers ( [50], [51], [44]). Hence, due to the absence of real-based data on the car-following algorithms used in the CAV industry, a frequently used approach to explore and anticipate CAVs impacts is to modify the current Car-Following Parameters (CFP) and adapt lane change models in the traffic simulation and modeling platforms to simulate the impact of CAVs operation on the network [52].…”

“…This model is composed of nine parameters related to the standstill distance (CC0), headway time (CC1), following variation (CC2), the threshold for entering following (CC3), following thresholds (CC4/CC5), speed dependency of oscillation (CC6), oscillation acceleration (CC7), standstill acceleration (CC8) and acceleration at 80 km.h-1 (CC9) ( [13], [61]). A great deal of emphasis has been placed on taking advantage of microscopic simulation tools for emulating the CAVs behavior as well as simulating their interactions within the road environment [44]. In particular, numerous studies have proposed to use the Wiedemann 99 car-following model and a suitable range of values for the above parameters to model CAVs in different types of roads and traffic conditions ( [18], [23], [24], [45], [50], [51], [54], [55]).…”

“…In summary, most of the existing literature focuses on assessing the impacts of CAVs deployment in terms of capacity, congestion, safety, fuel consumption, and CO2 emissions [50], [18], [35], [43], [44]. However, few studies have focused on air quality impacts, mainly concerning NOx emissions [17], [18], [49].…”