Assessing the Impact of Automated and Connected Automated Vehicles on Virginia Freeways

Abstract

This study assesses the impact of the introduction of connected and automated vehicles on Virginia freeway corridors. Three vehicle types: legacy vehicles (LV), automated vehicles (AV), and connected automated vehicles (CAV), were considered in mixed traffic scenarios. Previous relevant studies were reviewed and the proper operating parameters for LV, AV, and CAV identified. AV and CAV driving behavior models were developed in the VISSIM environment. According to the basic freeway test network results, AV and CAV increase road capacity by 29% and 91%. In the merging freeway test network, AV and CAV increase road capacity by 48% and 60% compared with LV, respectively. A model with diverse LV, AV, and CAV market penetration and diverse traffic demand was tested on I-95 in Virginia, where the research team tested the speed and throughput. Under the current traffic demand, the average speed was higher when there were more AV and no CAV in the traffic flow. However, the average speed of CAV in a congested segment is higher than LV. In the case of throughput, CAV shows poor performance under current traffic demand. With increased traffic demand, high penetrations of AV and CAV present better performance because of their short headway and homogeneity. Therefore, the study predicts that in the future, as the traffic demand grows, AV and CAV can reduce traffic congestion.

Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

“…Meanwhile, speed limit is also indicated as an important variable in freeway capacity, where higher speed limits leads to improvement of capacity. Another interesting finding based on demand fluctuation is reported in [51]. This study evaluated the impacts of (C)AVs on throughput in a Freeway segment.…”

“…Several studies utilized MIXIC and developed models based on MIXIC to conduct impact assessments of CAVs [6,[49][50][51][52][53][54]. For instance, [49] used MIXIC model to evaluate the influence of CACC on traffic flow characteristics.…”

“…This study approximates the driving behaviors of CAVs using CACC vehicles while cruising. Similarly, [51] recently used MIXIC model with default parameters values to replicate the driving behavior of (C)AVs and conduct impact assessments.…”

A fuel consumption model for mixed traffic flow in multiple connected and autonomous scenarios

“…These constructed models fall into two categories: the models based on engine efficiencies, such as Advisor and Autonomie, and the models based on traffic parameters, such as trajectory algorithm optimization [32][33][34]. From macroscopic evaluation to quantitative calculation, from rough evaluation to precise measurement, the methods of CAV energy consumption assessment cross a significant threshold [35][36][37]. For the models based on traffic parameters, the energy/emission models are generally classified into three categories: driving cycle, second-by-second speed, and the power demand-based models, according to the driving state of vehicles [38].…”

Assessing the Impacts of Autonomous Vehicles on Road Congestion Using Microsimulation

“…Examples of car-following models include the Helly model [1], Gipps model (GM) [2], Optimal-Velocity model (OVM) [3], Collision-Avoidance model (CA model) [4], Intelligent-Driver model (IDM) [5], and IDM+ [6]. Microsimulation modelling and car-following models have been widely used to assess the impact of autonomousvehicle operation, simulation and driving [1][2][3][4][5][6][7][8][9][10][11][12][13]. The impact of autonomous vehicles on congestion has not yet been concluded.…”