Modeling and analysis of human-machine mixed traffic flow considering the influence of the trust level toward autonomous vehicles

The emerging autonomous vehicles (AVs) are expected to bring pronounced evolutions in transport systems. This study explores the characteristics of mixed traffic flow with both AVs and human drivers in urban bottlenecks. We investigate the influences of penetration rate (PR) of AVs on the performances concerning traffic efficiency and safety in urban bottlenecks with road width reduction. We developed a cellular automata model (CAM) to realize the microscope simulation of the mixed traffic flow with both AVs and traditional vehicles manipulated by human drivers. The divergences in driving behavior of human drivers and AVs in terms of car-following, lane-change and free-driving are fully delineated and integrated in the simulation. The results demonstrate that the traffic flow stability firstly decreases and then increases with the PRs of AVs and in mixed traffic flow. When PR of AVs reaches 100%, the traffic flow is stabilized and shows high travel speed, indicating higher traffic efficiency. The lane-changing frequency increases when PR of AVs increases, reaching the maximum value at the PRs of 15%-25%, and then gradually drops. The lane-changing frequencies under the scenarios of all AVs are found to be smaller than the scenarios of all human drivers. The actual road capacity is reduced when PR of AVs increases at first, reaches lowest at the PR of 15%-25%, and then gradually rebounds. The risk of collision gradually increases with PRs of AVs, and then reaches the maximum value at the PR of 25%-30%. As PR of AVs continues to increase, the risk will keep decreasing to 0. The findings provide a comprehensive investigation of how the AVs will influence traffic efficiency and safety from different aspects, which are basic for the development and planning of AVs in the future.

This paper investigates the traffic flow characteristics of mixed stream of autonomous and human-driven vehicles. The proposed model aims at understanding the fundamental properties of mixed flow of human-driven (N) and autonomous vehicles (AV) such as headway, capacity, and delay at signalized intersections. This is challenging because of intrinsic differences between longitudinal driving characteristics of these two types of vehicles and the convoluted dynamics of car following situation within various combinations of AV and human-driven vehicles. The expected headway of the mixed flow is determined based on the penetration rate of AV and the headways between two successive AV-AV, AV-N, N-AV, and N-N. Furthermore, the upper and lower bounds of mixed flow headway is presented. The theoretical headway is validated by microsimulation data. The estimated headways are then incorporated to derive the delay of a mixed flow at a signalized 2-lane link. Four combination of (i) mixed lanes, (ii) dedicated lanes for AV and human-driven vehicles, (iii) one mixed lane and one AV dedicated lane, and (iv) one mixed lane and one human-driven vehicle dedicated lane are considered. The results demonstrate the performance of the four lane configurations for various stages of AV deployment penetration rate.

<div class="section abstract"><div class="htmlview paragraph">With the acceleration of urbanization, developing public transportation is an important means to alleviate travel pressure and traffic congestion in cities. Work zones that occupy urban road resources affect normal vehicle operations, leading to reduced vehicle efficiency. Based on this, the paper conducts research on traffic flow modeling and simulation analysis for work zones in a vehicle-road coordination environment. Based on the Gipps model and the SCAT model, optimizations and improvements were made to the following and lane-changing rules for three types of vehicles: human-driven vehicles (HVs), autonomous and connected vehicles (CAVs), and buses. Using cellular automata theory, it constructs a running model suitable for mixed traffic flow vehicles in work zones. MATLAB software is utilized to simulate the operation process of vehicles under work zone scenarios, analyzing changes in traffic flow from two directions: road geometric conditions (speed limits) and traffic flow states (volume, vehicle type ratios, etc.). The study analyzes the impact of vehicle motion behavior on mixed traffic flow under different road scenarios, and examines the effect of work zones on time-space diagrams. It concludes that, at the same density, the higher the proportion of CAVs, the lower the probability of congestion. When the traffic flow is in a free-flow state, the speeds of vehicles under different speed limit conditions are not the same. When the traffic density is around 45 veh/km, the traffic volume reaches its maximum. At the same density, the higher the proportion of CAVs, the greater the overall traffic flow speed and the higher the capacity of the road section.This research provides support for the improvement of theories related to traffic flow operations in work zones under a vehicle-road coordination environment.</div></div>

Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction

Modeling the mixed traffic capacity of minor roads at a priority intersection

Study on mixed traffic of autonomous vehicles and human-driven vehicles with different cyber interaction approaches

Stability analysis and connected vehicles management for mixed traffic flow with platoons of connected automated vehicles

Mitigating traffic oscillation through control of connected automated vehicles: A cellular automata simulation

Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.

The study investigates the impacts of isolated and dedicated lanes for autonomous vehicles on freeway traffic flow, focusing on mixed traffic scenarios where autonomous vehicles coexist with human-driven vehicles. Given the anticipated long-term coexistence and the complex dynamics of mixed traffic, dedicated lanes for autonomous vehicles have been explored globally, but their benefits remain unclear. This research constructs detailed models of real-world freeway scenarios, calibrated with parameters from car-following models tailored for autonomous driving. By comparing traffic flow under various dedicated lane policies with mixed flow environments, the study reveals nuanced effects on overall traffic characteristics, particularly as the market penetration rate of autonomous vehicles increases. The findings show that lane policies significantly influence traffic throughput and efficiency, with certain policies demonstrating clear advantages at higher autonomous vehicles penetration levels. Results show that bidirectional and unidirectional isolation policies enhance traffic efficiency at higher market penetration rate (above 70%), while less restrictive policies are more effective at lower market penetration rate (below 50%). These findings emphasize the need to tailor lane management strategies to autonomous vehicles’ market penetration rate, offering valuable insights for optimizing traffic flow and easing the transition to mixed traffic environments.

A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles

Efficiency and fuel consumption of mixed traffic flow with lane management of CAVs

Autonomous vehicles (AVs) will significantly affect road traffic management and control in the near future. In particular, AVs change the traffic flow characteristics of roads when mixed with human-driven vehicles (HVs). The penetration rate of AVs and their arrangement in a platoon of vehicles can influence the saturation flow of the traffic. Therefore, optimal lane management and signal timing design for arterials become crucial and challenging. We propose an integrated lane management and signal control (ILMSC) algorithm that minimizes the vehicle delay at an isolated and undersaturated intersection. Analytical models are proposed for estimating the saturation flow of the mixed traffic and the vehicle delay of a two-lane cyclic interrupted flow. Two alternative lane management policies are adopted: (i) dedicated lanes, and (ii) mixed-mixed lanes. Comprehensive microsimulation experiments emphasize the effectiveness of the proposed traffic control algorithm.

Mixed traffic capacity estimation of autonomous vehicles impact based on empirical data

With the emerging application of low-level driving automation technology, heterogeneous traffic flow mixed with human-driven vehicles and low-level autonomous vehicles is dawning. In this context, it is imperative to investigate its effect on mixed traffic flow. As a key component for adaptive cruise control (ACC) which is a practical low-level application of driving automation, the time gap policy determines the dynamic of ACC-equipped vehicles and plays a crucial role in traffic flow stability and efficiency. There are two main time gap policies used for ACC at present, namely, constant time gap (CTG) policy and variable time gap (VTG) policy. In this study, we carried out a detailed comparison between these time gap policies to investigate their potential effect on mixed traffic flow, where the analytical- and simulation-based approaches are both considered. Analytical results show that VTG policy is superior to CTG policy in stabilizing the mixed traffic flow. In addition, numerical simulations are also conducted and simulation results further support the analytical results. As for throughput, there is no difference between CTG policy and VTG policy in analytical progress when the same time gap is set at the equilibrium. However, simulation results based on an on-ramp scenario show that the throughput of mixed traffic flow with VTG policy is slightly higher than that of CTG policy. Meanwhile, the scatter of mixed traffic flow with VTG policy in the flow-density diagram gradually clusters in the middle range of density (i.e., 20–40 veh/km) with the increase of the penetration rates of ACC vehicles, where the traffic flow operates more efficiently. These results indicate that VTG policy is better than CTG policy when designing controllers for ACC in the context of traffic flow operation and control.