# Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
## 1 Introduction  
The escalating traffic congestion on the road network has led to extensive negative impacts on society, including time and productivity loss, increased air pollution, and decreased safety [1]. With the rapid development and cross-sector integration of industries such as transportation, automatic control, artificial intelligence, and communication, connected and autonomous vehicle (CAV) technology provides a new approach to alleviating traffic congestion and improving traffic performance [2, 3].  
According to the level of vehicle automation, automated vehicles are categorized into six levels: no automation (Level 0), driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and full automation (Level 5) [4, 5]. Taking into account the level of autonomy and connectivity of vehicles, they can be classified as human-driven vehicles (HDVs), connected human-driven vehicles (CHVs), autonomous vehicles (AVs), and CAVs [6, 7]. Specifically, these vehicles can be described as follows: (1) HDVs are traditional motor vehicles that are controlled and operated by human drivers; (2) CHVs are traditional vehicles operated by humans, equipped with advanced communication technologies, and their driving behavior aligns with that of manually driven vehicles; (3) AVs are advanced transportation devices that utilizeadvanced sensing technology, artificial intelligence, and autonomous decision-making systems to drive without human operation; (4) CAVs can utilize vehicle-to-infrastructure (V2I) communication technology to perceive their surroundings in real-time and coordinate the sensing and sharing of travel information through vehicle-to-vehicle (V2V) communication technology [8].  
The vehicle driving modes are steadily transitioning from manual driving to assisted driving, autonomous driving, human-vehicle hybrid driving, and even advanced self-driving. Therefore, mixed traffic flow has emerged as a hot topic due to the increasing development and implementation of intelligent transportation systems (ITSs). It involves efficiently managing and coordinating the traffic flow to enhance the efficiency, safety, and sustainability of the transportation system. In recent years, connected and autonomous vehicles have provided a new approach to alleviating traffic congestion. The jam-absorption driving (JAD) strategy [9; 10] is a technique utilized in vehicle automation and autonomous driving to mitigate the impact of traffic jams. The core principle of JAD is to reduce congestion by dynamically adjusting the headway between individual vehicles.  
This study introduces the JAD strategy to optimize traffic flow by regulating vehicles' longitudinal behaviors (such as acceleration and deceleration) and lateral behaviors (such as lane change). Specifically, the JAD strategy utilizes perception data (e.g., road environment and the state of preceding vehicles) and vehicles' dynamic information (e.g., speed, acceleration, and position) to adjust driving trajectories and speed variations. This approach focuses on exploring the interactions of heterogeneous traffic flow, including cars and trucks equipped with CAVs, AVs, and HDVs, on multi-lane ring roads and highways.  
The remainder of this paper is organized as follows. The next section provides a brief review of related work. Section 3 establishes the overall simulation framework. Section 4 details the JAD strategy, the car-following (CF) model, and the lane-change model used in mixed traffic flow. It also explores the process of generating heterogeneous traffic flow and specifies surrogate safety measures in the simulation experiments. In Section 5, experimental scenarios are set up for the circular road and highway with different proportions of mixed traffic flow, and the results evaluating the performance of mixed traffic are presented. Section 6 presents the conclusions and some topics for future research.