# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 1 Introduction  
### Background  
Due to the rapid development of vehicular networking technology, regular human-driven traffic flow is gradually evolving into a mixed traffic flow where human-driven vehicles (HVs) and connected-automated vehicles (CAVs) coexist. Previous studies have reported that the deployment of CAVs is a long-term process, with heterogeneous traffic flow as a required stage [1; 2]. The fast-growing trend of intelligent transportation systems (ITS) including active traffic management (ATM) [3] brings new opportunities and challenges to traffic management. ATM generally takes freeways and expressways as research objects, employing various management and control strategies to tackle regular and occasional congestion issues based on the present and expected traffic circumstances [4]. As one of the ATM systems, hard shoulder running (HSR) [5] has received considerable critical attention since it utilizes existing physical infrastructure and significantly reduces the cost of reconstruction [6]. Initially, the hard shoulder was primarily intended for safety [7]. It provides a safe temporary parking spot for malfunctioning vehicles on a freeway with high volumes and speeds, ensuring driving safety while not interfering with normal traffic. According to recent research, temporarily opening hard shoulders as additional lanes can successfully ease congestion [8; 9; 10; 11; 12]. Existing studies of HSR strategies usually focus on traditional road scenarios [13; 14; 15; 16; 17]. For example, [PERSON] et al. [12] proposed dynamic hard shoulder running (D-HSR) and applied different D-HSR strategies to study accident managementusing micro-simulation. He suggested that only the shoulder 0.5 miles upstream and downstream of the accident location needed to be opened, and that the D-HSR strategy was more appropriate for property damage only (PDO) events. A hybrid hard shoulder running operation system (H-HSROS) proposed by [PERSON] et al. [18] enables transit departments to operate HSR safely and efficiently. [PERSON] et al. [6] employed the cellular transmission model to conduct linear programming modeling of the hard shoulder running, ramp metering, and speed coordination control measures and took the minimum total delay as the optimization goal. The researcher modified the optimization goal to include a safety factor based on the frequency of shoulder changes, allowing the objective function to represent trade-offs between efficiency and safety. [PERSON] et al. [19] applied model predictive control (MPC) to introduce a new dynamic control strategy based on variable speed limit (VSL) and hard shoulder running (HSR), and discovered that the combined effect of various intelligent transportation system (ITS) strategies was far greater than the effect of a single strategy through experiments. [PERSON] et al. [20] proposed a VSL and HSR joint control strategy based on reinforcement Q-Learning, which established a connection between traffic flow data and the ATM control strategy during the self-learning process. Despite several studies on HSR, with the rapid rise of CAV market penetration, few researchers have investigated HSR in a hybrid network environment. Most of the earlier research focused on other ATM strategies in a hybrid network environment [21; 22; 23; 24; 25; 26; 27]. Numerous studies have demonstrated that the addition of CAVs will modify the headway and speed of traffic flow, resulting in changes in road capacity [28; 29; 30]. Therefore, current HSR research in traditional traffic scenarios cannot meet these requirements. To develop a hard shoulder opening approach that is more suitable for a hybrid network environment, this paper analyzed the features of traffic speed breakdown in the hybrid network environment and proposed a dynamic hard shoulder opening strategy based on a hidden Markov model (HMM). The remaining sections of this article are organized as follows.