# Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles
### Background  
Researchers demonstrate a significant inclination to reduce time and space headway by AVs either through normal traffic pattern [15] or platooning traffic pattern [16; 17; 18]. While achieving enhanced capacities in light traffic seems advantageous, careful consideration of congestion avoidance becomes crucial in denser traffic to prevent delays and surpass traffic breakdowns [19]. In a large body of literature, ACC models are employed to simulate the longitudinal movement behavior of autonomous vehicles (AVs). The expected effect of ACC systems on traffic flow and stability is viewed as favorable. [PERSON] et al. [20] revealed that cars equipped with ACC resulted in significant decreases in traffic congestion. Similarly, [PERSON] and [PERSON] [21], in their study, have shown that headway settings have a remarkable effect on the stability of traffic flow. The studies demonstrated that the shortest ACC headway setting can significantly improve road capacity compared with existing human-driven vehicle traffic. Furthermore, [PERSON] and [PERSON] [22] mentioned that extending ACC to the more developed CACCs allows for closer following distances, in which the advantages of the increase in traffic flow appear more clearly. It is worth mentioning that researchers have also considered the impact of AV penetration rate (percentage of ACC or other ADAS-equipped vehicles in mixed traffic), as it has significant influence on road capacities. For instance, [PERSON] et al. [23] revealed that an increase in traffic capacity of 40% was obtained when AV penetration rate reached 100%. [PERSON] et al. [24] stated that increasing ACC penetration from 20% to 40% results in a higher increase in traffic capacity than increasing ACC penetration from 0% to 20%. However, there are some other researchers who acknowledged the adverse effects of ACC on traffic capacity. [PERSON] et al. [25] demonstrate that a negative impact on traffic flow occurs at low penetration rates, while capacity enhancements are observed at autonomous vehicle (AV) rates exceeding 70%. In contrast, [PERSON] et al. [26] reported that a proportion of less than 25% of ACC vehicles is beneficial to roadway capacity, while the wider deployment of ACC vehicles significantly harms capacity. [PERSON] and [PERSON] [27] revealed that the capacity of the studied intersection decreased by using autonomous vehicles without communication. In general, the divergent perspectives on the impact of adaptive cruise control (ACC) range from positive to negative evaluations. It is important to note that most insights into ACC's effects stem from the validation of research conclusions employing simulation-based methodologies, rather than direct field testing. For this reason, field experiments yield more dependable conclusions, as they rely on observations in authentic, real-world conditions. However, conducting field tests presents challenges, demanding substantial resources and meticulous organization to ensure accurate testing. Refs. [28; 29; 30] have carried out a simultaneous empirical field experiments involving several cars to assess the real-world effects of ACC. This assessment involved measuring both the car-following space and time headways. Similarly, [PERSON] et al. [31] and [PERSON] and [PERSON] [21] also conducted empirical field tests to explore the influence of various parameters, including the response time of controllers, available time headway settings, and time lag. The outcomes revealed notably extended time ranges and time lags, surpassing the values documented in existing literature. This discrepancy emphasizes the importance of practical field investigations in uncovering nuances and deviations that may not be adequately captured by theoretical or simulated studies.