## 4 Results  
### Performance Evaluation  
This paper selects some indicators from three aspects of traffic efficiency, safety, and environmental sustainability to assess the performance of control and management strategy.  
In terms of evaluating traffic management efficacy, we use the waiting time (WT) metric, which indicates the average duration that each vehicle spends in a stationary or near-stationary state within the network. This measure reflects the efficiency of traffic flow by capturing the delays that vehicles experience due to congestion, bottlenecks, or traffic control measures. In addition, a reduction in WT suggests that vehicles spend less time idling, leading to improved flow, reduced travel times, and potentially lower emissions due to decreased engine idling. As such, WT serves as a comprehensive indicator of both operational efficiency and environmental impact in traffic management strategies.  
In terms of safety, vehicles are equipped with an SSM device that logs the conflicts between the vehicle and other traffic participants (currently only vehicles) and corresponding safety surrogate measures. In this study, the selected threshold values for TTC, the deceleration rate to avoid a crash (DRAC), and post-encroachment time (PET) are presentedas reference thresholds. The value of the ego vehicle equipped with an SSM Device exceeds the threshold, leading to an increase in the number of potential collisions (PC). Notably, the commonly used threshold of TTC < 3.0 s is well-established in traffic safety literature and is frequently applied on highways and in tunnel scenarios. However, it is essential to recognize that these thresholds are not absolute and can be adjusted based on specific traffic environments and conditions. For instance, adverse weather conditions, such as rain or snow, can increase the effective collision time threshold due to reduced visibility and traction, necessitating a longer response time for vehicles. Therefore, the applicability of these thresholds should be considered within the unique context of super-long-span bridges, where factors such as traffic density, vehicle dynamics, and structural characteristics may further influence safe driving behaviors and collision risks.  
In terms of environmental sustainability, we extend the fuel emission model within the simulation of the urban mobility (SUMO) framework, which relies primarily on the Handbook Emission Factors for Road Transport (HBEFA) database. This extension allows for the incorporation of emission coefficients specific to various vehicle types under differing traffic conditions, providing a more nuanced understanding of vehicle emissions. The model enables the calculation of emissions of key pollutants, including carbon monoxide (CO), carbon dioxide (CO\({}_{2}\)), particulate matter (PMx), and nitrogen oxides (NOx), for each segment of the roadway during the traffic control and management period. By capturing the dynamic nature of emissions in relation to traffic flow and management interventions, this approach facilitates a comprehensive assessment of the environmental impacts of traffic management strategies, highlighting areas for potential improvement in reducing overall emissions and promoting cleaner transportation options.