## 4 Results
### Simulation Validation
#### 4.2.3 Case Study  
After observing that the value of the potential field exceeds the threshold, it is crucial to determine the extent of the capacity reduction in the bottleneck section. In this simulation, we set a random bottleneck to occupy two lanes. That is to say, only one lane of the bottleneck section could pass usually. The scope of control is 22,440.60 m (the direction is from Zhuhai to Hong Kong). When the control strategy is triggered, the bridge entrance lane will be closed. Vehicles are prohibited from entering the main road of the bridge after an emergency occurs to avoid increasing the evacuation pressure. The traffic control strategy is completed when no vehicles are located upstream of the bottleneck section. To compare the traffic performances of the different strategies, simulations under low, middle, and high traffic demands (900, 1800, and 2700 veh/h, respectively) are run. We compare our method with the following methods. No control means no guidance strategy for the vehicles located upstream of the bottleneck section after a bottleneck section occurs. DQN-VSL (deep Q learning-based variable speed limit control), OL-VSL (open loop-based variable speed limit control), and MVSPC (multi-segment variable speed predictive cooperative control) are compared in this paper. The parameters of the MVSPC strategy are shown in Table 3.  
To analyze the simulation results and compare the traffic performances of the different strategies, WT, PC, and average emissions are used to assess efficiency, safety, and environment, as shown in Figures 4, 5 and 6, respectively. More details can be seen in Tables 4-6. Compared with the no-control condition, the performance improved by 48.49%, 19.95%, and 62.44% in efficiency and 61.65%, 59.36%, and 95.32% in terms of safety under low-, middle-, and high-traffic demand environments, respectively. These improvements \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Type 1** & **Type 2** & **Type 3** \\ \hline car-follow model & Krauss & Krauss & IDM \\ acceleration (m/s\({}^{2}\)) & (\(-\)4.5, 2.6) & (\(-\)4.5, 2.6) & (\(-\)4.5, 2.6) \\ length (m) & 5 & 6 & 5 \\ minGap (m) & 10 & 10 & 8 \\ speedDev (m/s) & 0.5 & 0.3 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The basic parameters of vehicles. Figure 3: The parameters of the traffic flow model. outperform those observed with no control. This is because our control method reduces acceleration and frequently avoids changes in vehicle state, thus decreasing exhaust emissions caused by these acceleration and deceleration behaviors. In terms of emissions, our strategy has achieved improvements of 63.87%, 58.82%, 65.35%, and 68.59% in CO\({}_{2}\), CO, NOx, and PMx under low traffic demand; improvements of 46.02%, 34.51%, 50.47%, 49.11%, in CO\({}_{2}\), CO, NOx, PMx under medium traffic demand; and improvements of 26.4%, 14.11%, 26.53%, and 32.90%, in CO\({}_{2}\), CO, NOx, PMx under high traffic demand. The results show that our integrated control can bring many environmental benefits. Here is a perspective to illustrate this. For example, the waiting time of vehicles was notably reduced, which in turn decreased the exhaust emissions from vehicles idling in queues. Compared to the other two methods, ours may be outperformed under certain conditions, but the performance of our proposed strategy is consistently good on average. For example, the DQN-VSL method outperforms the other three methods tested in terms of emissions, but it is worse in terms of WT, and PC, especially, improving by 34.28% under high traffic demands. In terms of safety, the performance of the OL-VSL method gradually decreases as the traffic demands increase. The case simulation aims to visually illustrate the changes in traffic speed across each segment. In this scenario, an uncertainty event occurs due to the closure of two lanes, resulting in a traffic flow rate of 1500 vehicles per hour (veh/h). The 8000 m upstream of the bottleneck point is divided into eight segments, each measuring 1000 m in length.