## 5 Evaluation
### Scenario Setting  
In a prior study [24], we meticulously designed a traffic simulation that incorporated numerous traffic rules, including overtaking allowance and connected junctions with roads to form a map. We also set up a Poisson vehicle arrival pattern to simulate vehicle entriesand exits. The experimental results indicated that these factors did not significantly impact the attacker, while the success rate of an attacker recognizing a vehicle dropped exponentially as the vehicle passed through several intersections (i.e., changed pseudonyms multiple times). This is because, during other times on the road when the vehicle did not change its pseudonym, attackers could easily track its trajectory segment. Since this paper focuses on the success rate of changing pseudonyms once, the experiment was simplified to only involve passing through junctions of different shapes, removing additional influencing factors.  
As illustrated in Figure 5, the simulation encompasses five commonly encountered junction shapes. These include two T-junctions with three potential exits, two crossroads with four possible exits, and a multi-way junction offering five potential exits. The mix zones, depicted as red circles, extend beyond the boundaries of the junctions. For performance evaluation, the radius of all the mix zones is set to 10 m, similar to the attacking strategy proposed in [24].  
In designing traffic rules, we considered the worst-case scenario, where the vehicle's dwell time within the mix zone is minimal (with no red light delays or traffic congestion). This approach ensures that attackers receive more closely linked segments of vehicle trajectories. As we have demonstrated in [24] and also seen in [56], traffic simulation experiments spanning multiple mix zones indicate that prolonged stays within the mix zone are highly disadvantageous for attackers, significantly reducing their success rate in tracking trajectories.  
For each junction in the simulation, we randomly select two ends. One end is designated as the vehicle entrance, while the other end is designated as the vehicle exit. The arrival interval of vehicles entering the mix zone \(\lambda\) is used to describe the vehicle density in the simulation. We set \(\lambda\) to 2, 4, 6, 8, and 10, representing a new vehicle entering the mix zone every 2, 4, 6, 8, and 10 seconds, respectively. For example, when \(\lambda\) is set to 2, multiple vehicles will appear in the junction simultaneously, with some having just arrived and others about to leave. In contrast, when \(\lambda\) is set to 10, vehicles will rarely encounter other vehicles during their journey inside the junction. The vehicles' speed ranges from 2 to 8. The minimum speed of 2 m/s ensures vehicles keep moving, reducing congestion risks, while the maximum of 8 m/s enhances safety, allowing drivers enough time to react to sudden changes. These limits are consistent with typical urban traffic rules, making the experimental conditions realistic. Their speed gradually decreases as they approach the mix zone, remains constant within the mix zone, and slightly increases at the exit of the mix zone to simulate real-world traffic conditions. All other parameters for the traffic and VANET simulations are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{RNN} & Learning rate & 0.001 \\  & Number of epochs & 50 \\  & Number of training vehicles & 5000 \\  & Number of inference vehicles & 250 \\ \hline \multirow{4}{*}{Traffic} & Maximum speed & 8 m/s \\  & Minimum speed & 2 m/s \\ \cline{1-1}  & Maximum acceleration & 0.8 m/s\({}^{2}\) \\ \cline{1-1}  & Maximum deceleration & 0.2 m/s\({}^{2}\) \\ \hline \multirow{2}{*}{VANET} & Mix-zone radius & 10 m \\ \cline{1-1}  & Beacon interval & 0.3 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.