## 4 Results and Discussion  
In this section, we conduct microscopic traffic simulation experiments to verify the feasibility of the IBA setting method base on lane multiplexing. As a powerful tool for studying traffic flow in a microenvironment [38], cellular automaton (CA) is adopted to build microscopic traffic models to help us evaluate the operation effect of lane operation strategies. As shown in Figure 6, a two-lane traffic simulation scene is constructed with CA. Lane 1 is a general lane and lane 2 is a bus lane. In our modeling, there are two types of vehicles on the road: cars and buses. When adopting open boundary rules, vehicles are created at the left boundary of lanes and exits from the right boundary. Specifically, lane 1 creates cars, where the input car flow can be controlled by adjusting the entry probability of cars; lane 2 creates buses at each bus departure interval, and similarly, the bus volume can be controlled by setting the bus departure interval. The signal phase of the downstream intersection can be controlled by adjusting the exit probability of the right boundary (e.g., the exit probability of vehicles is 1 in the green phase of the intersection signal and 0 in the non-green phase). The vehicle movement rule follows the NaSch model [39] and the vehicle lane-changing rule follows the STCA model [40]. The key parameters in the CA traffic model are summarized in Table 2. The details of the CA traffic modeling process are described in [20], which is no longer detailed in this paper. \begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Cell length & 3.75 m \\ Lane length & 300 cells \\ Car dimensions & 2 cells \\ Maximum velocity of cars \(V_{\mathrm{max}}^{\mathrm{car}}\) & 5 cell/s (67.5 km/h) \\ Bus dimensions & 4 cells \\ Maximum velocity of buses \(V_{\mathrm{max}}^{\mathrm{bus}}\) & 3 cell/s (40.5 km/h) \\ Randomization probability in the NaSch model & 0.35 \\ one time step & 1 s \\ \hline \end{tabular}
\end{table} 
Table 2: Key parameters in the CA traffic model. Figure 6: Two-lane traffic simulation scene. The basic variables are defined as follows:  
\[\rho_{i}(t)=\frac{2*N_{i,\text{car}}(t)}{L}+\frac{4*N_{i,\text{bus}}(t)}{L} \tag{23}\]  
\[v_{k}(i,t)=\frac{\sum\limits_{j=1}^{N_{i,k}(t)}v_{i,j(j\in k)}(t)}{N_{i,k}(t)} \tag{24}\]  
where \(\rho_{i}(t)\) represents the traffic density in lane \(i\) at time \(t\) (pcu/2 site); \(v_{k}(i,t)\) refers to the average velocity of type \(k\) vehicles in lane \(i\) at time \(t\), \(i\in(1,2)\), \(k\in(\text{car},\text{bus})\); \(L\) is the lane length (cells); \(N_{i,k}(t)\) is the number of type \(k\) vehicles in lane \(i\) at time \(t\); and \(v_{i,j(j\in k)}(t)\) refers to the velocity of vehicle \(j\) (type \(k\)) in lane \(i\) at time \(t\). \(j\) is the label of the vehicle of type \(k\) in lane \(i\) at time \(t\), \(j=1,2\ldots N_{i,k}(t)\). In our simulations, the proposed IBA lane operation strategy is applied to the CA traffic modeling, and three cases of CA two-lane traffic model under different lane operation strategies are designed for comparison experiments, as follows:  
Case A: conventional two-lane road section with no bus priority (i.e., road section with mixed traffic flow of buses and cars). In this road section, cars are free to change lanes except in the area of the intersection approach. Case B: BLTDM road section with conventional intersection approach setting. Case C: BLTDM road section with IBA setting. The lane operation strategies are transformed into the evolution rules in CA to construct the two-lane traffic models of the above three cases. The experimental parameters of the simulation environment are presented in Table 3. The number of the evolutionary time steps in simulations is set to 10,000 steps, and each time step corresponds to a physical time of 1 s. Since the transient phenomenon occurs in the initial stage of the simulation, the first 8000-time steps are discarded to reduce the negative effect of the transient time.