# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 4 Experiments
### Results and Evaluation  
The Nanshan District road network and bus trajectory points matched to the corresponding road segments were used as input data. The algorithm requires 10 min to complete 1000 iterations. Figure 3 depicts the evolution of multiobjective scores in 1000 iterations. The two objective function scores rapidly increase at early iterations. After approximately 500 iterations, both objective function score curves exhibit a plateau, indicating that the results have converged. Each point on the Pareto front in Figure 4 represents a nondominated bus lane proposal. Figure 5 shows three planning schemes with different objective preferences. Plan A tended to have the best bus punctuality rate. Additionally, Figure 4 illustrates that the road utilization rate of Plan A is the worst. Plan C tended to optimize the utilization of bus lanes the most, and the corresponding punctuality rate score was the lowest. Plan B is a balance between punctuality and utilization. The following points can be observed from Figure 5. (1) In the best case of the punctuality rate, there are many small branches in the planning result of Plan A. In the multiobjective balanced and optimal utilization cases, there are several long road segment sets in the planned bus lane scheme because the objective function of bus utilization incorporates the continuity of bus routes. (2) Overlaying the results with subway lines and sites reveals that the model results are highly connected to the subway, which is consistent with the complementarity of subways and buses in urban transportation systems. To illustrate the usability of the planning results, the results from the model were compared with the results of actual bus lane planning in Shenzhen, which is driven by historical experience and questionnaires organized by the bus management department. The balanced planning scheme created based on the proposed method was selected for comparison with the actual bus lane planning scheme that is implemented by the Transportation Bureau of Shenzhen Municipality. Figure 6 shows that the balanced planning scheme is largely consistent with the actual planning scheme. However, there are three road segments that are different from the case in the actual planning scheme. We used street-view images (which were collected in 2017 after the actual planning scheme wasimplemented) to investigate these differences. It can be observed from the street view that road segment (a) and road segment (c) have been planned as bus lanes, and road segment (b) is a two-lane road. In the proposed model, only roads with more than two lanes can be candidates. In practice, as in the test area, there is also a high demand for bus lanes on some two-lane roads, even though the planning of bus lanes on two-lane roads uses more traffic resources. Thus, the lane number threshold for bus lane planning needs further examination. Figure 4: Final Pareto front solution set for bus lane planning. Figure 3: Two objective function score curves for each generation. We also quantitatively compared the results of the two schemes. We calculated the length of the actual bus lane in Nanshan District, Shenzhen City, and used it as the construction budget constraint for bus lane planning. The statistics of the two objective functions for the actual planning scheme and the created planning scheme are shown in Fig. 5.  
Figure 5: Bus lane planning results for Nanshan District achieved using the proposed method: (**a**) Plan A tends to have the best bus punctuality rate; (**b**) Plan B is a balance between punctuality and utilization; (**c**) Plan C tends to best optimize the utilization of bus lanes. Figure 6: Comparison between the plans developed based on the proposed method and the actual planning scheme. (**a–c**) are the street views of three road segments that is in the red circle. in Figure 7. Both the utilization and punctuality scores suggest that the actual planning scheme is clearly worse than the scheme developed with the proposed method.