## 6 Conclusions  
The main contribution of this paper is to create a novel optimization methodology for DRTs with personalized travel and the synchronous coordination of rail transit, to reveal the relation between the ride-time for vehicles from the pickup locations to the rail station, and the wait-time for passengers taking the subway beforehand. In contrast to existing studies, the proposed methodology reflects: (1) an integration process for designing the DRT routing and schedule, considering synchronous transfer between the shuttle and feeder bus; and (2) a distributed GA developed to efficiently resolve \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**Improved GA**} & \multicolumn{4}{c}{**Standard GA**} \\ \cline{2-10}  & **Iteration** & **Best Solution** & **Worst Solution** & **Average Solution** & **Iteration Times of Convergence** & **Best Solution** & **Worst Solution** & **Average Solution** & **Difference** \\ \hline \(50\) & 67 & 416.1 & 438.7 & 423.3 & 112 & 416.1 & 449.2 & 438.9 & 3.7\% \\ \(100\) & 87 & 785.2 & 868.2 & 827.6 & 197 & 792.7 & 902.6 & 880.6 & 6.4\% \\ \(200\) & 104 & 1442.7 & 1566.4 & 1506.5 & 345 & 1472.3 & 1728.6 & 1676.7 & 11.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Objective(min)**} & \multicolumn{2}{c}{**Average Calculate Time (min)**} & \multirow{2}{*}{**Total Ride Time (min)**} & \multirow{2}{*}{**Total Wait Time (min)**} & \multirow{2}{*}{**Total Route**} & \multirow{2}{*}{**Total Route**} \\ \cline{2-2} \cline{4-4} \cline{6-9}  & **Cplex** & **Improved** & **GA** & **CDF** & **DA** & **DA** & **DA** & **DA** \\ \hline \(3\) Vehicles & 275.9 & 295.2 & 20.1 & 3.6 & 235.9 & 40 & 13.9 & 55.2 \\ \(4\) Vehicles & 241.2 & 253.3 & 120.2 & 4.2 & 145.2 & 96 & 14.3 & 57.0 \\ \(5\) Vehicles & 230.0 & 246.1 & 1500.3 & 4.6 & 145.7 & 84.3 & 18.1 & 72.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the models and algorithm performance among three scenarios. Figure 7: Three-scenario comparison between the proposed and the DRTNSCRT models. the proposed mode. Results show that the total ride-time of the proposed model is significantly increased by 9.36%, but the total wait-time is significantly reduced by 67.5%, which results in the total travel time of our model being reduced by 2.46% compared to the traditional DRT model. Additionally, the difference in optimal solutions between the proposed GA, standard GA, and the use of Cplex is about 5-7%, but the calculation speed is proved acceptable, which proves the validity of the algorithm. Note that this research is based on key hypotheses of pedestrian boarding places (demand points) being bus stops and the stable OD table. It neglects the integrated assignment of demand points to selected stops, and the dynamic setting with time-varying traffic conditions. Therefore, this model is very useful at the stage of strategic demand-responsive transit service for vehicles visiting demand points directly. However, the model can not deal with demand points located at one way streets or left-turn only intersections in real traffic network. Extending the model to simultaneously select optimal stops in candidates with assignments of pedestrians to these selected stops with time-varying OD is a worthwhile further work. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.