# RoPT: Route-Planning Model with Transformer
## 6 Conclusions and Future Work  
In this paper, a route-planning model, named RoPT, is proposed for use in an urban transportation system. The use of RoPT can capture drivers' experience in order to recommend routes that are less time-consuming to users with navigation apps, helping administrative traffic management discover abnormal road sections, and so forth. In RoPT, the GCN and Transformer are combined in order to handle complex spatio-temporal correlations in traffic, where the spatial representations of nodes in the road network are captured with stacking multi-layer GCNs and the temporal representations of nodes in trajectories are captured with the Transformer. Furthermore, considering that the destination has a great influence on route planning, in RoPT, features of the destination, captured from the graph corresponding to the road network, are combined with latent representations of the current trajectory to predict the next road section more accurately. The experimental results from the Porto and Chengdu datasets demonstrate that RoPT achieves consistent improvements  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average Inference Time (ms)** & **Standard Deviation (ms)** \\ \hline A* & 0.278 & 0.174 \\ Heuristic A* & 11.692 & 6.964 \\ CSSRNN & 242.951 & 22.409 \\ Neuro & 298.215 & 27.682 \\ GetNext & 308.423 & 29.455 \\ RoPT (ours) & 297.631 & 23.630 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reasoning efficiency (average inference time and standard deviation for each route planning in Chengdu dataset—the values in bold represent the best and the values with underline represent the second best).  
over baselines, with relative gains of 1.49% (Accuracy), 1.00% (Recall), and comparable F1/Acc@1 scores. And latent features learned with RoPT are more interpretable.  
Our inference time experiments demonstrate that deep-learning-based methods exhibit a significantly bigger computational cost compared to A* and its variants. In the future, we will explore methods of route planning which combine the advantages of these two types, according to the dynamics of traffic environments.  
Conceptualization: [PERSON]; methodology: [PERSON] and [PERSON]; software: [PERSON] and Y.T.; validation: [PERSON], Z.X. and Y.T.; formal analysis: [PERSON] and [PERSON]; investigation: [PERSON] and Y.W.; resources: [PERSON] and Z.X.; data curation: [PERSON], [PERSON] and Y.T.; writing--original draft preparation: [PERSON], [PERSON] and [PERSON]; writing--review and editing: [PERSON], [PERSON] and L.L.; visualization: [PERSON], [PERSON] and Y.T.; supervision: [PERSON], [PERSON] and [PERSON]; project administration: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study followed the relevant laws and regulations of the country, especially those related to human experimentation, or data privacy. The authors have described the security measures in the process of collecting, storing, processing, and analyzing research data to ensure that the data are not accessed or abused by unauthorized personnel.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The Chengdu dataset comes from DiDi Chuxing.  
All the authors disclosed no relevant relationships.