# RoPT: Route-Planning Model with Transformer
## 5 Experiments
### Performance Comparisons  
In Table 3, the performances of our model RoPT on two datasets are shown, compared to other baselines. Overall, RoPT achieves optimal performance on several metrics. And Neuro performed second best. This can be attributed to Porto's lower average node degree (2.39), which simplifies target identification within the top two recommendations. Similarly, all the metrics in the Chengdu dataset are lower than those in the Porto dataset because the number of neighbors per node in the Chengdu dataset is 4.34, much bigger than that in Porto, which increases the difficulty of recommendation.  
The reason why RoPT performs better stems from the fact that it captures the temporal correlations of nodes in trajectories with the Transformer, and captures the spatial correlations of nodes in the road network with the GCN. In contrast, in Neuro, only the average speed of edges in the different periods is focused on, and the order of nodes in trajectories is not fully considered. Although CSSRNN incorporates node sequence information and road  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Porto**} & \multicolumn{4}{c}{**Chengdu**} \\ \hline  & **Acc** & **Recall** & **F1** & **ACC@1** & **Acc** & **Recall** & **F1** & **ACC@1** \\ \hline Dijkstra & 63.42\% & 50.20\% & 56.04\% & 63.42\% & 55.73\% & 41.91\% & 47.84\% & 55.73\% \\ A* & 64.82\% & 54.37\% & 59.14\% & 64.82\% & 49.04\% & 44.30\% & 46.55\% & 49.04\% \\ Heuristic-A* & 66.55\% & 55.89\% & 60.76\% & 66.55\% & 52.74\% & 46.83\% & 49.61\% & 52.74\% \\ CSSRNN & 65.23\% & 68.84\% & 66.98\% & 92.70\% & 56.12\% & 54.33\% & 55.21\% & 80.56\% \\ Graphormer & 15.67\% & 40.12\% & 22.54\% & 75.40\% & 23.46\% & 42.56\% & 30.25\% & 68.43\% \\ GetNext & 7.73\% & 27.53\% & 12.07\% & 66.37\% & 11.07\% & 33.22\% & 16.60\% & 59.05\% \\ GetNext-N & 66.02\% & 68.92\% & 67.43\% & 92.83\% & 56.08\% & 55.65\% & 55.86\% & 80.81\% \\ Neuro & 74.13\% & 71.24\% & 72.65\% & 94.83\% & 61.85\% & 60.77\% & 61.30\% & 84.41\% \\ RoPT (Ours) & **75.62\%** & **71.30\%** & **73.40\%** & **95.36\%** & **62.85\%** & **61.42\%** & **62.00\%** & **85.61\%** \\ Improvement & **+1.49\%** & **+0.06\%** & **+0.75\%** & **+0.53\%** & **+1.00\%** & **+0.65\%** & **+0.7\%** & **+1.20\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall performance comparisons (the values in bold represent the best, the values with underline represent the second best, and the line of “improvement” compares RoPT with the second best).  
network constraints, it fails to conduct an in-depth analysis of spatial correlations among road network nodes. Even in Graphormer, where the Transformer is used, the spatial correlations of nodes are modeled statically with the shortest path distance, which degrades its performance. GetNext similarly combines the GCN and Transformer; yet, its full-node prediction strategy introduces unnecessary complexity for node recommendation tasks. To address this, in GetNext-N, a variant of GetNext, only neighbor nodes are considered when recommending the next node. It can be found that all metrics of GetNext-N are better than those of GetNext, which indicates the necessity of limiting the ranges of candidates.