# A Path Recommendation Method Considering Individual Driving Preferences
## 5 Case Studies
### Personalized Path Recommendation Results and Analysis
#### 5.3.2 Differences in Path Satisfaction at Different Departure Moments  
Traffic conditions are subject to time-dependent changes, and the dynamic attributes of a path are closely associated with these conditions. Consequently, the perception of path satisfaction is influenced by these variations, leading to changes in the recommended paths over time. Taking driver a as an example, we recommended different paths at various departure times throughout the day, assessing their respective satisfaction levels. Additionally, we included the satisfaction of the shortest path at different departure times for comparison purposes. The results of the path recommendations are presented in Figure 11.  
The figure illustrates that, for a given path, the fluctuation in path satisfaction primarily arose from changes in its dynamic attributes, including travel time, average path density, and maximum path density, while the static attributes of the shortest path remained \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Path 4 (Brown)**} & \multicolumn{2}{c}{**Path 5 (Yellow)**} & \multicolumn{2}{c}{**Path 6 (Blue)**} \\ \cline{2-7}  & **Actual Value** & \begin{tabular}{c} **Importance** \\ **Rating** \\ \end{tabular} & **Actual Value** & \
\begin{tabular}{c} **Importance** \\ **Rating** \\ \end{tabular} & **Actual Value** &
\begin{tabular}{c} **Importance** \\ **Rating** \\ \end{tabular} \\ \hline \(B21\) path travel time & 999.07 & 20.58 & 722.58 & 57.70 & 962.47 & 23.75 \\ \(B22\) path length & 4810.21 & 29.74 & 4067.39 & 97.81 & 4666.00 & 42.32 \\ \(B23\) number of path lanes & 2.72 & 96.03 & 1.67 & 14.87 & 2.35 & 81.90 \\ \(B24\) path steering & 2.29 & 96.80 & 3.20 & 58.79 & 2.79 & 83.98 \\ \(B25\) number of path & 1.87 & 97.82 & 1.97 & 92.70 & 2.14 & 64.03 \\ \(B26\) path average density & 14.81 & 22.56 & 15.08 & 21.22 & 9.82 & 62.99 \\ \(B27\) path maximum density & 21.39 & 57.29 & 17.71 & 71.88 & 17.71 & 71.88 \\ \(B28\) historical path similarity & 61.53 & 6.86 & 21.15 & 13.04 & 18.28 & 0.00 \\ \hline Satisfaction & & d & b & c & a & \\ \cline{2-7}  & & 53.00 & 51.33 & 58.64 & & 56.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Calculation results for index values related to paths 4–6. Figure 11: Satisfaction associated with different paths at different departure times. unchanged. Tables 5-24 further show how path satisfaction varied throughout the day in response to changes in urban traffic conditions. Two troughs can be observed during the morning and evening peak hours, gradually reaching a local maximum during off-peak hours and ultimately peaking in the early morning hours. Comparing the satisfaction of paths when considering individual preferences with that when considering only path length, it became evident that satisfaction considering individual preferences consistently surpassed those based solely on length. Furthermore, the extent of the maximum satisfaction relative to the shortest path satisfaction was also influenced by the traffic conditions of the road network. Typically, when the traffic flow density reached its lowest point, the magnitude of the maximum satisfaction over the shortest path satisfaction also reached its peak. This indicated that residents who prioritize good traffic conditions and low path difficulty (such as driver a) may not necessarily have a favorable driving experience during idle and low vehicle density hours when traffic conditions are optimal. In such cases, a more effective approach would be to schedule trips during less congested hours, in order to avoid high traffic density periods. However, it should be noted that these findings were specific to drivers like driver a. Other driver types, such as driver b (see Figures 6-10), could still achieve higher levels of satisfaction during peak hours through personalized path recommendations.