## 5 Conclusions  
In this study, a mobile application to simultaneously provide real-time traffic congestion estimation along with nearby tourism spots was developed. The experiment results showed that if delaying the departure significantly shortened the required travel time for returning home, most users tended to delay their departure. If the estimated travel time could be shortened by 20 min or more, the selection ratio of delaying the departure time was higher than that of departing immediately, and if it was shortened by 30 min or more, 100% of the participants delayed their departure. Thus, it is expected that approximately 40% of people will perform an additional stopover if the returning route is congested and a near-future traffic congestion estimate is provided. Therefore, the hypothesis that tourists would make additional stopovers to delay their departure is partially supported by the experimental results. The experimental results show that providing information on estimated near-future traffic congestion and nearby tourism spots promoted stopover behavior. A major contribution of this study is that it empirically confirmed that information interventions are effective in promoting unplanned stopovers when avoiding traffic congestion, especially in the case of tourism-related travel. Moreover, the experimental results revealed that chosen options detected by operation logs showed a high consistency with actual stopover behaviors confirmed by questionnaires and travel trajectory, suggesting the possibility of inferring actual stopover behaviors from smartphone application operation logs. This finding eliminates the need for GPS trajectory data to determine the effectiveness of interventions and eases the process of conducting extensive large-scale experiments in future studies from the viewpoint of personal information protection. It should be noted that the participants of this experiment had an intention to avoid congestion. It is necessary to reconsider the methodology of the recruitment of participants for future experiments. In addition, the number of active participants was 60, and the experimental period was one month. The number of active participants may seem to be small. The recruitment of participants for this experiment was difficult because it requires personal information such as GPS trajectory data. People hesitated to agree to participate, even if it was clearly stated that the data will be processed such that no individual \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Participant** & **Probe Data** & **Shortened Travel** & **Chosen Option in the** & & **Consistency of Chosen** \\
**Recorded** & **Time Main** & **Application’s Operation Logs** & **Actional Behavior** & **Option and Actual Behavior** \\ \hline
**\#1** & Yes & 2.5 & Dparant now & No stopover & Yes \\
**\#2** & Yes & 22.1 & Dparant later & Additional stopover & Yes \\
**\#3** & No data & 3.3 & Dparant later & Additional stopover & Yes \\
**\#4** & Yes & 1.3 & Dparant now & No stopover & Yes \\
**\#5** & Yes & 2.2 & Dparant now & No stopover & Yes \\
**\#6** & Yes & 26.9 & Dparant later & Additional stopover & Yes \\
**\#7** & Yes & 10.4 & Dparant now & No stopover & Yes \\
**\#8** & Yes & 4.6 & Dparant later & Additional stopover & Yes \\
**\#9** & Yes & 1.5 & Dparant later & No stopover & No \\
**\#10** & No data & 4.1 & Dparant now & Additional stopover & No \\
**\#11** & No data & 6.2 & Dparant now & No stopover & Yes \\
**\#12** & Yes & 16.7 & Dparant now & No stopover & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of operation logs and actual behavior of participants who answered the post-experiment questionnaire. Figure 7: Timing of searches for returning routes using the app. can be identified. It is expected that the effectiveness of the developed application would enable the increase in the number of users of the application for a more extended experimental period. Therefore, a large-scale and long-term control experiment with more participants is planned in the future.