## 4 Results and Discussion  
### Participants  
Seventy-two participants agreed to participate in the experiment and installed the application on their mobile phones. Sixty participants searched for routes to their intended destination using the application and are designated as active participants in this paper. Table 1 provides an overview of the participant attributes, which were obtained via a questionnaire within the application.  
Figure 3: Overview of the application architecture using NAVITIME Application Programming Interfaces (APIs).  
Figure 2: Screenshot of the English version of the app: (**a**) Route choice; (**b**) Spot choice; (**c**) Route check.  
The experiment included more male participants than female. This may be because there are more male drivers than female drivers in Japan. According to a Japanese survey on transportation means during holidays, self-driving was selected by 48.9% of males and only 24.2% of females [19]. More than 75% of all participants were between the age of 30 and 59 years. Most participants traveled with their families, and the presence of small children, in particular, could potentially influence stopover behavior during late hours on Sundays or the last day of consecutive holidays. Therefore, the presence or absence of children under elementary school age was also obtained as an individual attribute. The most common purposes of travel were sightseeing, homecoming, and visiting friends, i.e., activities that have minor time constraints.  
Before the experiment started, the participants' current practice of considering traffic congestion to adjust the departure time and their intention to change the departure time accordingly were examined using a questionnaire within the application. Figures 4 and 5 show the results of this questionnaire.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Attributes** & **Number** & **Percentage** \\ \hline \multirow{2}{*}{**Gender**} & Male & 53 & 73.6\% \\  & Female & 19 & 26.4\% \\ \hline \multirow{4}{*}{**Age**} & 20s & 8 & 11.1\% \\  & 30s & 17 & 23.6\% \\  & 40s & 19 & 26.4\% \\  & 50s & 19 & 26.4\% \\  & 60s & 8 & 11.1\% \\  & 70s or more & 1 & 1.4\% \\ \hline \multirow{4}{*}{**Companion**} & Traveling alone & 7 & 9.7\% \\  & Family with children & 30 & 41.7\% \\ \cline{1-1}  & Family without children & 27 & 37.5\% \\ \cline{1-1}  & Friends & 7 & 9.7\% \\ \cline{1-1}  & Others & 1 & 1.4\% \\ \hline \multirow{4}{*}{**Purpose of travel**} & Sightseeing & 56 & 77.8\% \\ \cline{1-1}  & Homecoming/visiting & 11 & 15.3\% \\ \cline{1-1}  & friends & 0 & 0.0\% \\ \cline{1-1}  & Business & 5 & 6.9\% \\ \hline \end{tabular}
\end{table}
Table 1: Participant attributes overview.  
Figure 4: Current practice of considering traffic congestion to adjust departure time for returning home.  
Figure 4 shows the answers to questions such as \"Do you adjust your departure time considering traffic congestion?\" The aggregate of the responses \"Sometimes\", \"I do\", and \"Always\" were 90% of the total results. This suggests that participants consider traffic to a large extent. No difference in gender was observed in these responses.  
If the participants were accompanied by a family with children in elementary school, their consideration of delaying departure time and their flexibility in changing the time was slightly lower.  
Figure 5 shows the answers to the question: \"Is it possible to delay the departure time for returning home after sightseeing?\" Positive responses accounted for 88% of the total responses, and information regarding traffic congestion was likely to change their behavior. The proportion of positive answers was lower for female participants and participants who had children in elementary school: approximately 70%.