## 1 Introduction  
One of the biggest challenges in the transportation sector is addressing traffic congestion on expressways. In Japan, traffic congestion is often caused by traffic moving from surrounding tourism spots toward the Tokyo metropolitan area; the number of vehicles tend to increase on Sundays and the last day of consecutive holidays. This results in tourists losing time and also negatively impacts tourism as travelers return early to avoid traffic congestion. A potential solution to reduce traffic congestion is to promote delayed departure for tourists. While the construction of new roads can potentially ease congestion, it is time-consuming and expensive. However, promoting delayed departures is a behavior change approach, which is inexpensive, effective, and feasible. [PERSON] [1] showed that delaying the departure time of vehicles could reduce traffic congestion. A survey of Japanese tourists showed that over 60% of respondents would change their departure time based on traffic congestion information [2]. Additionally, delaying departure from tourist spots could potentially increase consumer activity, which may not have been initially planned, such as shopping or eating at restaurants. In this study, such unplanned activities are referred to as \"additional stopovers\". In recent years, the increased use of smartphones and mobile applications has made it easier to provide real-time information to consumers. Such applications have been used to promote socially desirable behavior in various fields [3, 4], including the transportation tourism sectors (e.g., [5, 6]). If tourists are provided with traffic congestion information tailored to their homeward journey via mobile applications, they may delay their departure to avoid traffic. Prior studies on this subject examined the Global Positioning System (GPS) trajectory data (mobile probe data) of tourists in the Yamanashi prefecture of Japan and demonstrated that self-driving tourists tend to make additional stopovers to avoid congestion [7]. These studies provided indirect evidence showing that the real-time traffic congestion information can increase stopovers. However, these studies also reported results of situations where no estimated near-future traffic information was provided. Moreover, the reasons for additional stopovers could not be explained using the existing datasets. Thus, an experimental approach is necessary to verify the effect of providing near-future traffic estimates on reducing congestion and promoting tourism. This study aimed to provide empirical evidence to demonstrate the effectiveness of providing real-time traffic congestion estimates to increase tourism-related additional stopovers. For this, the authors tested and confirmed the following hypothesis through a field experiment. **Hypothesis 1**. : _Self-driving tourists will make additional stopovers to delay their departure and avoid traffic if near-future traffic congestion estimation is provided._  
A mobile application was developed to provide information on real-time traffic congestion estimation and nearby tourism spots, for self-driving tourists in Yatsugatake, Japan, to understand its impact on their stopover behavior. Existing traffic congestion data indicate that congestion is at a peak between 16:00 and 17:00 approximately, and it gradually reduces after that. Therefore, delaying departure times during peak hours would potentially reduce the travel time for returning home. The mobile application offers options such as \"depart now\" and \"depart later\", along with the estimated travel time required to reach the intended destination. The user's operation logs their travel trajectory, and post-experiment questionnaire results were obtained via the application. The aim of this study was to provide evidence to demonstrate the effectiveness of information intervention to travel and tourism behaviors, while also providing empirical evidence that actual stopover behavior can be inferred from smartphone application operation logs.