Abstract
In Japan, traffic congestions often occur on the expressways connecting tourism areas with the Tokyo metropolitan area. This congestion can be mitigated if tourists delayed their departure of homeward trips to avoid peak traffic hours. A potential method to promote staggered departure times is providing the estimates of near-future traffic congestion. This study hypothesized and experimentally confirmed that some tourists would delay their departure to avoid traffic based on near-future traffic estimates. The experiment was conducted in the Yatsugatake area using a mobile application that provided this information to tourists. The results suggest that approximately 40% of self-driving tourists will perform an additional stopover if the returning route is congested and near-future traffic congestion estimate is provided.
Highlights
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 negatively impacts tourism as travelers return early to avoid traffic congestion
Summary
One of the biggest challenges in the transportation sector is addressing traffic congestion on expressways. 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. The aim of this study was to provide evidence to demonstrate the effectiveness of information intervention to travel and tourism behaviors, while providing empirical evidence that actual stopover behavior can be inferred from smartphone application operation logs This finding eliminates the need for GPS trajectory data to determine the effectiveness of such interventions and eases the process of conducting such experiments from the viewpoint of personal information protection. It presents methods for field experimentation to demonstrate that actual behavior can be inferred using an application operation log.
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