Trip Destination Prediction by Cross-City Exploratory Data Analysis Approach in People Flow Data

Understanding human mobility is mostly based on destination prediction. The reality that training and test data frequently differ makes it challenging to broaden the application of destination prediction, and when a prediction model that was trained on certain areas is then applied to the area of interest but with a different data distribution, the accuracy is suboptimal at best. The objective of the IEEE Big Data Cup 2022 is to solicit a robust and generalizable model that can effectively forecast a person’s daily destination based on facts and qualities obtained from four Japanese urban areas, as well as to predict the destination for a new metropolitan area using the models that were trained with the prior data. To address the issue and face the challenge of this Cup, our KDDI Research team has developed a prediction method based on Exploratory Data Analysis (EDA) that does not employ geographical zone information that varies from area to area. Instead, we employ the mobility characteristics of human groups, where each group is categorized according to demographics, which according to our EDA are relatively universal across areas. Our experimental findings show that our method achieves a relatively good prediction accuracy, which is attested by this Cup’s leaderboard. Albeit our method is simple and straightforward, we can argue that our approach can be used as good baseline for human mobility destination prediction.