Proposal of a classification method for public space usage based on pedestrian trajectory data

Abstract

Abstract Herein, we propose a machine learning method based on pedestrian trajectory data to classify public space usage states and discriminate unknown usage states. Assuming that there are several frequent patterns in the usage state of public spaces, each element of the data set representing the usage state of public spaces can be classified into several clusters. Each cluster is defined as a “type” of usage state. They were classified into usage state “types” via principal component analysis and x‐means clustering. We employed data from the detection and recording of pedestrian trajectories by six 3D laser (LiDAR) sensors, conducted by the authors during the summer of 2019 in a public space in Yokohama, Japan. In the training phase, we defined “types” of usage states based on the data obtained for 3 weeks. In the test phase, the “type” of usage state was determined for the data of other periods. Consequently, 16 types that appeared at specific times and days were identified, and 1.1% of the test data were determined to be “new usage states,” which were not found in the training data. This method helps understand long‐term and complex variations in public space utilization patterns.

Keywords

• clustering
• human flow
• machine learning
• pedestrian trajectory data
• public space usage