Pedestrian mode identification, classification and characterization by tracking mobile data

In recent years, with the emergence of personal mobility (PM) and the importance of eco-friendly modes, the role of pedestrian has increased. However, studies on pedestrian, especially methods for determining pedestrian volume, are very limited. Therefore, in this research, we study algorithms for detecting pedestrians based on mobile data and GPS base station information, which depends on the actual user location. To identify the travel modes, including pedestrian group, the key variables are travel speed, travel time, travel distance, and departure time. In addition, the key variable for categorizing pedestrian group into main and access modes is whether or not to go dwell location (destination) and to use transportation vehicles. The results of pedestrian as main mode and access mode are based on a spatio-temporal distribution, and the ratios of the two pedestrian mode types are compared and verified using household traffic survey data.