Visualization Study Based on Cab Trajectory Data

Abstract

With the acceleration of urbanization and the popularization of mobile technology, cabs have become an important part of urban transportation. Cab trajectory data, which is an important source of urban transportation information, contains rich information on driving paths, stopping time, number of passengers and so on. By mining and analyzing these trajectory data, it can reveal the spatial and temporal distribution law of urban traffic and provide powerful support for traffic management, urban planning, business analysis and other fields. Visualization, as an intuitive and effective way to convey information, plays a crucial role in the analysis and application of cab track data. The purpose of this paper is to use data preprocessing, machine learning, and data visualization techniques to study the cab trajectory data, to explore the value of this kind of data at a deeper level, and ultimately to provide strong support for urban transportation and business decision-making fields.