Visualization Of Taxi GPS Trajectory Data

With the rise of smart cities,the scale of traffic data has grown rapidly,and the traditional traffic data analysis methods have difficult to apply.This has brought new opportunities and challenges to solving urban problems and building smart cities.With the rise of big data visualization technology,it uses the internal rules of data representation in graphics to realize the hierarchical display of data in the form of interaction,and plays an increasingly important role in analyzing traffic data,discovering traffic problems,and assisting in decision-making.As an important part of urban traffic,taxis have important indications for reflecting the residents' travel activities.Vehicle GPS trajectory data records the state of all aspects of the city,contains a large number of residents travel information,can be applied to urban planning,intelligent transportation and other fields.This paper focuses on the research of taxi GPS trajectory data,and proposes a method for visual analysis of taxi GPS trajectory data by integrating clustering visualization and feature visualization.The basic idea is to use the innate cognitive advantages of humans for visual images such as images,to process data through a series of automatic analysis algorithms and visual algorithms,and using a combination of multi-view collaboration and a map visualization interface to help users analyze the urban traffic conditions from both macroscopic and microscopic perspectives,explore the temporal and spatial variation of taxi rider characteristics,At the same time,a visual prototype system is developed to support visual analysis.The research content of this article mainly includes the following aspects:(1)Trajectory data compression and visual analysis of taxi track.A trajectory compression algorithm that takes into account the user's needs and the complexity of the trajectory structure is proposed.The experimental results show that the algorithm can effectively preserve the vehicle's driving characteristics,and can effectively reduce the error while ensuring the compression rate.Finally,visualization technology can be used to visually analyze the trajectory of road vehicles and to find out the urban road traffic conditions.(2)The hot spots extraction and visual analysis.According to the taxi GPS trajectory data,get up and down passenger points,and design an improved density clustering algorithm(KDE+G-DBSCAN)cluster analysis of upper and lower passenger points,generate up/down Guest hot spots.Finally,a clustering icon was used to visualize the distribution of passengers at different times.(3)Taxi passenger travel feature extraction and visual analysis.we firstly extract the travel characteristics of the passengers from the trajectory data through the data mining algorithm,such as the number of vehicles on the road,the travel demand,the real loading rate and so on.Then use multi-view visualization technology to visualize it and design corresponding visual interaction events.(4)Implementing a visualization prototype system.Building a visual analysis system based on D3.js.Using multi-view visualization technology,the taxi GPS trajectory data can be visualized.Based on the visualization results,A visual analysis of the traffic conditions in Xi'an was performed.The visualization method of this paper uses a variety of automatic algorithms to deal with cumbersome and complex tasks.At the same time,in the process of visual analysis,the principle of high-level task handling is emphasized.The final visual interface clearly restores the traffic conditions of the road.