Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1407-1412, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1407-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1407-1412, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1407-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Sep 2017

14 Sep 2017

A WEB-BASED PLATFORM FOR VISUALIZING SPATIOTEMPORAL DYNAMICS OF BIG TAXI DATA

H. Xiong1, L. Chen1, and Z. Gui1,2 H. Xiong et al.
  • 1School of Remote Sensing Information and Engineering, Wuhan University, 129 Luoyu Rd., 430079 Wuhan, China
  • 2Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China

Keywords: Taxi trajectory, GPS, Visualization, Hotspots, Colour, Shape

Abstract. With more and more vehicles equipped with Global Positioning System (GPS), access to large-scale taxi trajectory data has become increasingly easy. Taxis are valuable sensors and information associated with taxi trajectory can provide unprecedented insight into many aspects of city life. But analysing these data presents many challenges. Visualization of taxi data is an efficient way to represent its distributions and structures and reveal hidden patterns in the data. However, Most of the existing visualization systems have some shortcomings. On the one hand, the passenger loading status and speed information cannot be expressed. On the other hand, mono-visualization form limits the information presentation. In view of these problems, this paper designs and implements a visualization system in which we use colour and shape to indicate passenger loading status and speed information and integrate various forms of taxi visualization. The main work as follows: 1. Pre-processing and storing the taxi data into MongoDB database. 2. Visualization of hotspots for taxi pickup points. Through DBSCAN clustering algorithm, we cluster the extracted taxi passenger’s pickup locations to produce passenger hotspots. 3. Visualizing the dynamic of taxi moving trajectory using interactive animation. We use a thinning algorithm to reduce the amount of data and design a preloading strategyto load the data smoothly. Colour and shape are used to visualize the taxi trajectory data.