Visualization of spatio-temporal data of bus trips

Bus is public means to travel in several cities. Traditional maps provide with the information of bus routes that passengers may use to design a travel from a place to another. It is difficult for them to obtain an appropriate route because of the lack of the information of time on the traditional maps. Some web sites enable passengers to take bus but they do not support passengers to have more diverse selections. In this paper, buses are considered as moving objects. Mathematically, movements of objects are the mappings (functions) from times to locations. In 3-D Cartesian coordinate systems, space-time cubes, which are called temporal maps in this paper, are models representing movements in spatio-temporal domain. A bus route on a traditional map is the curve connecting the spatial positions where the bus visits, from the departure station to the arrival. A bus trip is a bus route included time. It is represented on temporal maps. With the visualization of spatio-temporal data of bus trips on temporal maps, passengers may mark out bus trips for their more appropriate travels. This article implemented visualization tools for the design of bus travels on temporal maps.

[1]  Harvey J. Miller,et al.  Exploring traffic flow databases using space-time plots and data cubes , 2011, Transportation.

[2]  Heidrun Schumann,et al.  3D information visualization for time dependent data on maps , 2005, Ninth International Conference on Information Visualisation (IV'05).

[3]  Gennady L. Andrienko,et al.  Visual Analytics for Geographic Analysis, Exemplified by Different Types of Movement Data , 2009, IF&GIS.

[4]  Véronique Malaisé,et al.  An integrated approach for visual analysis of a multisource moving objects knowledge base , 2010, Int. J. Geogr. Inf. Sci..

[5]  Shih-Lung Shaw,et al.  Revisiting Hägerstrand’s time-geographic framework for individual activities in the age of instant access , 2007 .

[6]  Gennady L. Andrienko,et al.  Interactive analysis of event data using space-time cube , 2004, Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004..

[7]  Pascal Matsakis,et al.  Relative positions in words: a system that builds descriptions around Allen relations , 2010, Int. J. Geogr. Inf. Sci..

[8]  N. Andrienko,et al.  Basic Concepts of Movement Data , 2008, Mobility, Data Mining and Privacy.

[9]  Anna Monreale,et al.  A Generalisation-based Approach to Anonymising Movement Data , 2010 .

[10]  Gennady L. Andrienko,et al.  Spatial Generalization and Aggregation of Massive Movement Data , 2011, IEEE Transactions on Visualization and Computer Graphics.

[11]  Slava Kisilevich,et al.  A conceptual framework and taxonomy of techniques for analyzing movement , 2011, J. Vis. Lang. Comput..

[12]  D. Peuquet It's About Time: A Conceptual Framework for the Representation of Temporal Dynamics in Geographic Information Systems , 1994 .

[13]  Robert Weibel,et al.  Towards a taxonomy of movement patterns , 2008, Inf. Vis..

[14]  Natalia Adrienko,et al.  Spatial Generalization and Aggregation of Massive Movement Data , 2011 .

[15]  Menno-Jan Kraak,et al.  The space - time cube revisited from a geovisualization perspective , 2003 .

[16]  Phuoc Tran Vinh,et al.  Visualization Cube for Tracking Moving Object , 2022 .

[17]  Heidrun Schumann,et al.  Space, time and visual analytics , 2010, Int. J. Geogr. Inf. Sci..

[18]  Torsten Hägerstraand WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .